Skylos

What is Skylos about?

Skylos provides a unified workflow for dead‑code detection, security scanning (SAST), and AI‑generated code defence across Python, TypeScript, and Go. It integrates with local development, CI/CD pipelines, and AI assistants via an MCP server.

How to use Skylos?

1. Install the CLI from PyPI:

   pip install skylos          # core
   pip install skylos[llm]    # optional LLM features
   pip install skylos[fast]   # optional Rust‑accelerated backend
   

2. Run locally:
   • skylos suite . – full scan (dead code, security, debt, AI defence).
   • skylos . – basic dead‑code, security, and quality analysis.
   • skylos . --diff origin/main – focus on changed lines.
3. Add CI/CD with a single command:

   skylos cicd init
   

   This generates a GitHub Actions workflow that gates PRs, adds annotations, and can upload results.
4. VS Code extension – install from the Marketplace to get in‑editor findings and one‑click AI‑assisted fixes.
5. MCP server – start the server (python -m skylos_mcp.server) and configure AI assistants (e.g., Claude Desktop) to call Skylos tools directly.

Key features of Skylos

• Framework‑aware dead‑code detection for FastAPI, Django, Flask, pytest, Next.js, React, etc., reducing false positives.
• Static security analysis (taint tracking, secret hunting, dangerous‑function detection) with OWASP LLM Top 10 checks.
• AI‑generated code defence – discovers LLM integrations, validates guardrails, and scores defenses.
• Hybrid agent workflow – combines fast static scans with LLM reasoning for verification, remediation, and PR comment generation.
• Multi‑language support – Python, TypeScript/TSX/JSX, Go, and Java via Tree‑sitter parsers.
• GitHub Action & VS Code extension for seamless CI/CD and in‑editor feedback.
• Technical debt hotspot analysis with baseline tracking.
• Customizable thresholds, whitelisting, and rule suppression via pyproject.toml.
• Optional Rust backend (skylos[fast]) for up to 63× speed‑up.

Use cases of Skylos

• Prevent dead code from accumulating in large monorepos, keeping the codebase lean.
• Secure CI pipelines by failing builds on hard‑coded secrets, SQL injection, SSRF, and other high‑severity issues.
• Guard AI‑assisted refactors – catch removed security decorators or missing validation after Copilot/Claude edits.
• Enforce code‑quality standards (complexity, nesting, duplicate literals) before merges.
• Assist AI agents – provide an MCP endpoint so assistants can query for dead code, security gaps, or generate remediation patches.
• Automated remediation via skylos agent remediate to scan, fix, test, and open PRs.

FAQ from the Skylos

Q: Does Skylos work with frameworks like Django or FastAPI? A: Yes. It recognises routes, decorators, and test fixtures, assigning lower confidence to framework‑driven entry points to minimise false positives.

Q: How does the --trace option improve results? A: It runs your test suite (or any command) with tracing, marking every function that actually executes as used, which eliminates false dead‑code reports caused by dynamic dispatch.

Q: Can I run Skylos without internet access? A: Core static analysis works completely offline. LLM‑powered features require a cloud provider or a local OpenAI‑compatible endpoint (e.g., Ollama).

Q: What is the recommended confidence level for safe cleanup? A: Start with the default --confidence 60. For framework‑heavy projects use --confidence 30 or 20 to see low‑confidence entries.

Q: How do I configure the MCP server for Claude Desktop? A: Add the following to ~/.config/claude/claude_desktop_config.json (or the macOS equivalent):

{
  "mcpServers": {
    "skylos": {
      "command": "python",
      "args": ["-m", "skylos_mcp.server"]
    }
  }
}

Afterwards you can ask Claude to "scan my project for security issues" and it will invoke Skylos via the MCP endpoint.

Q: How are secrets detected? A: Skylos matches known provider patterns (AWS, Stripe, GitHub, etc.) and falls back to high‑entropy string detection across source and config files.

Q: Is there a way to whitelist false positives globally? A: Yes. Use skylos whitelist <pattern> or add entries under [tool.skylos.whitelist] in pyproject.toml.

📖 Website · Documentation · Blog · GitHub Action · VS Code Extension · MCP Server

English | 中文

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What is Skylos?

Skylos is an open-source static analysis tool and PR gate for Python, TypeScript, and Go. It helps teams detect dead code, hardcoded secrets, exploitable flows, and AI-generated security regressions before they land in main.

If you use Vulture for dead code, Bandit for security checks, or Semgrep/CodeQL for CI enforcement, Skylos combines those workflows with framework-aware dead code detection and diff-aware regression detection for AI-assisted refactors.

The core use case is straightforward: run it locally, add it to CI, and gate pull requests on real findings with GitHub annotations and review comments. Advanced features like AI defense, remediation agents, VS Code, MCP, and cloud upload are available, but you do not need any of them to get value from Skylos.

Best for

• Python teams that want dead code detection with fewer false positives than Vulture
• Repositories using Cursor, Copilot, Claude Code, or other AI coding assistants
• CI/CD pull request gates with GitHub annotations and review comments
• Python and TypeScript LLM applications that need OWASP LLM Top 10 checks

Available as

• CLI for local scans and CI/CD workflows
• GitHub Action for pull request gating and annotations
• VS Code extension for in-editor findings and AI-assisted fixes
• MCP server for AI agents and coding assistants

Start here

Goal	Command	What you get
Run everything local	skylos suite .	Static findings, technical debt hotspots, Python AI defense, and provenance summary in one report
Scan a repo	skylos . -a	Dead code, risky flows, secrets, and code quality findings
Gate pull requests	skylos cicd init	A GitHub Actions workflow with a quality gate and inline annotations
Audit an LLM app	skylos defend .	Optional AI defense checks for Python and direct TypeScript LLM integrations

If you only remember 4 commands

• skylos suite . for the full local overview
• skylos . for the focused static scan
• skylos cicd init for CI setup
• skylos agent scan . for hybrid static + LLM review

Why teams adopt it

1. Better dead code signal on real frameworks: Skylos understands FastAPI, Django, Flask, pytest, Next.js, React, and more, so dynamic code produces less noise.
2. Diff-aware AI regression detection: Skylos can catch removed auth decorators, CSRF, rate limiting, validation, logging, and other controls that disappear during AI-assisted refactors.
3. One workflow instead of three tools: Dead code, security scanning, and PR gating live in the same CLI and CI flow.
4. Local-first by default: You can keep scans on your machine and add optional AI or cloud features later if you need them.
5. Self-explaining output: Every table prints a legend explaining what each column and number means — no manual required.
6. Monorepo-aware TS resolution and reachability: Skylos uses declared workspaces and importer-local direct tsconfig project references during TypeScript package resolution, and it keeps workspace package entrypoints alive during dead-file and unnecessary-export analysis.
7. AI defense now reaches TS repos too: skylos discover and skylos defend can now pick up direct TypeScript LLM integrations in Node / Next-style codepaths as a beta surface.

Why Skylos over Vulture for Python dead code detection?

	Skylos	Vulture
Recall	98.1% (51/52)	84.6% (44/52)
False Positives	220	644
Framework-aware (FastAPI, Django, pytest)	Yes	No
Security scanning (secrets, SQLi, SSRF)	Yes	No
AI-powered analysis	Yes	No
CI/CD quality gates	Yes	No
TypeScript + Go support	Yes	No

Benchmarked on 9 popular Python repos (350k+ combined stars) + TypeScript (consola). Every finding manually verified. Full case study →

🚀 New to Skylos? Start with CI/CD Integration

# Generate a GitHub Actions workflow in 30 seconds
skylos cicd init

# Commit and push to activate
git add .github/workflows/skylos.yml && git push

What you get:

• Automatic dead code detection on every PR
• Security vulnerability scanning (SQLi, secrets, dangerous patterns)
• Quality gate that fails builds on critical issues
• Inline PR review comments with file:line links
• GitHub Annotations visible in the "Files Changed" tab

No configuration needed - works out of the box with sensible defaults. See CI/CD section for customization.

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Table of Contents

• What is Skylos?
• Quick Start
• Technical Debt Hotspots
• Key Capabilities
• Installation
• Skylos vs Vulture
• Projects Using Skylos
• How It Works
• Advanced Workflows
• CI/CD
• MCP Server
• Baseline Tracking
• Gating
• VS Code Extension
• Integration and Ecosystem
• Auditing and Precision
• Coverage Integration
• Filtering
• Release Automation
• Release Workflow Runbook
• CLI Options
• FAQ
• Limitations and Troubleshooting
• Contributing
• Roadmap
• License
• Contact

Quick Start

If you are evaluating Skylos, start with the core workflow below. The LLM and AI defense commands are optional.

Core Workflow

Objective	Command	Outcome
Everything local	skylos suite .	One report for static findings, technical debt, Python AI defense, and provenance
First scan	skylos .	Dead code findings with confidence scoring
Audit risk and quality	skylos . -a	Dead code, risky flows, secrets, quality, and SCA findings
Higher-confidence dead code	skylos . --trace	Cross-reference static findings with runtime activity
Review only changed lines	skylos . --diff origin/main	Focus findings on active work instead of legacy debt
Local staged hook	skylos agent pre-commit .	Fast staged check for security, secrets, and quality
Gate locally	skylos --gate	Fail on findings before code leaves your machine
Set up CI/CD	skylos cicd init	Generate a GitHub Actions workflow in 30 seconds
Gate in CI	skylos cicd gate --input results.json	Fail builds when issues cross your threshold

Optional Workflows

Objective	Command	Outcome
Detect Unused Pytest Fixtures	skylos . --pytest-fixtures	Find unused @pytest.fixture across tests + conftest
AI-Powered Analysis	skylos agent scan . --model gpt-4.1	Fast static + LLM file review with dead-code verification available on demand
Dead Code Verification	skylos agent verify . --model gpt-4.1	Dead-code-only second pass: static findings reviewed by the LLM
Security Audit	skylos agent scan . --security	Deep LLM security review with interactive file selection
Auto-Remediate	skylos agent remediate . --auto-pr	Scan, fix, test, and open a PR — end to end
Code Cleanup	skylos agent remediate . --standards	LLM-guided code quality cleanup against coding standards
PR Review	skylos agent scan . --changed	Analyze only git-changed files
PR Review (JSON)	skylos agent scan . --changed --format json -o results.json	LLM review with code-level fix suggestions
Local LLM	skylos agent scan . --base-url http://localhost:11434/v1 --model codellama	Use Ollama/LM Studio (no API key needed)
PR Review (CI)	skylos cicd review -i results.json	Post inline comments on PRs
AI Defense: Discover	skylos discover .	Map all LLM integrations in your codebase
AI Defense: Defend	skylos defend .	Check LLM integrations for missing guardrails
AI Defense: CI Gate	skylos defend . --fail-on critical --min-score 70	Block PRs with critical AI defense gaps
Whitelist	skylos whitelist 'handle_*'	Suppress known dynamic patterns

Technical Debt Hotspots

Use skylos debt <path> to rank structural debt hotspots without collapsing everything into a single urgency number.

• score is the project-level structural debt score.
• priority is the hotspot triage score used for ordering fix candidates.
• --changed limits the visible hotspot list to changed files, but keeps the structural debt score anchored to the whole project.

# Full project debt scan
skylos debt .

# Review only changed hotspots without distorting the project score
skylos debt . --changed

# Compare the current project against a saved debt baseline
skylos debt . --baseline

# Save a repo-level debt baseline
skylos debt . --save-baseline

Debt policy files such as skylos-debt.yaml are discovered from the scan target upward, and explicit CLI flags like --top override policy defaults.

Demo

Backup (GitHub): https://github.com/duriantaco/skylos/discussions/82

Key Capabilities

The core product is dead code detection, security scanning, and PR gating. The AI-focused features below are optional layers on top of that baseline workflow.

Security Scanning (SAST)

• Taint Analysis: Traces untrusted input from API endpoints to databases to prevent SQL Injection and XSS.
• Secrets Detection: Hunts down hardcoded API keys (AWS, Stripe, OpenAI) and private credentials before commit.
• Vulnerability Checks: Flags dangerous patterns like eval(), unsafe pickle, and weak cryptography.

AI-Generated Code Guardrails

Skylos can also flag common AI-generated code mistakes. Every finding includes vibe_category and ai_likelihood (high/medium/low) metadata so you can filter them separately if you want.

• Phantom Call Detection: Catches calls to security functions (sanitize_input, validate_token, check_permission, etc.) that are never defined or imported, including local-module references like security.require_auth(). hallucinated_reference, high
• Phantom Decorator Detection: Catches security decorators (@require_auth, @rate_limit, @authenticate, etc.) that are never defined or imported, including local-module decorators like @guards.require_auth. hallucinated_reference, high
• Unfinished Generation: Detects functions with only pass, ..., or raise NotImplementedError — AI-generated stubs that silently do nothing in production. incomplete_generation, medium
• Undefined Config: Flags os.getenv("ENABLE_X") referencing feature flags that are never defined anywhere in the project. ghost_config, medium
• Stale Mock Detection: Catches mock.patch("app.email.send_email") where send_email no longer exists — AI renames functions but leaves tests pointing at the old name. stale_reference, medium
• Security TODO Scanners: Flags # TODO: add auth placeholders that AI left behind and nobody finished.
• Disabled Security Controls: Detects verify=False, @csrf_exempt, DEBUG=True, and ALLOWED_HOSTS=["*"].
• Credential & Randomness Checks: Catches hardcoded passwords and random.choice() used for security-sensitive values like tokens and OTPs.

Prompt Injection and Content Scanning

These checks run under --danger and look for prompt injection patterns or obfuscated instructions in repository content.

• Multi-File Prompt Injection Scanner: Scans Python, Markdown, YAML, JSON, TOML, and .env files for hidden instruction payloads — instruction overrides ("ignore previous instructions"), role hijacking ("you are now"), AI-targeted suppression ("do not flag", "skip security"), data exfiltration prompts, and AI-targeting phrases.
• Text Canonicalization Engine: NFKC normalization, whitespace folding, and confusable replacement neutralize obfuscation before pattern matching.
• Zero-Width & Invisible Unicode: Detects zero-width spaces, joiners, BOM, and bidi overrides (U+200B–U+202E) that hide payloads from human reviewers.
• Base64 Obfuscation Detection: Automatically decodes base64-encoded strings and re-scans for injection content.
• Homoglyph / Mixed-Script Detection: Flags Cyrillic and Greek characters mixed with Latin text (e.g., Cyrillic 'а' in password) that bypass visual review.
• Location-Aware Severity: Findings in README files, HTML comments, and YAML prompt fields get elevated severity. Test files are automatically skipped.

Advanced: AI Defense for LLM Apps

Static analysis for AI application security that maps every LLM call in your codebase and checks for missing guardrails. Python support is mature; direct TypeScript / TSX discovery and shared guardrail checks are now available in beta.

# Discover all LLM integrations
skylos discover .

# Check defenses and get a scored report
skylos defend .

# CI gate: fail on critical gaps, require 70% defense score
skylos defend . --fail-on critical --min-score 70

# JSON output for dashboards and pipelines
skylos defend . --json -o defense-report.json

# Filter by OWASP LLM Top 10 category
skylos defend . --owasp LLM01,LLM04

13 checks across defense and ops:

Check	Severity	OWASP	What it detects
no-dangerous-sink	Critical	LLM02	LLM output flowing to eval/exec/subprocess
untrusted-input-to-prompt	Critical	LLM01	Raw user input in prompt with no processing
tool-scope	Critical	LLM04	Agent tools with dangerous system calls
tool-schema-present	Critical	LLM04	Agent tools without typed schemas
output-validation	High	LLM02	LLM output used without structured validation
prompt-delimiter	High	LLM01	User input in prompts without delimiters
rag-context-isolation	High	LLM01	RAG context injected without isolation
output-pii-filter	High	LLM06	No PII filtering on user-facing LLM output
model-pinned	Medium	LLM03	Model version not pinned (floating alias)
input-length-limit	Low	LLM01	No input length check before LLM call
logging-present	Medium	Ops	No logging around LLM calls
cost-controls	Medium	Ops	No max_tokens set on LLM calls
rate-limiting	Medium	Ops	No rate limiting on LLM endpoints

Defense and ops scores are tracked separately — adding logging won't inflate your security score.

Custom policy via skylos-defend.yaml:

rules:
  model-pinned:
    severity: critical    # Upgrade severity
  input-length-limit:
    enabled: false        # Disable check
gate:
  min_score: 70
  fail_on: high

Supports OpenAI, Anthropic, Google Gemini, Cohere, Mistral, Ollama, Together AI, Groq, Fireworks, Replicate, LiteLLM, LangChain, LlamaIndex, CrewAI, and AutoGen.

TypeScript beta scope today is intentionally narrow: direct SDK patterns in .ts / .tsx / .js / .jsx for providers such as OpenAI, Anthropic, Google Gemini, LiteLLM, and Vercel AI SDK. Wrapper-heavy code and full TS agent parity are follow-up work.

Dead Code Detection & Cleanup

• Find Unused Code: Identifies unreachable functions, orphan classes, and unused imports with confidence scoring.
• Smart Tracing: Distinguishes between truly dead code and dynamic frameworks (Flask/Django routes, Pytest fixtures).
• Safe Pruning: Uses LibCST to safely remove dead code without breaking syntax.

Advanced: Agents, Reviews, and Remediation

• Context-aware audits: Combines static analysis speed with LLM reasoning to validate findings and filter noise.
• Remediation workflow: skylos agent remediate can scan, generate fixes, run tests, and optionally open a PR.
• Local model support: Supports Ollama and other OpenAI-compatible local endpoints if you want code to stay on your machine.

CI/CD and PR Gating

• 30-Second Workflow Setup: skylos cicd init generates GitHub Actions workflows with sensible defaults.
• Diff-Aware Enforcement: Gate only the lines that changed, fail on severity thresholds, and keep legacy debt manageable with baselines.
• PR-Native Feedback: GitHub annotations, inline review comments, and optional dashboard upload keep findings where teams already work.
• Corpus Guard: Require the Corpus Guard workflow on PRs to catch dead-code precision regressions against curated framework and language fixtures.

Safe Cleanup and Workflow Controls

• CST-safe removals: Uses LibCST to remove selected imports or functions (handles multiline imports, aliases, decorators, async etc..)
• Logic Awareness: Deep integration for Python frameworks (Django, Flask, FastAPI) and TypeScript (Tree-sitter) to identify active routes and dependencies.
• Granular Filtering: Skip lines tagged with # pragma: no skylos, # pragma: no cover, or # noqa

Operational Governance & Runtime

• Coverage Integration: Auto-detects .skylos-trace files to verify dead code with runtime data
• Quality Gates: Enforces hard thresholds for complexity, nesting, and security risk via pyproject.toml to block non-compliant PRs
• Interactive CLI: Manually verify and remove/comment-out findings through an inquirer-based terminal interface
• Security-Audit Mode: Leverages an independent reasoning loop to identify security vulnerabilities

Pytest Hygiene

• Unused Fixture Detection: Finds unused @pytest.fixture definitions in test_*.py and conftest.py
• Cross-file Resolution: Tracks fixtures used across modules, not just within the same file

Multi-Language Support

Language	Parser	Dead Code	Security	Quality
Python	AST	✅	✅	✅
TypeScript/TSX	Tree-sitter	✅	✅	✅
Java	Tree-sitter	✅	✅	✅
Go	Standalone binary	✅	-	-

Languages are auto-detected by file extension. Mixed-language repos work out of the box. No Node.js or JDK required — all parsers are built-in via Tree-sitter.

TypeScript Rules

Rule	ID	What It Catches
Dead Code
Functions	-	Unused functions, arrow functions, and overloads
Classes	-	Unused classes, interfaces, enums, and type aliases
Imports	-	Unused named, default, and namespace imports
Methods	-	Unused methods (lifecycle methods excluded)
Security
eval()	SKY-D201	eval() usage
Dynamic exec	SKY-D202	exec(), new Function(), setTimeout with string
XSS	SKY-D226	innerHTML, outerHTML, document.write(), dangerouslySetInnerHTML
SQL injection	SKY-D211	Template literal / f-string in SQL query
Command injection	SKY-D212	child_process.exec(), os.system()
SSRF	SKY-D216	fetch()/axios with variable URL
Open redirect	SKY-D230	res.redirect() with variable argument
Weak hash	SKY-D207/D208	MD5 / SHA1 usage
Prototype pollution	SKY-D510	__proto__ access
Dynamic require	SKY-D245	require() with variable argument
JWT bypass	SKY-D246	jwt.decode() without verification
CORS wildcard	SKY-D247	cors({ origin: '*' })
Internal URL	SKY-D248	Hardcoded localhost/127.0.0.1 URLs
Insecure random	SKY-D250	Math.random() for security-sensitive ops
Sensitive logs	SKY-D251	Passwords/tokens passed to console.log()
Insecure cookie	SKY-D252	Missing httpOnly/secure flags
Timing attack	SKY-D253	===/== comparison of secrets
Storage tokens	SKY-D270	Sensitive data in localStorage/sessionStorage
Error disclosure	SKY-D271	error.stack/.sql sent in HTTP response
Secrets	SKY-S101	Hardcoded API keys + high-entropy strings
Quality
Complexity	SKY-Q301	Cyclomatic complexity exceeds threshold
Nesting depth	SKY-Q302	Too many nested levels
Function length	SKY-C304	Function exceeds line limit
Too many params	SKY-C303	Function has too many parameters
Duplicate condition	SKY-Q305	Identical condition in if-else-if chain
Await in loop	SKY-Q402	await inside for/while loop
Unreachable code	SKY-UC002	Code after return/throw/break/continue

Framework-aware: Next.js convention exports (page.tsx, layout.tsx, route.ts, middleware.ts), config exports (getServerSideProps, generateMetadata, revalidate), React patterns (memo, forwardRef), and exported custom hooks (use*) are automatically excluded from dead code reports.

TypeScript dead code detection tracks: callbacks, type annotations, generics, decorators, inheritance (extends), object shorthand, spread, re-exports, and typeof references. Benchmarked at 95% recall with 0 false positives on alive code.

Installation

Basic Installation

## from pypi
pip install skylos

## with LLM-powered features (agent verify, agent remediate, etc.)
pip install skylos[llm]

## with Rust-accelerated analysis (up to 63x faster)
pip install skylos[fast]

## both
pip install skylos[llm,fast]

## or from source
git clone https://github.com/duriantaco/skylos.git
cd skylos

pip install .

skylos[fast] installs an optional Rust backend that accelerates clone detection (63x), file discovery (5x), coupling analysis, and cycle detection. Same results, just faster. Pure Python works fine without it — the Rust module is auto-detected at runtime.

skylos[llm] installs litellm for LLM-powered features (skylos agent verify, skylos agent remediate, --llm). Core static analysis works without it.

🎯 What's Next?

After installation, we recommend:

1. Set up CI/CD (30 seconds):

   skylos cicd init
   git add .github/workflows/skylos.yml && git push
   

   This will automatically scan every PR for dead code and security issues.

2. Run your first scan:

   skylos .                              # Dead code only
   skylos . --danger --secrets           # Include security checks
   

3. Keep scans focused on active work:

   skylos . --diff origin/main
   

4. Try advanced workflows only if you need them:

   skylos agent review . --model gpt-4.1
   skylos defend .
   

See all commands in the Quick Start table

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Skylos vs. Vulture Benchmark

We benchmarked Skylos against Vulture on 9 of the most popular Python repositories on GitHub — 350k+ combined stars, covering HTTP clients, web frameworks, CLI tools, data validation, terminal UIs, and progress bars. Every single finding was manually verified against the source code. No automated labelling, no cherry-picking.

Why These 9 Repos?

We deliberately chose projects that stress-test dead code detection in different ways:

Repository	Stars	What It Tests
psf/requests	53k	__init__.py re-exports, Sphinx conf, pytest classes
pallets/click	17k	IO protocol methods (io.RawIOBase subclasses), nonlocal closures
encode/starlette	10k	ASGI interface params, polymorphic dispatch, public API methods
Textualize/rich	51k	__rich_console__ protocol, sentinel vars via f_locals, metaclasses
encode/httpx	14k	Transport/auth protocol methods, zero dead code (pure FP test)
pallets/flask	69k	Jinja2 template globals, Werkzeug protocol methods, extension hooks
pydantic/pydantic	23k	Mypy plugin hooks, hypothesis @resolves, __getattr__ config
fastapi/fastapi	82k	100+ OpenAPI spec model fields, Starlette base class overrides
tqdm/tqdm	30k	Keras/Dask callbacks, Rich column rendering, pandas monkey-patching

No repo was excluded for having unfavorable results. We include repos where Vulture beats Skylos (click, starlette, tqdm).

Results

Repository	Dead Items	Skylos TP	Skylos FP	Vulture TP	Vulture FP
psf/requests	6	6	35	6	58
pallets/click	7	7	8	6	6
encode/starlette	1	1	4	1	2
Textualize/rich	13	13	14	10	8
encode/httpx	0	0	6	0	59
pallets/flask	7	7	12	6	260
pydantic/pydantic	11	11	93	10	112
fastapi/fastapi	6	6	30	4	102
tqdm/tqdm	1	0	18	1	37
Total	52	51	220	44	644

Metric	Skylos	Vulture
Recall	98.1% (51/52)	84.6% (44/52)
False Positives	220	644
Dead items found	51	44

Skylos finds 7 more dead items than Vulture with 3x fewer false positives.

Why Skylos Produces Fewer False Positives

Vulture uses flat name matching — if the bare name X appears anywhere as a string or identifier, all definitions named X are considered used. This works well for simple cases but drowns in noise on framework-heavy codebases:

• Flask (260 Vulture FP): Vulture flags every Jinja2 template global, Werkzeug protocol method, and Flask extension hook. Skylos recognizes Flask/Werkzeug patterns.
• Pydantic (112 Vulture FP): Vulture flags all config class annotations, TYPE_CHECKING imports, and mypy plugin hooks. Skylos understands Pydantic model fields and __getattr__ dynamic access.
• FastAPI (102 Vulture FP): Vulture flags 100+ OpenAPI spec model fields (Pydantic BaseModel attributes like maxLength, exclusiveMinimum). Skylos recognizes these as schema definitions.
• httpx (59 Vulture FP): Vulture flags every transport and auth protocol method. Skylos suppresses interface implementations.

Where Skylos Still Loses (Honestly)

• click (8 vs 6 FP): IO protocol methods (readable, readinto) on io.RawIOBase subclasses — called by Python's IO stack, not by direct call sites.
• starlette (4 vs 2 FP): Instance method calls across files (obj.method()) not resolved back to class definitions.
• tqdm (18 vs 37 FP, 0 vs 1 TP): Skylos misses 1 dead function in __init__.py because it suppresses __init__.py definitions as potential re-exports.

Reproduce any benchmark: cd real_life_examples/{repo} && python3 ../benchmark_{repo}.py

Full methodology and per-repo breakdowns in the skylos-demo repository.

Skylos vs. Knip (TypeScript)

We also benchmarked Skylos against Knip on a real-world TypeScript library:

	unjs/consola (7k stars, 21 files, ~2,050 LOC)
Dead items	4 (entire orphaned src/utils/format.ts module)

Metric	Skylos	Knip
Recall	100% (4/4)	100% (4/4)
Precision	36.4%	7.5%
F1 Score	53.3%	14.0%
Speed	6.83s	11.08s

Both tools find all dead code. Skylos has ~5x better precision — Knip incorrectly flags package entry points as dead files (its package.json exports point to dist/ not src/) and reports public API re-exports as unused.

Reproduce: cd real_life_examples/consola && python3 ../benchmark_consola.py

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Projects Using Skylos

If you use Skylos in a public repository, open an issue and add it here. This list is based on self-submissions, so it will stay small until more teams opt in publicly.

Project	Description
Skylos	Uses Skylos on itself for dead code, security, and CI gating
Your project here	Add yours

Add your project →

---

How It Works

Skylos builds a reference graph of your entire codebase - who defines what, who calls what, across all files.

Parse all files -> Build definition map -> Track references -> Find orphans (zero refs = dead)

High Precision & Confidence Scoring

Static analysis often struggles with Python's dynamic nature (e.g., getattr, pytest.fixture). Skylos minimizes false positives through:

1. Confidence Scoring: Grades findings (High/Medium/Low) so you only see what matters.
2. Hybrid Verification: Uses LLM reasoning to double-check static findings before reporting.
3. Runtime Tracing: Optional --trace mode validates "dead" code against actual runtime execution.

Confidence	Meaning	Action
100	Definitely unused	Safe to delete
60	Probably unused (default threshold)	Review first
40	Maybe unused (framework helpers)	Likely false positive
20	Possibly unused (decorated/routes)	Almost certainly used
0	Show everything	Debug mode

skylos . -c 60  # Default: high-confidence findings only
skylos . -c 30  # Include framework helpers  
skylos . -c 0  # Everything

Framework Detection

When Skylos sees Flask, Django, FastAPI, Next.js, or React imports, it adjusts scoring automatically:

Pattern	Handling
@app.route, @router.get	Entry point → marked as used
app.add_url_rule(...), app.add_api_route(...), app.add_route(...), app.register_listener(...), app.register_middleware(...)	Imperative route or lifecycle registration → marked as used
@pytest.fixture	Treated as a pytest entrypoint, but can be reported as unused if never referenced
@pytest.hookimpl, @hookimpl	Plugin hook implementation → marked as used
@celery.task	Entry point → marked as used
getattr(mod, "func")	Tracks dynamic reference
getattr(mod, f"handle_{x}")	Tracks pattern handle_*
Next.js page.tsx, layout.tsx, route.ts	Default/named exports → marked as used
Next.js getServerSideProps, generateMetadata	Config exports → marked as used
React.memo(), forwardRef()	Wrapped components → marked as used
Exported use* hooks	Custom hooks → marked as used

Test File Exclusion

Tests call code in weird ways that look like dead code. By default, Skylos excludes:

Detected By	Examples
Path	/tests/, /test/, *_test.py
Imports	pytest, unittest, mock
Decorators	@pytest.fixture, @patch

# These are auto-excluded (confidence set to 0)
/project/tests/test_user.py
/project/test/helper.py  

# These are analyzed normally
/project/user.py
/project/test_data.py  # Doesn't end with _test.py

Want test files included? Use --include-folder tests.

Philosophy

When ambiguous, we'd rather miss dead code than flag live code as dead.

Framework endpoints are called externally (HTTP, signals). Name resolution handles aliases. When things get unclear, we err on the side of caution.

Precision Regression Guard

Skylos ships a curated corpus of small fixtures that encode framework contracts and important Python runtime patterns we must not regress on.

Run it locally when you change analysis behavior:

python3 scripts/corpus_ci.py --manifest corpus/manifest.json

In GitHub, keep the Corpus Guard workflow required in branch protection. When you fix a confirmed false positive, add a focused fixture and expectation to the corpus in the same change.

Unused Pytest Fixtures

Skylos can detect pytest fixtures that are defined but never used.

skylos . --pytest-fixtures

This includes fixtures inside conftest.py, since conftest.py is the standard place to store shared test fixtures.

Advanced Workflows

These commands are optional. Use them when you want LLM-assisted review, remediation, or AI defense on top of the core scanner and CI gate.

Skylos uses a hybrid architecture that combines static analysis with LLM reasoning:

Why Hybrid?

Approach	Recall	Precision	Logic Bugs
Static only	Low	High	❌
LLM only	High	Medium	✅
Hybrid	Highest	High	✅

Research shows LLMs find vulnerabilities that static analysis misses, while static analysis validates LLM suggestions. However, LLMs are prone to false positives in dead code if they are asked to invent findings from raw source alone.

Skylos now splits agent workflows into a fast review lane and a slower verification lane.

For dead code, Skylos uses a stricter contract:

• static analysis generates the candidate list
• repo facts and graph evidence are gathered around each candidate
• skylos agent verify is the dedicated dead-code adjudication pass
• skylos agent scan --verify-dead-code adds that slower verifier back into the review pipeline when you explicitly want it
• deterministic suppressors still exist, and in judge_all mode they are attached as evidence instead of silently deciding the outcome

Use --verification-mode production if you want the cheaper deterministic-first path for agent verify.

Agent Commands

Command	Description
skylos agent scan PATH	Fast hybrid review: static findings plus one-pass LLM security/quality review
skylos agent scan PATH --verify-dead-code	Same review path, plus the slower dead-code verification pass
skylos agent scan PATH --no-fixes	Same review pipeline, skip fix suggestions (faster)
skylos agent scan PATH --changed	Analyze only git-changed files
skylos agent scan PATH --security	Security-only LLM audit with interactive file selection
skylos agent verify PATH	Dead-code-only verification pass over static findings
skylos agent verify PATH --fix --pr	Verify, generate removal patches, create branch and commit
skylos agent remediate PATH	End-to-end: scan, fix, test, and create PR
skylos agent remediate PATH --standards	LLM-guided cleanup with built-in standards (or --standards custom.md)
skylos agent triage suggest	Show auto-triage candidates from learned patterns
skylos agent triage dismiss ID	Dismiss a finding from the queue

Provider Configuration

Skylos supports cloud and local LLM providers:

# Cloud - OpenAI (auto-detected from model name)
skylos agent scan . --model gpt-4.1

# Cloud - Anthropic (auto-detected from model name)
skylos agent scan . --model claude-sonnet-4-20250514

# Local - Ollama
skylos agent scan . \
  --provider openai \
  --base-url http://localhost:11434/v1 \
  --model qwen2.5-coder:7b

# Cheaper dead-code verification path
skylos agent verify . \
  --model claude-sonnet-4-20250514 \
  --verification-mode production

Note: You can use the --model flag to specify the model that you want. We support Gemini, Groq, Anthropic, ChatGPT and Mistral.

Keys and configuration

Skylos can use API keys from (1) skylos key, or (2) environment variables.

Recommended (interactive)

skylos key
# opens a menu:
# - list keys
# - add key (openai / anthropic / google / groq / mistral / ...)
# - remove key

Environment Variables

Set defaults to avoid repeating flags:

# API Keys
export OPENAI_API_KEY="sk-..."
export ANTHROPIC_API_KEY="sk-ant-..."

# Default to local Ollama
export SKYLOS_LLM_PROVIDER=openai
export SKYLOS_LLM_BASE_URL=http://localhost:11434/v1

LLM PR Review

skylos agent scan --changed analyzes git-changed files, runs static analysis, then uses the LLM for fast file review and code-level fix suggestions. Dead-code verification is optional and not on the critical path by default.

# Run LLM review and output JSON
skylos agent scan . --changed --model claude-sonnet-4-20250514 --format json -o llm-results.json

# Use with cicd review to post inline comments on PRs
skylos cicd review --input results.json --llm-input llm-results.json

The hybrid pipeline runs in stages:

1. Static analysis — finds security, quality, and dead code issues
2. LLM review — one-pass file or diff review for security, logic, quality, and performance issues static analysis may miss
3. Optional dead-code verification — when requested, the LLM judges static dead-code candidates using graph evidence, repo facts, and surrounding context
4. Code fix generation — for each reported finding, generates the problematic code snippet and a corrected version

Each PR comment shows the exact vulnerable lines and a drop-in replacement fix.

What LLM Analysis Detects

Category	Examples
Hallucinations	Calls to functions that don't exist
Logic bugs	Off-by-one, incorrect conditions, missing edge cases
Business logic	Auth bypasses, broken access control
Context issues	Problems requiring understanding of intent

Local LLM Setup (Ollama)

# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh

# Pull a code model
ollama pull qwen2.5-coder:7b

# Use with Skylos
skylos agent scan ./src \
  --provider openai \
  --base-url http://localhost:11434/v1 \
  --model qwen2.5-coder:7b

Remediation Agent

The remediation agent automates the full fix lifecycle. It scans your project, prioritizes findings, generates fixes via the LLM, validates each fix by running your test suite, and optionally opens a PR.

# Preview what would be fixed (safe, no changes)
skylos agent remediate . --dry-run

# Fix up to 5 critical/high issues, validate with tests
skylos agent remediate . --max-fixes 5 --severity high

# Full auto: fix, test, create PR
skylos agent remediate . --auto-pr --model gpt-4.1

# Use a custom test command
skylos agent remediate . --test-cmd "pytest test/ -x"

Safety guardrails:

• Dry run by default — use --dry-run to preview without touching files
• Fixes that break tests are automatically reverted
• Low-confidence fixes are skipped
• After applying a fix, Skylos re-scans to confirm the finding is actually gone
• --auto-pr always works on a new branch, never touches main
• --max-fixes prevents runaway changes (default 10)

Recommended Models

Model	Provider	Use Case
gpt-4.1	OpenAI	Best accuracy
claude-sonnet-4-20250514	Anthropic	Best reasoning
qwen2.5-coder:7b	Ollama	Fast local analysis
codellama:13b	Ollama	Better local accuracy

CI/CD

Run Skylos in your CI pipeline with quality gates, GitHub annotations, and PR review comments.

Quick Start (30 seconds)

# Auto-generate a GitHub Actions workflow
skylos cicd init

# Commit and activate
git add .github/workflows/skylos.yml && git push

That's it! Your next PR will have:

• Dead code detection
• Security scanning (SQLi, SSRF, secrets)
• Quality checks
• Inline PR comments with clickable file:line links
• Quality gate that fails builds on critical issues

Want AI-powered code fixes on PRs?

skylos cicd init --llm --model claude-sonnet-4-20250514

This adds an LLM step that generates code-level fix suggestions — showing the vulnerable code and the corrected version inline on your PR.

Optional GitHub Secrets

For the default skylos cicd init workflow, you do not need any Skylos-specific secrets. Add these only if you enable the matching feature in GitHub Actions (Settings > Secrets and variables > Actions):

Secret	When needed	Description
ANTHROPIC_API_KEY	If using Claude models	Your Anthropic API key
OPENAI_API_KEY	If using GPT models	Your OpenAI API key
SKYLOS_API_KEY	For Skylos Cloud features	Get from skylos.dev
SKYLOS_TOKEN	If using --upload	Upload token from skylos.dev/dashboard/settings

GH_TOKEN is automatically provided by GitHub Actions — no setup needed for PR comments.

Release Automation

Skylos uses a split release workflow:

• .github/workflows/release-please.yml updates CHANGELOG.md, bumps pyproject.toml, and opens or updates the release PR.
• .github/workflows/publish.yml publishes from an immutable release tag, either automatically on v* tag pushes or manually via workflow_dispatch.
• For protected repos, prefer a dedicated RELEASE_PLEASE_TOKEN secret so bot-authored release PRs can satisfy required PR checks.

First-time bootstrap (already configured in this repo)

Release Please is bootstrapped with:

• tools/release/.release-please-manifest.json set to 4.2.1
• tools/release/release-please-config.json set with bootstrap-sha at the commit that prepared 4.2.1 (a498b27b6902b34e469acfddac1068635aae8122)

This prevents backfilling old history and starts automated releases from the current baseline.

Normal release flow

1. Merge conventional commits into main (for example: feat: ..., fix: ...).
2. Release Please opens/updates the release PR.
3. Merge the release PR to main.
4. Release Please creates the GitHub release tag (vX.Y.Z).
5. The tag push triggers .github/workflows/publish.yml.
6. Skylos builds and publishes to PyPI from that tag using PYPI_TOKEN.

Manual build/publish checks

If you need to validate packaging before a release:

python -m pip install --upgrade pip
python -m pip install "build>=1.2.2" "twine>=6.1.0"
python -m build --sdist --wheel --outdir dist
python -m twine check dist/*

If you need to manually run the fallback publish workflow, use Actions -> Build and publish -> Run workflow and set ref to the exact release tag (for example v4.2.1). Do not use a branch name.

PR title types used for release semantics

Skylos validates semantic PR titles via .github/workflows/pr-title.yml with these allowed types:

• feat
• fix
• docs
• refactor
• test
• chore
• perf
• style
• ci
• infra
• revert

For complete release ownership, guardrails, and recovery steps, see RELEASE_WORKFLOW.md.

Release Workflow Runbook

Release roles, prerequisites, branch protection guidance, semantic type policy, and incident recovery steps are documented in RELEASE_WORKFLOW.md.

Command Reference

Core Analysis

Command	Description
skylos suite <path>	Full local bundle: static analysis, debt, Python AI defense, and provenance summary
skylos <path>	Dead code, security, and quality analysis
skylos debt <path>	Technical debt hotspot analysis with baseline-aware prioritization
skylos discover <path>	Map LLM/AI integrations in your codebase
skylos defend <path>	Check LLM integrations for missing defenses

AI Agent

Command	Description
skylos agent scan <path>	Fast hybrid static + LLM review
skylos agent verify <path>	LLM-verify dead code (100% accuracy)
skylos agent remediate <path>	Auto-fix issues and create PR
skylos agent watch <path>	Continuous repo monitoring with optional triage pattern learning
skylos agent pre-commit <path>	Staged local hook for security, secrets, and quality
skylos agent triage	Manage finding triage (dismiss/snooze)

CI/CD

Command	Description
skylos cicd init	Generate GitHub Actions workflow
skylos cicd gate	Quality gate (CI exit code)
skylos cicd annotate	Emit GitHub Actions annotations
skylos cicd review	Post inline PR review comments

Account

Command	Description
skylos login	Connect to Skylos Cloud
skylos whoami	Show connected account info
skylos key	Manage API keys
skylos credits	Check credit balance

Utility

Command	Description
skylos init	Initialize config in pyproject.toml
skylos baseline <path>	Save current findings as baseline
skylos whitelist <pattern>	Manage whitelisted symbols
skylos badge	Get badge markdown for README
skylos rules	Install/manage community rule packs
skylos doctor	Check installation health
skylos clean	Remove cache and state files
skylos tour	Guided tour of capabilities
skylos commands	List all commands (flat)

Run skylos <command> --help for detailed usage of any command.

Commands (Detailed)

skylos cicd init

Generates a ready-to-use GitHub Actions workflow.

skylos cicd init
skylos cicd init --triggers pull_request schedule
skylos cicd init --analysis security quality
skylos cicd init --python-version 3.11
skylos cicd init --llm --model gpt-4.1
skylos cicd init --upload                        # include --upload step + SKYLOS_TOKEN env
skylos cicd init --upload --llm --model claude-sonnet-4-20250514  # upload + LLM
skylos cicd init --defend                        # add AI Defense check step
skylos cicd init --defend --upload               # defend + upload results to cloud
skylos cicd init --no-baseline
skylos cicd init -o .github/workflows/security.yml

skylos cicd gate

Checks findings against your quality gate. Exits 0 (pass) or 1 (fail). Uses the same check_gate() as skylos . --gate.

skylos . --danger --quality --secrets --json > results.json 2>/dev/null
skylos cicd gate --input results.json
skylos cicd gate --input results.json --strict
skylos cicd gate --input results.json --summary

You can also use the main CLI directly:

skylos . --gate --summary

Configure thresholds in pyproject.toml:

[tool.skylos.gate]
fail_on_critical = true
max_critical = 0
max_high = 5
max_security = 10
max_quality = 10

skylos cicd annotate

Emits GitHub Actions annotations (::error, ::warning, ::notice). Uses the same _emit_github_annotations() as skylos . --github, with sorting and a 50-annotation cap.

skylos cicd annotate --input results.json
skylos cicd annotate --input results.json --severity high
skylos cicd annotate --input results.json --max 30

skylos . --github

skylos cicd review

Posts inline PR review comments and a summary via gh CLI. Only comments on lines changed in the PR.

skylos cicd review --input results.json
skylos cicd review --input results.json --pr 20
skylos cicd review --input results.json --summary-only
skylos cicd review --input results.json --max-comments 10
skylos cicd review --input results.json --diff-base origin/develop

# With LLM-generated code fixes (vulnerable code → fixed code)
skylos cicd review --input results.json --llm-input llm-results.json

When --llm-input is provided, each inline comment shows the problematic code and the corrected version:

🔴 CRITICAL SKY-D211

Possible SQL injection: tainted or string-built query.

Why: User input is concatenated directly into the SQL query string.

Vulnerable code:
  results = conn.execute(f"SELECT * FROM users WHERE name LIKE '%{q}%'").fetchall()

Fixed code:
  results = conn.execute("SELECT * FROM users WHERE name LIKE ?", (f"%{q}%",)).fetchall()

In GitHub Actions, PR number and repo are auto-detected. Requires GH_TOKEN.

How It Fits Together

The gate and annotation logic lives in the core Skylos modules (gatekeeper.py and cli.py). The cicd commands are convenience wrappers that read from a JSON file and call the same functions:

skylos cicd command	Calls
gate	gatekeeper.run_gate_interaction(summary=True)
annotate	cli._emit_github_annotations(max_annotations=50)
review	New — cicd/review.py (PR comments via gh api)
init	New — cicd/workflow.py (YAML generation)

Tips

• Run analysis once, consume many times — use --json > results.json 2>/dev/null then pass --input results.json to each subcommand.
• Baseline — run skylos baseline . to snapshot existing findings, then --baseline in CI to only flag new issues.
• Local testing — all commands work locally. gate and annotate print to stdout. review requires gh CLI.

MCP Server

mcp-name: io.github.duriantaco/skylos

Skylos exposes its analysis capabilities as an MCP (Model Context Protocol) server, allowing AI assistants like Claude Desktop to scan your codebase directly.

Setup

pip install skylos

Add to your Claude Desktop config (~/.config/claude/claude_desktop_config.json on Linux, ~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

{
  "mcpServers": {
    "skylos": {
      "command": "python",
      "args": ["-m", "skylos_mcp.server"]
    }
  }
}

Available Tools

Tool	Description
analyze	Dead code detection (unused functions, imports, classes, variables)
security_scan	Security vulnerability scan (--danger equivalent)
quality_check	Code quality and complexity analysis (--quality equivalent)
secrets_scan	Hardcoded secrets detection (--secrets equivalent)
remediate	End-to-end: scan, generate LLM fixes, validate with tests
generate_fix	Generate removal patches for confirmed dead code
verify_dead_code	LLM-verify dead code findings (reduce false positives)
learn_triage	Record a triage decision for pattern learning
get_triage_suggestions	Get auto-triage candidates from learned patterns

Available Resources

Resource	URI	Description
Latest result	skylos://results/latest	Most recent analysis run
Result by ID	skylos://results/{run_id}	Specific analysis run
List results	skylos://results	All stored analysis runs

Usage in Claude Desktop

Once configured, you can ask Claude:

• "Scan my project for security issues" → calls security_scan
• "Check code quality in src/" → calls quality_check
• "Find hardcoded secrets" → calls secrets_scan
• "Fix security issues in my project" → calls remediate

Baseline Tracking

Baseline tracking lets you snapshot existing findings so CI only flags new issues introduced by a PR.

# Create baseline from current state
skylos baseline .

# Run analysis, only show findings NOT in the baseline
skylos . --danger --secrets --quality --baseline

# In CI: compare against baseline
skylos . --danger --baseline --gate

The baseline is stored in .skylos/baseline.json. Commit this file to your repo so CI can use it.

VS Code Extension

Real-time AI-powered code analysis directly in your editor.

Installation

1. Search "Skylos" in VS Code marketplace or run:

   ext install oha.skylos-vscode-extension

2. Make sure the CLI is installed:

   pip install skylos

3. (Optional) Add your API key for AI features in VS Code Settings → skylos.openaiApiKey or skylos.anthropicApiKey

How It Works

Layer	Trigger	What It Does
Static Analysis	On save	Runs Skylos CLI for dead code, secrets, dangerous patterns
AI Watcher	On idle (2s)	Sends changed functions to GPT-4/Claude for bug detection

Features

• Real-time Analysis: Detects bugs as you type — no save required
• CodeLens Buttons: "Fix with AI" and "Dismiss" appear inline on error lines
• Streaming Fixes: See fix progress in real-time
• Smart Caching: Only re-analyzes functions that actually changed
• Multi-Provider: Choose between OpenAI and Anthropic

New Features

• MCP Server Support: Connect Skylos directly to Claude Desktop or any MCP client to chat with your codebase.
• CI/CD Agents: Autonomous bots that scan, fix, test, and open PRs automatically in your pipeline.
• Hybrid Verification: Eliminates false positives by verifying static findings with LLM reasoning.

Extension Settings

Setting	Default	Description
skylos.aiProvider	"openai"	"openai" or "anthropic"
skylos.openaiApiKey	""	Your OpenAI API key
skylos.anthropicApiKey	""	Your Anthropic API key
skylos.idleMs	2000	Wait time before AI analysis (ms)
skylos.runOnSave	true	Run Skylos CLI on save
skylos.enableSecrets	true	Scan for hardcoded secrets
skylos.enableDanger	true	Flag dangerous patterns

Usage

Action	Result
Save a Python file	Skylos CLI scans the workspace
Type and pause	AI analyzes changed functions
Click "Fix with AI"	Generates fix with diff preview
Cmd+Shift+P -> "Skylos: Scan Workspace"	Full project scan

Privacy

• Static analysis runs 100% locally
• AI features send only changed function code to your configured provider
• We DO NOT collect any telemetry or data

Install from VS Code Marketplace

Gating

Block bad code before it merges. Configure thresholds, run locally, then automate in CI.

Initialize Configuration

skylos init

Creates [tool.skylos] in your pyproject.toml:

[tool.skylos]
# Quality thresholds
complexity = 10
nesting = 3
max_args = 5
max_lines = 50
duplicate_strings = 3
ignore = []
model = "gpt-4.1"

# Language overrides (optional)
[tool.skylos.languages.typescript]
complexity = 15
nesting = 4

# Gate policy
[tool.skylos.gate]
fail_on_critical = true
max_security = 0      # Zero tolerance
max_quality = 10      # Allow up to 10 warnings
strict = false

Free Tier

Run scans locally with exit codes:

skylos . --danger --gate

• Exit code 0 = passed
• Exit code 1 = failed

Use in any CI system:

name: Skylos Quality Gate

on:
  pull_request:
    branches: [main, master]

jobs:
  skylos:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-python@v5
        with:
          python-version: '3.11'
      - run: pip install skylos
      - run: skylos . --danger --gate

Limitation: Anyone with repo access can delete or modify this workflow.

---

Pro Tier

Server-controlled GitHub checks that cannot be bypassed by developers.

Quick Setup

pip install skylos
skylos sync setup

How It Works

1. Developer opens PR → GitHub App creates required check ("Queued")
2. Scan runs → Results upload to Skylos server
3. Server updates check → Pass ✅ or Fail ❌
4. Developer cannot merge until check passes

Free vs Pro

Feature	Free	Pro
Local scans	✅	✅
--gate exit codes	✅	✅
GitHub Actions	✅ (DIY)	✅ (auto)
Developer can bypass?	Yes	No
Server-controlled check	❌	✅
Slack/Discord alerts	❌	✅

GitHub App Setup

1. Dashboard -> Settings -> Install GitHub App
2. Select your repository
3. In GitHub repo settings:
   • Settings -> Branches -> Add rule -> main
   • Require status checks
   • Select "Skylos Quality Gate"

Add Token to GitHub

Repo Settings → Secrets → Actions → New secret

• Name: SKYLOS_TOKEN
• Value: (from Dashboard → Settings)

Integration and Ecosystem

Skylos is designed to live everywhere your code does—from your IDE to your deployment pipeline.

1. Integration Environments

Environment	Tool	Use Case
VS Code	Skylos Extension	Real-time guarding. Highlights code rot and risks on-save.
Web UI	skylos run	Launch a local dashboard for visual auditing. Defaults to localhost:5090; override with --port or SKYLOS_PORT.
CI/CD	GitHub Actions / Pre-commit	Automated gates that audit every PR before it merges.
Quality Gate	skylos --gate	Block deployment if security or complexity thresholds are exceeded.

2. Output Formats

Control how you consume the watchdog's findings.

Flag	Format	Primary Use
--tui	TUI Dashboard	Launch the interactive TUI dashboard.
--tree	Logic Tree	Visualizes code hierarchy and structural dependencies.
--json	Machine Raw	Piping results to jq, custom scripts, or log aggregators.
--sarif	SARIF	GitHub Code Scanning, IDE integration. Includes CWE taxonomy and per-rule CWE relationships
--llm	LLM Report	Structured findings with code context for Claude Code, Codex, or any AI agent.
-o, --output	File Export	Save the audit report directly to a file instead of stdout.

Auditing and Precision

By default, Skylos finds dead code. Enable additional scans with flags.

For dead-code precision regressions, use the checked-in corpus guard:

python3 scripts/corpus_ci.py --manifest corpus/manifest.json

This is a deterministic regression suite built from curated framework and Python-language patterns. It is not a proof of correctness, but it is the main guard against reintroducing known false positives.

Dead Code (default)

skylos .

Reading the output:

Column	Meaning
Name	The unused function, import, class, or variable
Location	file:line where it's defined
Conf	Confidence score (0–100%) — how certain Skylos is that this code is truly unused. Higher = safer to remove

Security (--danger)

Tracks tainted data from user input to dangerous sinks.

skylos . --danger

Reading the output:

Column	Meaning
Issue	The vulnerability type (e.g. SQL injection, eval) with its rule ID
Severity	Risk level: Critical > High > Medium > Low
Message	What was found and why it's dangerous
Location	file:line where the issue occurs
Symbol	The function or scope containing the vulnerable code

Rule	ID	What It Catches
Injection
SQL injection	SKY-D211	cur.execute(f"SELECT * FROM users WHERE name='{name}'")
SQL raw query	SKY-D217	sqlalchemy.text(), pandas.read_sql(), Django .raw() with tainted input
Command injection	SKY-D212	os.system(), subprocess(shell=True) with tainted input
SSRF	SKY-D216	requests.get(request.args["url"])
Path traversal	SKY-D215	open(request.args.get("p"))
XSS (mark_safe)	SKY-D226	Untrusted content passed to mark_safe() / Markup()
XSS (template)	SKY-D227	Inline template with autoescape disabled
XSS (HTML build)	SKY-D228	HTML built from unescaped user input
Open redirect	SKY-D230	User-controlled URL passed to redirect()
Dangerous Calls
eval()	SKY-D201	Dynamic code execution via eval()
exec()	SKY-D202	Dynamic code execution via exec()
os.system()	SKY-D203	OS command execution
pickle.load	SKY-D204	Unsafe deserialization
yaml.load	SKY-D206	yaml.load() without SafeLoader
Weak hash (MD5)	SKY-D207	hashlib.md5()
Weak hash (SHA1)	SKY-D208	hashlib.sha1()
shell=True	SKY-D209	subprocess with shell=True
TLS disabled	SKY-D210	requests with verify=False
Unsafe deserialization	SKY-D233	marshal.loads, shelve.open, jsonpickle.decode, dill
Web Security
CORS misconfiguration	SKY-D231	Wildcard origins, credential leaks, overly permissive headers
JWT vulnerabilities	SKY-D232	algorithms=['none'], missing verification, weak secrets
Mass assignment	SKY-D234	Django Meta.fields = '__all__' exposes all model fields
Supply Chain
Hallucinated dependency	SKY-D222	Imported package doesn't exist on PyPI (CRITICAL)
Undeclared dependency	SKY-D223	Import not declared in requirements.txt / pyproject.toml
MCP Security
Tool description poisoning	SKY-D240	Prompt injection in MCP tool metadata
Unauthenticated transport	SKY-D241	SSE/HTTP MCP server without auth middleware
Permissive resource URI	SKY-D242	Path traversal via MCP resource URI template
Network-exposed MCP	SKY-D243	MCP server bound to 0.0.0.0 without auth
Hardcoded secrets in MCP	SKY-D244	Secrets in MCP tool parameter defaults

Full list in DANGEROUS_CODE.md.

Secrets (--secrets)

Detects hardcoded credentials.

skylos . --secrets

Reading the output:

Column	Meaning
Provider	The service the secret belongs to (e.g. AWS, Stripe, GitHub) or "generic" for high-entropy strings
Message	Description of the detected credential
Preview	A masked snippet of the secret (e.g. sk_live_****)
Location	file:line where the secret was found

Providers: GitHub, GitLab, AWS, Stripe, Slack, Google, SendGrid, Twilio, private keys.

Dependency Vulnerabilities (--sca)

Scans your installed dependencies against the OSV.dev vulnerability database.

skylos . --sca

Reading the output:

Column	Meaning
Package	The dependency and its installed version (e.g. requests@2.28.0)
Vuln ID	The CVE or advisory identifier
Severity	Risk level: Critical > High > Medium > Low
Reachability	Whether your code actually calls the vulnerable code path: Reachable (confirmed risk), Unreachable (safe), or Inconclusive
Fix	The patched version to upgrade to

Quality (--quality)

Flags functions that are hard to maintain.

skylos . --quality

Reading the output:

Column	Meaning
Type	The category: Complexity, Nesting, Structure, Quality (duplicate literals, coupling, cohesion)
Name	The function, class, or string literal that triggered the finding
Detail	The measured value and the threshold — e.g. Complexity: 14 (max 10) means 14 branches were found but the limit is 10; repeated 5× (max 3) means a string literal appears 5 times but should appear at most 3
Location	file:line where the finding starts

Rule	ID	What It Catches
Complexity
Cyclomatic complexity	SKY-Q301	Too many branches/loops (default: >10)
Deep nesting	SKY-Q302	Too many nested levels (default: >3)
Async Blocking	SKY-Q401	Detects blocking calls inside async functions that kill server throughput
God class	SKY-Q501	Class has too many methods/attributes
Coupling (CBO)	SKY-Q701	High inter-class coupling (7 dependency types: inheritance, type hints, instantiation, attribute access, imports, decorators, protocol/ABC)
Cohesion (LCOM)	SKY-Q702	Low class cohesion — disconnected method groups that should be split (LCOM1/4/5 metrics with Union-Find)
Architecture
Distance from Main Sequence	SKY-Q802	Module far from ideal balance of abstractness vs instability
Zone warning	SKY-Q803	Module in Zone of Pain (rigid) or Zone of Uselessness (throwaway)
DIP violation	SKY-Q804	Stable module depends on unstable module (Dependency Inversion Principle)
Structure
Too many arguments	SKY-C303	Functions with >5 args
Function too long	SKY-C304	Functions >50 lines
Logic
Mutable default	SKY-L001	def foo(x=[]) - causes state leaks
Bare except	SKY-L002	except: swallows SystemExit
Dangerous comparison	SKY-L003	x == None instead of x is None
Anti-pattern try block	SKY-L004	Nested try, or try wrapping too much logic
Unused exception var	SKY-L005	except Error as e: where e is never referenced
Inconsistent return	SKY-L006	Function returns both values and None
Duplicate string literal	SKY-L027	Same string repeated 3+ times (see suppressing duplicate strings)
Too many returns	SKY-L028	Function has 5+ return statements
Boolean trap	SKY-L029	Boolean positional parameter harms call-site readability
Performance
Memory load	SKY-P401	.read() / .readlines() loads entire file
Pandas no chunk	SKY-P402	read_csv() without chunksize
Nested loop	SKY-P403	O(N²) complexity
Unreachable
Unreachable Code	SKY-UC001	if False: or else after always-true
Empty
Empty File	SKY-E002	Empty File

To ignore a specific rule:

# pyproject.toml
[tool.skylos]
ignore = ["SKY-P403"]  # Allow nested loops

Tune thresholds and disable rules in pyproject.toml:

[tool.skylos]
# Adjust thresholds
complexity = 15        # Default: 10
nesting = 4            # Default: 3
max_args = 7           # Default: 5
max_lines = 80

Suppressing Duplicate String Findings

Skylos flags string literals that appear 3+ times (rule SKY-L027). If a repeated string is intentional (e.g. a status value checked in multiple places), you have three options:

Option 1: Raise the threshold — only flag strings repeated more than N times:

# pyproject.toml
[tool.skylos]
duplicate_strings = 10   # Default: 3. Set to 999 to effectively disable.

Option 2: Disable the rule entirely:

# pyproject.toml
[tool.skylos]
ignore = ["SKY-L027"]

Option 3: Suppress inline — on the specific line:

if somevar == "lokal":  # skylos: ignore
    do_something()

Technical Debt (skylos debt)

Ranks structural debt hotspots using the existing static findings from quality, architecture, and dead code analysis.

skylos debt .
skylos debt . --changed
skylos debt . --baseline
skylos debt . --save-baseline
skylos debt . --history
skylos debt . --json

How the debt output works:

Field	Meaning
score	Structural debt score for the hotspot itself
priority	Triage priority for what to fix next. Changed files and baseline drift raise this without changing structural debt score
project score	Repo-level structural debt score. This stays project-scoped even when --changed is used
baseline status	Whether a hotspot is new, worsened, improved, or unchanged versus the saved debt baseline

--changed is a filter/view mode, not a different scoring model. It limits the visible hotspot list to git-changed files, but the repo debt score still reflects the full project.

Debt baselines and debt history are project-level artifacts. --save-baseline and --history only work when you scan the project root.

Default CLI Options (addopts)

Set default flags in pyproject.toml so you don't have to type them every time — just like pytest's addopts:

[tool.skylos]
addopts = ["--quality", "--danger", "--secrets"]

String format also works:

[tool.skylos]
addopts = "--quality --danger --confidence=80"

CLI flags override addopts, so you can always narrow or widen a run without editing config.

Skylos also honors [tool.skylos].exclude during CLI scans, which is the cleanest place to keep team-specific paths like custom venv names or .claude/worktrees/.

Legacy AI Flags

# LLM-powered audit (single file)
skylos . --audit

# Specify model
skylos . --audit --model claude-haiku-4-5-20251001

Note: For full project context and better results, use skylos agent scan instead. For auto-fixing, use skylos agent remediate.

Combine Everything

skylos suite .                        # Static + debt + AI defense + provenance
skylos . -a                           # All static scans (danger + secrets + quality + sca)
skylos agent remediate . --dry-run    # Preview AI-assisted fixes

Smart Tracing

Static analysis can't see everything. Python's dynamic nature means patterns like getattr(), plugin registries, and string-based dispatch look like dead code—but they're not.

Smart tracing solves this. By running your tests with sys.settrace(), Skylos records every function that actually gets called.

Quick Start

# Run tests with call tracing, then analyze
skylos . --trace

# Trace data is saved to .skylos_trace
skylos .

How It Works

Analysis Type	Accuracy	What It Catches
Static only	70-85%	Direct calls, imports, decorators
+ Framework rules	85-95%	Django/Flask routes, pytest fixtures
+ --trace	95-99%	Dynamic dispatch, plugins, registries

Example

# Static analysis will think this is dead because there's no direct call visible
def handle_login():
    return "Login handler"

# But it is actually called dynamically at runtime
action = request.args.get("action")  
func = getattr(module, f"handle_{action}")
func()  # here  

Without Tracing	With --trace
handle_login flagged as dead	handle_login marked as used

When To Use

Situation	Command
Have pytest/unittest tests	skylos . --trace
No tests	skylos . (static only; repeated runs reuse .skylos/cache/grep_results.json for grep verification)
CI with cached trace	skylos . (reuses .skylos_trace)

What Tracing Catches

These patterns are invisible to static analysis but caught with --trace:

# 1. Dynamic dispatch
func = getattr(module, f"handle_{action}")
func()

# 2. Plugin or registry patterns  
PLUGINS = []
def register(f): 
  PLUGINS.append(f)
return f

@register
def my_plugin(): ...  

# 3. Visitor patterns
class MyVisitor(ast.NodeVisitor):
    def visit_FunctionDef(self, node): ...  # Called via getattr

# 4. String-based access
globals()["my_" + "func"]()
locals()[func_name]()

Important Notes

• Tracing only adds information. Low test coverage won't create false positives. It just means some dynamic patterns may still be flagged.
• Commit .skylos_trace to reuse trace data in CI without re-running tests.
• Tests don't need to pass. Tracing records what executes, regardless of pass/fail status.

Filtering

Control what Skylos analyzes and what it ignores.

Inline Suppression

Silence specific findings with comments:

# Ignore dead code detection on this line
def internal_hook():  # pragma: no skylos
    pass

# this also works
def another():  # pragma: no cover
    pass

def yet_another():  # noqa
    pass

Folder Exclusion

By default, Skylos excludes: __pycache__, .git, .pytest_cache, .mypy_cache, .tox, htmlcov, .coverage, build, dist, *.egg-info, venv, .venv

# See what's excluded by default
skylos --list-default-excludes

# Add more exclusions
skylos . --exclude-folder vendor --exclude-folder generated

# Skylos also respects project `.gitignore` entries during file discovery
# so ignored folders like custom venvs and worktrees are skipped automatically

# Force include an excluded folder
skylos . --include-folder venv

# Scan everything (no exclusions)
skylos . --no-default-excludes

Use [tool.skylos].exclude in pyproject.toml for team-wide custom exclusions that should apply even outside .gitignore.

Rule Suppression

Disable rules globally in pyproject.toml:

[tool.skylos]
ignore = [
    "SKY-P403",   # Allow nested loops
    "SKY-L003",   # Allow == None
    "SKY-S101",   # Allow hardcoded secrets (not recommended)
]

Summary

Want to...	Do this
Skip one line	# pragma: no skylos
Skip one secret	# skylos: ignore[SKY-S101]
Skip a folder	--exclude-folder NAME
Skip a rule globally	ignore = ["SKY-XXX"] in pyproject.toml
Include excluded folder	--include-folder NAME
Skip team-specific folders	exclude = ["customenv", ".claude/worktrees"] in pyproject.toml
Run all checks	-a or addopts in pyproject.toml
Scan everything	--no-default-excludes

Whitelist Configuration

Suppress false positives permanently without inline comments cluttering your code.

CLI Commands

# Add a pattern
skylos whitelist 'handle_*'

# Add with reason
skylos whitelist dark_logic --reason "Called via globals() in dispatcher"

# View current whitelist
skylos whitelist --show

Inline Ignores

# Single line
def dynamic_handler():  # skylos: ignore
    pass

# Also works
def another():  # noqa: skylos
    pass

# Block ignore
# skylos: ignore-start
def block_one():
    pass
def block_two():
    pass
# skylos: ignore-end

Config File (pyproject.toml)

[tool.skylos.whitelist]
# Glob patterns
names = [
    "handle_*",
    "visit_*",
    "*Plugin",
]

# With reasons (shows in --show output)
[tool.skylos.whitelist.documented]
"dark_logic" = "Called via globals() string manipulation"
"BasePlugin" = "Discovered via __subclasses__()"

# Temporary (warns when expired)
[tool.skylos.whitelist.temporary]
"legacy_handler" = { reason = "Migration - JIRA-123", expires = "2026-03-01" }

# Per-path overrides
[tool.skylos.overrides."src/plugins/*"]
whitelist = ["*Plugin", "*Handler"]

Summary

Want to...	Do this
Whitelist one function	skylos whitelist func_name
Whitelist a pattern	skylos whitelist 'handle_*'
Document why	skylos whitelist x --reason "why"
Temporary whitelist	Add to [tool.skylos.whitelist.temporary] with expires
Per-folder rules	Add [tool.skylos.overrides."path/*"]
View whitelist	skylos whitelist --show
Inline ignore	# skylos: ignore or # noqa: skylos
Block ignore	# skylos: ignore-start ... # skylos: ignore-end

CLI Options

Main Command Flags

Usage: skylos [OPTIONS] PATH

Arguments:
  PATH  Path to the Python project to analyze

Options:
  -h, --help                   Show this help message and exit
  --json                       Output raw JSON instead of formatted text  
  --tree                       Output results in tree format
  --tui                        Launch interactive TUI dashboard
  --sarif                      Output SARIF format for GitHub/IDE integration
  --llm                        Output LLM-optimized report with code context for AI agents
  -c, --confidence LEVEL       Confidence threshold 0-100 (default: 60)
  --comment-out                Comment out code instead of deleting
  -o, --output FILE            Write output to file instead of stdout
  -v, --verbose                Enable verbose output
  --version                    Checks version
  -i, --interactive            Interactively select items to remove
  --dry-run                    Show what would be removed without modifying files
  --exclude-folder FOLDER      Exclude a folder from analysis (can be used multiple times)
  --include-folder FOLDER      Force include a folder that would otherwise be excluded
  --no-default-excludes        Don't exclude default folders (__pycache__, .git, venv, etc.)
  --list-default-excludes      List the default excluded folders
  --secrets                    Scan for api keys/secrets
  --danger                     Scan for dangerous code
  --quality                    Code complexity and maintainability
  --sca                        Scan dependencies for known CVEs (OSV.dev)
  -a, --all                    Enable all checks: --danger --secrets --quality --sca
  --trace                      Run tests with coverage first
  --audit                      LLM-powered logic review (legacy)
  --model MODEL                LLM model (default: gpt-4.1)
  --gate                       Fail on threshold breach (for CI)
  --force                      Bypass quality gate (emergency override)

Agent Command Flags

Usage: skylos agent <command> [OPTIONS] PATH

Commands:
  scan                Hybrid static + LLM analysis (replaces analyze/audit/review/security-audit)
  verify              LLM-verify dead code findings
  remediate           Scan, fix, test, and create PR (end-to-end)
  watch               Continuous repo monitoring
  pre-commit          Staged local hook for security, secrets, and quality
  triage              Manage finding triage (suggest/dismiss/snooze/restore)
  status              Show active-agent summary
  serve               Local HTTP API for editor integrations

Agent scan options:
  --model MODEL                LLM model to use (default: gpt-4.1)
  --provider PROVIDER          Force provider: openai or anthropic
  --base-url URL               Custom endpoint for local LLMs
  --format FORMAT              Output: table, tree, json, sarif
  -o, --output FILE            Write output to file
  --min-confidence LEVEL       Filter: high, medium, low
  --no-fixes                   Skip fix suggestions (faster)
  --changed                    Analyze only git-changed files
  --security                   Security-only LLM audit mode
  -i, --interactive            Interactive file selection (with --security)

Agent remediate options:
  --dry-run                    Show plan without applying fixes (safe preview)
  --max-fixes N                Max findings to fix per run (default: 10)
  --auto-pr                    Create branch, commit, push, and open PR
  --branch-prefix PREFIX       Git branch prefix (default: skylos/fix)
  --test-cmd CMD               Custom test command (default: auto-detect)
  --severity LEVEL             Min severity filter: critical, high, medium, low
  --standards [FILE]           Enable LLM cleanup mode (uses built-in standards, or pass custom .md)

Agent watch options:
  --once                       Run one refresh cycle and exit
  --interval SECONDS           Poll interval for continuous watch mode
  --cycles N                   Stop after N refresh cycles (0 = keep watching)
  --learn                      Enable triage pattern learning during watch mode
  --format FORMAT              Output: table, json

AI Defense Command Flags

Usage: skylos discover [OPTIONS] PATH
  Map all LLM integrations in a Python codebase.

Options:
  --json                       Output as JSON
  -o, --output FILE            Write output to file
  --exclude FOLDER [FOLDER...] Additional folders to exclude

Usage: skylos defend [OPTIONS] PATH
  Check LLM integrations for missing defenses.

Options:
  --json                       Output as JSON
  -o, --output FILE            Write output to file
  --min-severity LEVEL         Minimum severity to include (critical/high/medium/low)
  --fail-on LEVEL              Exit 1 if any defense finding at or above this severity
  --min-score N                Exit 1 if defense score below this percentage (0-100)
  --policy FILE                Path to skylos-defend.yaml policy file
  --owasp IDS                  Comma-separated OWASP LLM IDs (e.g. LLM01,LLM04)
  --exclude FOLDER [FOLDER...] Additional folders to exclude
  --upload                     Upload defense results to Skylos Cloud dashboard

Commands

Commands:
  skylos PATH                  Analyze a project (static analysis)
  skylos debt PATH             Analyze technical debt hotspots
  skylos discover PATH         Map LLM integrations in a codebase
  skylos defend PATH           Check LLM integrations for missing defenses
  skylos agent scan PATH       Hybrid static + LLM analysis
  skylos agent verify PATH     LLM-verify dead code findings
  skylos agent remediate PATH  End-to-end scan, fix, test, and PR
  skylos agent triage CMD      Manage finding triage
  skylos baseline PATH         Snapshot current findings for CI baselining
  skylos cicd init             Generate GitHub Actions workflow
  skylos cicd gate             Check findings against quality gate
  skylos cicd annotate         Emit GitHub Actions annotations
  skylos cicd review           Post inline PR review comments (supports --llm-input)
  skylos init                  Initialize pyproject.toml config
  skylos key                   Manage API keys (add/remove/list)
  skylos whitelist PATTERN     Add pattern to whitelist
  skylos whitelist --show      Display current whitelist
  skylos run                   Start web UI at localhost:5090 (default; override with --port or SKYLOS_PORT)

Whitelist Options:
  skylos whitelist PATTERN           Add glob pattern (e.g., 'handle_*')
  skylos whitelist NAME --reason X   Add with documentation
  skylos whitelist --show            Display all whitelist entries

CLI Output

Skylos displays confidence for each finding:

────────────────── Unused Functions ──────────────────
#   Name              Location        Conf
1   handle_secret     app.py:16       70%
2   totally_dead      app.py:50       90%

Higher confidence = more certain it's dead code.

Interactive Mode

The interactive mode lets you select specific functions and imports to remove:

1. Select items: Use arrow keys and spacebar to select/unselect
2. Confirm changes: Review selected items before applying
3. Auto-cleanup: Files are automatically updated

FAQ

Q: Why doesn't Skylos find 100% of dead code? A: Python's dynamic features (getattr, globals, etc.) can't be perfectly analyzed statically. No tool can achieve 100% accuracy. If they say they can, they're lying.

Q: Are these benchmarks realistic? A: They test common scenarios but can't cover every edge case. Use them as a guide, not gospel.

Q: Why doesn't Skylos detect my unused Flask routes? A: Web framework routes are given low confidence (20) because they might be called by external HTTP requests. Use --confidence 20 to see them. We acknowledge there are current limitations to this approach so use it sparingly.

Q: What confidence level should I use? A: Start with 60 (default) for safe cleanup. Use 30 for framework applications. Use 20 for more comprehensive auditing.

Q: What does --trace do? A: It runs pytest (or unittest) with coverage tracking before analysis. Functions that actually executed are marked as used with 100% confidence, eliminating false positives from dynamic dispatch patterns.

Q: Do I need 100% test coverage for --trace to be useful? A: No. However, we STRONGLY encourage you to have tests. Any coverage helps. If you have 30% test coverage, that's 30% of your code verified. The other 70% still uses static analysis. Coverage only removes false positives, it never adds them.

Q: Why are fixtures in conftest.py showing up as unused? A: conftest.py is the standard place for shared fixtures. If a fixture is defined there but never referenced by any test, Skylos will report it as unused. This is normal and safe to review.

Q: My tests are failing. Can I still use --trace? A: Yes. Coverage tracks execution, not pass/fail. Even failing tests provide coverage data.

Q: What do the numbers in the quality table mean? A: Each quality finding has a measured value and a threshold (the configured maximum). For example, Complexity: 14 (max 10) means the function has 14 branches but the limit is 10. For duplicate string literals, repeated 5× (max 3) means the same string appears 5 times — extract it to a named constant. You can tune thresholds in pyproject.toml under [tool.skylos].

Q: What's the difference between skylos . --audit and skylos agent scan? A: skylos agent scan is the fast hybrid review path: static analysis plus one-pass LLM security/quality review, with fix suggestions when enabled. Use --verify-dead-code if you also want the slower dead-code adjudication pass. The --audit flag on the base command is the legacy static-only mode.

Q: What does --verification-mode do? A: It controls how aggressively Skylos sends dead-code candidates to the LLM in verification workflows. judge_all is the most aggressive dead-code review mode and is mainly useful for agent verify or agent scan --verify-dead-code. production is cheaper and lets more obvious alive cases get suppressed before the LLM sees them.

Q: Can I use local LLMs instead of OpenAI/Anthropic? A: Yes! Use --base-url to point to Ollama, LM Studio, or any OpenAI-compatible endpoint. No API key needed for localhost.

Limitations and Troubleshooting

Limitations

• Dynamic code: getattr(), globals(), runtime imports are hard to detect
• Frameworks: Django models, Flask, FastAPI routes may appear unused but aren't
• Test data: Limited scenarios, your mileage may vary
• False positives: Always manually review before deleting code
• Secrets PoC: May emit both a provider hit and a generic high-entropy hit for the same token. Supported file types: .py, .pyi, .pyw, .env, .yaml, .yml, .json, .toml, .ini, .cfg, .conf, .ts, .tsx, .js, .jsx, .go
• Quality limitations: Quality thresholds (complexity, nesting, max_args, max_lines, duplicate_strings) are configurable in pyproject.toml under [tool.skylos].
• Coverage requires execution: The --trace flag only helps if you have tests or can run your application. Pure static analysis is still available without it.
• LLM limitations: AI analysis requires API access (cloud) or local setup (Ollama). Results depend on model quality.

Troubleshooting

1. Permission Errors

   Error: Permission denied when removing function
   

   Check file permissions before running in interactive mode.

2. Missing Dependencies

   Interactive mode requires 'inquirer' package
   

   Install with: pip install skylos[interactive]

3. No API Key Found

   # For cloud providers
   export OPENAI_API_KEY="sk-..."
   export ANTHROPIC_API_KEY="sk-ant-..."
   
   # For local LLMs (no key needed)
   skylos agent scan . --base-url http://localhost:11434/v1 --model codellama
   

4. Local LLM Connection Refused

   # Verify Ollama is running
   curl http://localhost:11434/v1/models
   
   # Check LM Studio
   curl http://localhost:1234/v1/models
   

Contributing

We welcome contributions! Please read our Contributing Guidelines before submitting pull requests.

Quick Contribution Guide

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Roadmap

• Expand our test cases
• Configuration file support
• Git hooks integration
• CI/CD integration examples
• Deployment Gatekeeper
• Further optimization
• Add new rules
• Expanding on the dangerous.py list
• Porting to uv
• Small integration with typescript
• Expanded TypeScript dead code detection (interfaces, enums, type aliases, 95% recall)
• Expand and improve on capabilities of Skylos in various other languages
• AI Defense Engine: discover + defend commands with 13 checks, OWASP LLM Top 10 mapping, ops score
• AI Defense Cloud Dashboard: upload, trend chart, OWASP grid, per-integration cards, dedicated project page
• AI Defense CI/CD: skylos cicd init --defend, pre-commit hook
• Expand the providers for LLMs (OpenAI, Anthropic, Ollama, LM Studio, vLLM)
• Expand the LLM portion for detecting dead/dangerous code (hybrid architecture)
• Coverage integration for runtime verification
• Implicit reference detection (f-string patterns, framework decorators)

More stuff coming soon!

License

This project is licensed under the Apache 2.0 License - see the LICENSE file for details.

Contact

• Author: oha
• Email: aaronoh2015@gmail.com
• GitHub: @duriantaco
• Discord: https://discord.gg/Ftn9t9tErf