classifier-dev

# 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](https://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. Testimonials use `signal.category: "testimonial"`. They require `reporter.agent_type` and `reporter.agent_description`, so praise arrives with enough context to understand what kind of agent benefited and how it works. `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`, `AI_GATEWAY_API_KEY` (Vercel's AI Gateway, which serves Jev on a free monthly credit; when set it is asked first and TypeSafe catches what it refuses), `OPENROUTER_API_KEY`, `CONTEXT_API_KEY` (context.dev, the chat's web search and page reads), `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](https://docs.typesafe.ai), 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 and `POST /subscribe` send a confirmation email through Resend. The API returns `202 {"ok":true,"status":"pending_confirmation"}`. Nothing is written to the newsletter database until the emailed token is submitted to `POST /subscribe/confirm` as `{"token":"..."}` (or with the confirmation form). GET only renders the form, so mail scanners cannot confirm subscriptions. Tokens are signed with `NEWSLETTER_CONFIRMATION_SECRET`, expire within 24 hours, and are never returned from signup. Resend's idempotency key deduplicates repeat requests for the same inbox within each clock hour. Existing per-IP limits also apply. Signing-key rotation invalidates outstanding links. Apply `migrations/postgres/` with `npm run db:migrate` before deploying. The subscriber table shares the application database. Existing unconfirmed subscribers stay unconfirmed; do not backfill `confirmed_at` or send them updates until they confirm. An existing unsubscribe is never cleared by confirmation or by replaying an old token. Required Worker secrets: `DATABASE_URL`, `NEWSLETTER_RESEND_API_KEY`, and a random `NEWSLETTER_CONFIRMATION_SECRET` of at least 32 bytes. `NEWSLETTER_FROM` in `wrangler.example.toml` must use a verified Resend sending domain. `REPORT_TO` is the reply address and receives notifications only for newly confirmed rows. The subscriber table holds email, source, signup/confirmation/unsubscribe dates, which roadmap items were ticked (`wants text[]`, holding `ROADMAP` keys; the ticks ride in the confirmation token and are written on confirmation), the requested latency when faster inference is selected, and an internal id. It holds no IP, request id, or classification traffic. Pending signups are not stored. Tokens and mail-provider error bodies must not be logged. `migrations/postgres/0007_newsletter.sql` defines the table; a repeat confirmation replaces the ticks only when it ticked something, and never clears an unsubscribe or moves the first confirmation date. Read only confirmed, active recipients when sending updates: SELECT email, wants, desired_latency_ms FROM subscriber WHERE confirmed_at IS NOT NULL AND unsubscribed_at IS NULL; What people asked for first, to order the work by: SELECT unnest(wants) AS item, count(*) FROM subscriber WHERE unsubscribed_at IS NULL GROUP BY 1 ORDER BY 2 DESC; The Worker uses the application `DATABASE_URL` for newsletter and account data. Subscriber rows have no account foreign key or API traffic identifier; sharing storage does not subscribe account holders or change existing consent. The old newsletter project is retained as a read-only migration archive. See `docs/postgres-setup.md` for the verified cutover and recovery procedure. 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. ## Jev long context Default Jev and explicit `model: "jev"` inputs over 32,000 characters use Chonkie RecursiveChunker with 600-token `cl100k_base` chunks, parallel Jev screening for relevant or uncertain evidence (including opposing evidence and exceptions), then final Jev over whole eligible chunks in source order. The final evidence budget is 20,000 `cl100k_base` tokens plus a conservative provider context check. Eligible chunks can be omitted when that budget fills; `usage.long_context` reports selection counts, tokens, calls and timing. This does not guarantee full-document final reading or universal accuracy. Use Fast with a workspace key backed by paid balance or an active paid subscription; anonymous access and signup credit do not qualify. Synchronous limits: 250,000 original context tokens summed across inputs, 20 documents, 32 decisions, and a 1 MB body. Retail is $0.084/M original `cl100k_base` context tokens, counted once across inputs regardless of dimensions and actual inference usage. Each continuous whitespace or non-whitespace run is limited to 8,192 UTF-16 code units. Longer runs return `400 long_context_input` before document tokenization or chunking, bounding tokenizer work on pathological inputs. No eligible evidence returns `422 long_context_no_evidence` without charge. Explicit `model: "chunklaya"` keeps its separate legacy opt-in behavior. Send one whole document of up to 10 million input tokens (100 MB) to `POST /v1/classify` with a funded workspace key. Use the normal JSON input and labels, or send `text/plain` with a `labels` query parameter. Large uploads return `202` with a `status_url`; `Prefer: respond-async` also works for smaller documents. The server splits, screens, retries and finishes automatically. The repository CLI handles upload and polling with `node cli/classify.js a,b --document document.txt --json`. The exact whole-document token count sets the reservation and $0.084/M charge. Source text is stored privately while queued and deleted as it is screened. Final Jev reads at most 20,000 selected evidence tokens. Completion, failure, cancellation and 24-hour expiry delete remaining source and evidence; failed or canceled work is refunded. Results remain available until expiry. ## Analytics Every request writes one Analytics Engine datapoint (tier, label-set fingerprint, country, status, count, latency). No request text is written to analytics. Long-context aggregates also go to `classifier_long_context_events`, with context/document totals, chunk screening/selection/omission counts, phase input tokens (including unknown-count flags), answered-call counts and phase timings. Account events append the corresponding fields; their ordered column contract is in [`src/server/analytics/schema.ts`](src/server/analytics/schema.ts). Documents and original context tokens count once; chunk and phase counters accumulate across dimension passes. No document text or caller identifiers are written to the dedicated dataset. 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 Jev provider attempts are stored separately in `classifier_jev_attempts` through `JEV_AE`, including recovered failures, retries and gateway cooldown skips. The report includes status, reason, count and mean latency for each provider; `/alerts` shows current incidents even when their notification is suppressed. The existing 15-minute alert check warns when at least three attempts fail and failures exceed 5% for either provider. Counts account for Analytics Engine sampling. No input text, caller identifiers or upstream messages are stored. Successful simple and multi-label classifier names are retained for 90 days in a separate aggregate KV record with no caller or source-text fields. Its keyed fingerprint also appears in per-request analytics, allowing operators to resolve label names in those pseudonymous records. `AI_GATEWAY_DISABLED = "true"` in `wrangler.example.toml` keeps production on TypeSafe directly after the gateway repeatedly returned 429 on September 19. The gateway key is retained. To restore gateway-first routing, verify gateway capacity, change this variable to `"false"` in the example and local config, and deploy. Check provider attempts and the live API tests after re-enabling. 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 ...`. Classification quotas are unmetered, but inference uses a separate $2/day operator allowance (`OPERATOR_DAILY_USD`), shared across IPs, with the same request and concurrency limits as free usage. It neither consumes nor expands the anonymous allowance and does not replace `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. For a funded workspace key with an arranged bulk quota, set the `API_KEY_RATE_LIMIT_MULTIPLIERS` Worker secret to a JSON object mapping the SHA-256 hex digest of each key to an integer multiplier (1–1,000). Overrides only raise the admission quota for that key; account counters, balance charging, model capacity limits, and other keys remain unchanged. Remove the entry to restore the plan quota. ## Finding the classification code The domain terms are defined in [CONTEXT.md](CONTEXT.md). | Module | Responsibility | | --- | --- | | `src/index.ts` | HTTP validation, quota, model selection, smart escalation, fallback, and response formatting. | | `src/jev.ts` | Jev questions and answers, shared batch budgets and recovery, gateway/direct transport, and provider validation. | | `src/dimensions.ts` | Dimension definitions and the mapping from input–dimension decisions to Jev questions and back. | Both ordinary and dimension classification use the same Jev batching module. A question group is the smallest part of a request that recovery keeps together: one input's questions for ordinary classification, one decision for dimensions. Batch preparation accounts for shared input text once, checks both provider budgets, and runs before quota charging for dimensions. Execution limits concurrent requests and splits an oversized batch between question groups. Change provider budgets and recovery in `src/jev.ts`; keep dimension meaning in `src/dimensions.ts`. Test the posted requests and returned classifications through `jevClassify` and the Worker HTTP interface, without depending on packing internals. ## Multiple dimensions `POST /v1/classify` also accepts `items` and `dimensions`: ```json { "items": ["Checkout charges me twice"], "dimensions": { "team": ["billing", "identity", "platform"], "urgency": { "labels": ["immediate", "normal", "low"], "instructions": "Active financial harm is immediate." }, "kind": ["bug", "request", "question"] } } ``` Each `results[i].dimensions[name]` carries its own label, confidence, scores, model and latency. Jev shares state across item–dimension questions, packing both context limits. Smart escalation is per field and clears the original scores; unavailable Jev falls back only up to 20 decisions. The API accepts up to 20 dimensions and 1,000 decisions, with each decision charged to quota. The `classify_dimensions` MCP tool uses this same path. Analytics adds `blob9` (single/multi/dimensions), `double6` (successful input items), `double7` (dimension count), `double8` (uncertain or unscored fields), and `double9` (fields served by fallback). `double1` counts successful decisions. Dimension configurations are keyed fingerprints, never stored verbatim. The admin panel queries dimension traffic separately, including failures and caller-days. The existing alert cron flags three or more dimension 5xx in an hour above 10% of dimension requests, plus any dimension fallback usage. Run `npm run test:e2e` with Node 22.18+ against the real production API, or set `CLASSIFIER_BASE_URL` to a staging origin. These are named TypeScript tests using `node:test` and real HTTP/model calls, with no fetch or inference mocks. Set `CLASSIFIER_API_KEY` to an authorized key when the shared IP quota is used up; it is optional. The suite uses inference and counts toward normal quotas. The tests check exact decisions and ordering for a 300-decision batch, every field's allowed labels and probability distribution, real smart escalation, provider score preservation, aliases, validation errors, MCP, OpenAPI, and legacy calls. Responses start as `unknown` and are validated before becoming typed matrices. Smart tests require an actual escalation; model drift that makes every fixture confident fails the test instead of silently skipping the reasoning provider. `npm run typecheck` checks the Worker and the TypeScript live tests. Deployment CI runs the live suite after publishing; `npm test` stays offline. Fault injection remains in `test/dimensions.test.ts` and `test/dimensions-observability.test.ts`. Run the **live API tests** workflow manually from GitHub Actions (or `gh workflow run e2e.yml`) to check production without deploying a Worker.