Classifier Dev

What is Classifier Dev about?

Classifier Dev offers a simple, key‑less HTTP endpoint for zero‑shot text classification. It uses TypeSafe's Jev decision model as the primary engine and falls back to LLMs when needed, delivering labels and confidence scores for single‑ or multi‑label tasks.

How to use Classifier Dev?

• Direct HTTP: curl https://classifier.dev/spam,not+spam/Win+a+free+iPhone or curl "https://classifier.dev/?labels=spam,not+spam&text=Win+a+free+iPhone".
• CLI: Install globally with npm i -g classifier-dev and run classify bug,feature,praise < feedback.txt.
• Integration: Send GET requests with labels and text query parameters; the service returns the top label.

Key features of Classifier Dev

• No API key or signup – open to anyone.
• Batch support – up to 1,000 texts per request (batching is done in a single upstream call).
• Fast primary model – TypeSafe Jev answers in ~150 ms with calibrated probabilities.
• LLM fallback – automatic escalation to OpenRouter models when Jev is unavailable.
• Multi‑label in one pass – yes/no per label, returning scores and ordered results.
• Rate limiting – per‑IP limits via Durable Objects (fast tier: 3k/min, 20k/day; smart tier: 200/min, 2k/day).
• Analytics & privacy – request metrics stored without raw text; caller and label hashes protect privacy.
• CLI package – dependency‑free Node binary that mirrors the HTTP API.
• Agent feedback – implements the feedback.now protocol for automated reporting.
• Deployable as a single Cloudflare Worker – no database, no framework, compiled with esbuild.

Use cases of Classifier Dev

• Spam detection for emails or chat messages.
• Sentiment or emotion analysis in real‑time pipelines.
• Content moderation where quick label decisions are needed.
• Bulk classification of newsletters, news headlines, or product reviews (up to 1k items per call).
• Agent‑driven automation that can invoke the /skill.md endpoint for discovery.

FAQ from the Classifier Dev

Q: Do I need an API key? A: No. The service is publicly accessible via HTTP.

Q: How many requests can I make? A: Rate limits are enforced per IP – 3,000 classifications/min (fast tier) or 200/min (smart tier).

Q: Can I classify multiple labels at once? A: Yes. Provide a comma‑separated label list; the API returns all labels with scores above a confidence threshold.

Q: What happens if the primary model is down? A: The fallback chain automatically switches to LLM providers and marks the response with a FALLBACK flag.

Q: How do I install the CLI? A: Run npm i -g classifier-dev (or npx classifier-dev classify … for a one‑off).

Q: Is any user data stored? A: Only hashed fingerprints for analytics; raw request text is never persisted.

Q: How can I receive a daily digest of analytics? A: Set the REPORT_TO secret to an email address; a 15‑minute cron sends a digest via Resend.

classifier.dev

Zero-shot text classification. Plain text in, a label and a calibrated confidence out. No key, no signup. Up to a thousand texts per request.

curl https://classifier.dev/spam,not+spam/Win+a+free+iPhone
spam

curl "https://classifier.dev/?labels=spam,not+spam&text=Win+a+free+iPhone"   # same call, query form
spam

Single Cloudflare Worker. No database, no framework, no build step beyond esbuild.

CLI

npm i -g classifier-dev
classify bug,feature,praise < feedback.txt

cli/ is a separate npm package (classifier-dev, bin classify): one dependency-free Node file, tests against a mock API (npm test), semver with its own CHANGELOG, released with npm run release patch|minor|major which tags cli-v<version> and lets .github/workflows/publish-cli.yml publish (needs an NPM_TOKEN repo secret). It talks to the API exactly like curl does.

Layout

src/index.ts    routing, validation, tiers, LLM fallback chain, analytics
src/query.ts    the GET query form, read and written with nuqs; the URL an error suggests
src/jev.ts      TypeSafe's Jev: packs inputs into requests, reads probabilities
src/limiter.ts  Durable Object: per-IP rate limiting
src/report.ts   digest — Analytics Engine SQL -> Resend, flags model fallbacks
src/alerts.ts   every 15 minutes; emails only when something is wrong
src/feedback.ts agent feedback, feedback.now protocol -> email
src/privacy.ts  keyed pseudonyms: nothing kept points back at a caller
src/cost.ts     per-request upstream spend, from the providers' own accounting
src/docs.ts     the site (GET / and GET /benchmark), plain text
src/home.ts     the same two documents rendered, for browsers only
src/ui.ts       the shared look: markdown in a terminal
cli/            the `classify` command, published to npm as classifier-dev
eval/           benchmarks; read eval/README.md before quoting a number
finish-dns.sh   one-shot DNS wiring, see below
wrangler.example.toml  the Worker config, minus the account-specific ids

The site

curl classifier.dev prints plain text, exactly as it always has. A browser sends Accept: text/html and gets the same document rendered — headings, bracketed links, copy buttons — from src/home.ts. Nothing is duplicated: the page is generated from DOCS and BENCHMARK at request time, so the text stays canonical and the two cannot drift. ?format=text opts out by hand, and both responses carry Vary: accept.

Agent feedback

Implements the feedback.now protocol (schema 1.1), so any agent that speaks it can report a problem without being told how:

GET  /.well-known/agent-feedback.json   what this host accepts
GET  /api/v1/policy                     categories, severities, limits
POST /api/v1/feedback                   full structured report
POST /api/v1/observations               lighter signal
POST /api/v1/feedback/{id}/attachments  more evidence, later
GET  /api/v1/receipts/{id}              did it land, and was it any good

Accepted submissions are emailed to REPORT_TO. Reports are kept in KV for 90 days. A repeat of the same domain + surface + category + title is stored and acknowledged as a duplicate but not emailed again, so one looping agent cannot empty itself into the inbox; the hourly budget is 100 per IP and the remainder comes back on every receipt.

quality_score is a deterministic function of how complete the report is — an agent can read the rule and write a better one next time. Nothing here calls the classifier or Analytics Engine: this is where reports arrive saying those are broken, so it must work when they do not.

Deploy

Merging to main deploys. .github/workflows/deploy.yml typechecks, runs the Worker and CLI tests, runs wrangler deploy, and then asks the live service for /v1/health and one classification, so a deploy that uploads a broken Worker fails in CI rather than in somebody's terminal.

By hand, to try something before it is merged:

cp wrangler.example.toml wrangler.toml     # once, then fill in your own ids
npx wrangler deploy

wrangler.toml is gitignored and holds the two values that are specific to one Cloudflare account: account_id, and the STATS KV namespace id that npx wrangler kv namespace create STATS hands back. The tracked wrangler.example.toml carries everything else — crons, bindings, migrations — so the deployment shape is in the repository and only the identifiers are not.

CI has no wrangler.toml, so .github/render-wrangler.mjs writes one from the example and three repository secrets. That makes the example the deployed shape rather than a copy of it: change a binding in wrangler.toml alone and CI keeps deploying the old one.

Repository secrets the deploy needs:

CLOUDFLARE_API_TOKEN    dash.cloudflare.com > My Profile > API Tokens >
                        Create Token > "Edit Cloudflare Workers"
CLOUDFLARE_ACCOUNT_ID   the account_id from wrangler.toml
STATS_KV_ID             the STATS namespace id from wrangler.toml
REPORT_TO               where the daily digest goes

Secrets set with wrangler secret put live on the Worker, not in the script bundle, so a deploy leaves them alone and CI never needs to know them.

Secrets the Worker reads: TYPESAFE_API_KEY, OPENROUTER_API_KEY, RESEND_API_KEY, CF_ANALYTICS_TOKEN, REPORT_KEY, PRIVACY_SALT. Add one with npx wrangler secret put NAME; none of them are ever read from the repository. src/index.ts lists the rest in the Env interface.

Secrets are compared with secretEquals (src/secrets.ts), never ===: a plain comparison returns on the first wrong byte and tells a caller how much of a guess was right.

The model

Both tiers answer from TypeSafe's Jev, a decision model rather than a language model: it takes a state and typed questions and returns a calibrated probability per option, in ~150ms. That shape is why the API can do three things the LLM version could not.

A thousand inputs per request. State is an array of {id, text} and each input gets its own question, so the whole batch is one upstream call. The documented limit is 64k tokens per request; jev.ts packs to a conservative budget and runs the resulting requests eight at a time. Measured: 400 news headlines classified in 650ms end to end, and packing 100 items scored the same as sending them one at a time.

Confidence that means something. On 400 six-way emotion items, answers at

= 0.9 confidence were right 82% of the time and answers below 0.5 were right 29%. The previous model's logprob "confidence" put 87% of news items above 0.9 and was right on 68% of those. So tier: "smart" now means: re-ask the single-label answers below 0.7 of a fast reasoning model and replace them, marked escalated: true. Nothing else changes. Which model matters: on exactly the items Jev is unsure about, deepseek-v4-flash, qwen3.7-flash and mercury-2.5 were no better than Jev; gemini-3.8-flash took news topics from 87.5% to 90.0% and emotion from 61.8% to 63.7%, so that is the chain. A frontier model (claude-fable-5.1) gets 72.3% / 90.7% at ~3x the price; the numbers are on /benchmark if that trade ever looks worth it.

Multi-label in one pass. One yes/no question per label, labels at >= 0.7 returned most-likely-first with the full score map. F1 0.887 on the seven-case set against 0.799 for the sweep-and-verify LLM cascade it replaced, in 230ms instead of 1.5s. Re-judging its candidates with the reasoning model made it worse (and took 23s), so multi-label ignores the tier.

The LLM chains in index.ts remain as the fallback when TypeSafe is unavailable, limited to twenty inputs because they are one call per input. The digest reports which model actually answered, with a FALLBACK marker, because the previous primary was delisted upstream and served its backup for weeks at F1 0.546 without anything saying so.

Updates list

The form at the foot of the home page posts to POST /subscribe, which appends the address to Postgres in its own Neon project (classifier-newsletter; the id is in .secrets.env, or from neonctl projects list). The table has four columns and no others: the address, where it came from, when it arrived, and a column for unsubscribing. There is deliberately no IP and no request id, so there is no column an address could be joined on. on conflict (email) do nothing makes a repeat submission a no-op, which also stops the response being used to test whether an address is already on the list.

npx wrangler secret put NEWSLETTER_DATABASE_URL

The Worker writes over Neon's HTTP SQL endpoint rather than a Postgres driver: a Worker has no TCP, and this is one parameterised INSERT.

Read the list:

neonctl connection-string --project-id "$NEWSLETTER_PROJECT_ID" \
  --role-name neondb_owner --database-name neondb \
  | xargs -I{} psql {} -c "select email, created_at from subscriber order by id"

The Worker connects as newsletter_writer, which is granted INSERT on that one table and nothing else. Do not read more into that than it deserves: Neon puts every role it creates into neon_superuser, which can read any table whatever the grants say, and neither the project owner nor ALTER ROLE can revoke that membership. So the connection string can in fact read the list. What actually keeps the addresses apart is the separate project and the absent columns, not the grant. To rotate the credential, delete and recreate the role (neonctl roles delete newsletter_writer, then create) and put the new string back with wrangler secret put.

The copy is one constant — ROADMAP in src/newsletter.ts. The plain text at curl classifier.dev, the form on the rendered page and index.md all read it, so a change to the roadmap changes all three or none.

Analytics

Every request writes one Analytics Engine datapoint (tier, label-set fingerprint, country, status, count, latency). No request text is ever stored.

Every request also records what it cost us: OpenRouter returns the charge for a call when asked, and Jev is billed on the input tokens it reports, at the rate eval/bench.py prices the benchmarks with. Spend accumulates in a per-request meter (src/cost.ts) and lands in double3. That column was added after launch, so it reads 0 for anything older than that deploy.

A cron at 15:00 UTC queries it and emails a digest via Resend.

Alerts

A separate cron runs every fifteen minutes and stays silent unless something fires. It only watches conditions with an action attached: the Jev key being refused, Jev not answering (the fallback chain serving quietly, which has happened), 5xx rates, smart-tier escalations failing (the shape an exhausted OPENROUTER_API_KEY takes), mean latency, a spend spike against the trailing day, and traffic stopping outright. 4xx is ignored — that is scanners probing for /wp-admin, not a fault.

Jev credits. TypeSafe publishes no balance endpoint — its API is /v1/systemone and /v1/models, nothing else — so there is no number to watch. Instead the check calls /v1/models with the key every fifteen minutes and reports back whatever TypeSafe says: a 401, 402 or 403 there means out of credit, revoked or wrong, and raises a critical alert quoting TypeSafe's own message rather than guessing which status means what. Because it probes rather than waiting for traffic, it fires on a quiet host before any caller meets the fallback chain, and it runs even when Analytics Engine is down.

Each condition emails once when it starts, again every six hours while it lasts, and once when it clears, with the state in KV under alert:. Thresholds are the T object at the top of src/alerts.ts.

curl -H "authorization: Bearer $REPORT_KEY" https://classifier.dev/alerts
curl -H "authorization: Bearer $REPORT_KEY" "https://classifier.dev/alerts?demo=1&send=1"

The first previews without sending or touching state; the second emails a sample through the real path, to prove delivery works.

Preview the digest any time without sending it:

curl -H "authorization: Bearer $REPORT_KEY" https://classifier.dev/report

The header is the only way in. A query string lands in access logs, in browser history and in the Referer header of whatever gets clicked next, so ?key= is gone. Append ?send=1 to actually email it.

Cloudflare's Analytics Engine SQL is a narrow ClickHouse subset — no uniq(), no SELECT DISTINCT, and a bare SELECT col ... GROUP BY col is rejected. Distinct counts therefore use SELECT col, count() ... GROUP BY col and count the returned rows. Each query is isolated so one failure cannot blank the report.

Privacy

Two columns in that dataset used to be the caller: the IP address, and the label set, joined and lowercased. Both are keyed hashes now (src/privacy.ts), so the figures still count distinct callers and distinct classifiers and nothing can be read back into an address or into somebody's wording. The caller hash takes the UTC day as well, so it stops being the same value tomorrow — which is why a unique-caller count over 7d or 30d is really caller-days.

Set the key once, and treat it as a secret like any other:

npx wrangler secret put PRIVACY_SALT     # 32 random bytes

Rotating it renumbers every fingerprint, so distinct counts double-count across the rotation. It falls back to ADMIN_SIGNING_KEY, then REPORT_KEY, then a per-isolate random value, because an unkeyed hash of an IPv4 address or of a common label set inverts in seconds.

Eval

npm run bench                 # multi-label, 7 cases: jev vs any OpenRouter model
npm run single -- --dataset emotion --backend jev
npm run single -- --dataset ag_news --backend openrouter:qwen/qwen3.7-flash
python3 eval/escalate.py --dataset emotion     # what the smart tier buys

single.py downloads AG News and dair-ai/emotion test rows on first use and caches raw results under eval/data/results/ so escalate.py can combine backends without re-spending. eval/README.md lists the caveats.

Rate limiting

Per IP in a Durable Object, counted in classifications: 3,000/min and 20,000/day on fast, 200/min and 2,000/day on smart.

Two other approaches were tried and rejected:

• Cloudflare's native ratelimit binding registers fine but never decremented (70 calls against a limit of 60 all returned success: true).
• KV is edge-cached and eventually consistent, so a counter written this second is invisible to the next read — every request saw remaining: 59.

A Durable Object is single-threaded and strongly consistent, which is what a counter needs. Verified at the original 60/min: 75 requests -> 60 × 200, 15 × 429.

DNS

The domain is registered at Porkbun; the Worker is on Cloudflare. Cloudflare Workers custom domains require the zone to live in Cloudflare, and neither API token here has zone.create, so that one step is manual:

1. https://dash.cloudflare.com -> Add a domain -> classifier.dev -> Free plan
2. ./finish-dns.sh — reads the assigned nameservers, points Porkbun at them via the Porkbun API, and attaches the Worker to the apex and www.

Agent skill

npx skills add https://classifier.dev

Served from this domain over RFC 8615 well-known discovery, so there is no repository in the middle:

src/SKILL.md                          the skill, bundled as a Text module
GET /skill.md                         the artifact
GET /.well-known/agent-skills/index.json   discovery, schema v0.2.0

The index must carry a sha256 of the artifact, and an index that disagrees with the file makes the skill uninstallable. Rather than commit a digest that a later edit would silently invalidate, src/skill.ts hashes the bytes it actually serves, once per isolate. Editing SKILL.md is therefore enough; nothing else needs updating.

The skill teaches the case the API pitch misses: you are already a model and can classify anything you can see, so the reason to call out is context, not capability — filtering forty search results down to six without reading forty.

Two things it documents because testing found them the hard way. Cloudflare 403s Python's stdlib urllib User-Agent before the request reaches the Worker, so the recipe sets one explicitly. And filters should be told "when in doubt, keep it": on a ten-snippet research filter that took signal kept from 4/6 to 6/6 with no extra noise, where adding a third "possibly relevant" label did nothing.

Discovery surfaces

GET /openapi.json               OpenAPI 3.1, also at /.well-known/openapi.json
GET /llms.txt                   short index for agents, linked from robots.txt
GET /benchmark                  measured accuracy, cost, latency

Both are linked from the third paragraph of GET / so an agent reading the landing page finds them immediately.

Known issue: Cloudflare's managed robots.txt

Adding the zone enabled Cloudflare AI Crawl Control, which prepends a managed block to /robots.txt disallowing GPTBot, ClaudeBot, CCBot, Google-Extended, Bytespider, Amazonbot and meta-externalagent. The Worker's own robots.txt is appended after it and cannot override it.

This blocks training crawlers, not runtime API consumers — any agent can still call the API. But it does keep the docs out of future model training data, which works against discovery. The toggles at dash.cloudflare.com -> classifier.dev -> AI Crawl Control -> Security did not persist when flipped, so this likely needs a plan-level change or support.

Operator agent access

For operator-owned bulk agent work, set a dedicated AGENT_API_KEY Worker secret and send it as Authorization: Bearer .... It uses the existing unmetered classification path without replacing ENTERPRISE_API_KEY. The CLI accepts it through CLASSIFY_API_KEY or CLASSIFIER_API_KEY. It does not authorize private reports or admin access. Keep it in an ignored secret file; never give it to public clients. Anonymous quotas continue to apply to unauthenticated traffic.