kydlikebtc/awesome-jev

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_{Counts describe saved link and evidence records, not current CI passes or runtime tests. About these counts}

• Jev is a decision model from TypeSafe AI. It does not write text — you hand it state plus typed questions and it returns typed answers with calibrated confidence, fast and cheap enough to sit in an agent's inner loop.
• This repo indexes public examples of using it, organised by the decision being made. The resource you read this week is disposable; the decision pattern is not.
• How to assess it: every row names its source. Call-site citations, primitive claims and caveats are recorded where available, so you can inspect what was read and what remains untested.

Three primitives. Every pattern below is built out of them, and the asymmetry in the last row is the single most common source of bugs.

Under each primitive, two counts that are never added together: rows whose question_types records a person reading the code call it, and rows where only a text signal shows it — the weekly refresh found its request or answer shape in the one file the row cites (primitives_seen), which does not show that the code calls it. See what is verified.

Input is text only — string, JSON object, or array of text. Context is 64k tokens per request, 32k for the state plus the longest question. Output tokens are free. There are no published weights, so it cannot be run locally. Full cross-platform differences: docs/compatibility.md.

Which primitive fits a decision? The figure below arranges TypeSafe's own guidance as a decision list: read down and stop at the first yes. The order and the wording are this repository's; every step cites the page it follows, listed under the figure. It is the vendor's design guidance with its sources, not a recommendation of any row here.

Beside each primitive, the rows filed under the pattern named there, counted as the figure above counts them: read by a person, or text signal only. The steps live in picker.json, which the site's primitives view reads too. Sources, numbered as in the figure: [1] docs.typesafe.ai/model-jaggedness/jev-1.13#generation · [2] docs.typesafe.ai/concepts/how-to-build-with-system-one#use-code-when-you-can · [3] docs.typesafe.ai/primitives#split-a-complex-judgment-into-several-questions · [4] docs.typesafe.ai/primitives/noul#good-practice-ask-more-than-one-question-per-call · [5] docs.typesafe.ai/model-jaggedness/jev-1.13#common-sense-structural-invariants · [6] docs.typesafe.ai/primitives#choose-a-question-type · [7] docs.typesafe.ai/primitives/score#writing-good-levels · [8] docs.typesafe.ai/primitives/noul#writing-a-noul-question · [9] docs.typesafe.ai/primitives#ask-for-one-snap-judgment-per-question.

Six things in reading order. Hand-picked, because "most starred" is not the same as "read this first".

1. Quickstart

   The canonical first call: one support ticket, one Choice, one Score and one Noul in a single request, in Python, JS and cURL.

2. Jev 1.13 known limitations

   The most useful page in the docs and the least linked. It explains, among other things, that a Choice over options and one Noul per option answer different questions.

3. Example: three primitives in one request

   Written from the official API reference and checked field by field against it, but not executed against the live API.

4. fast-jev-compaction

   Exactly two nouls per tool call: does knowing this call happened still matter, and is the full output still needed verbatim. Despite the word "scored" in its own description, no score primitive is used.

5. ai-cookbook: Jev track

   The best structured tutorial found. It states plainly that typed output does not guarantee a correct decision, lists the documented weaknesses, and qualifies its own cost illustration rather than selling it.

6. Hermes Agent: Jev compaction evaluation

   The single most credible row in this catalog. Recall came out below their existing summariser, and at a matched context budget it tied plain recency ordering. Cost was genuinely far lower. Publishing a negative result on a hyped model is rare.

Every decision pattern, sized by how many entries this catalogue contains. Use the pattern index below the chart to jump to a section. A zero is a research gap, not a rendering bug.

All 18 patterns have at least one catalogue entry. Coverage does not imply runtime testing or equal maturity. See docs/status.md.

Pattern index · jump to the examples

Independent measurement reports in the catalogue, including negative results that help explain where an approach fails. These are the original authors' measurements; this repository has not independently reproduced them. Check each report's dataset, method and model version before comparing results.

Author's conclusion is the direction a benchmark's own author states for Jev on the task they measured (measurement.direction: favourable, mixed, unfavourable or inconclusive), indexed from the author's report: author-stated, not reproduced here, and absent where the author states none in words. docs/benchmarks.md sets every benchmark's measurement side by side.

Rows whose own author measured Jev for the use and concluded against it: a benchmark whose measurement's direction is unfavourable, or another row flagged measured, not adopted. Author-stated, not reproduced here. Read them before the positive examples; the site lists them.

• Hermes Agent: Jev compaction evaluation
  Ported the Jev compaction approach, measured it against their shipping summariser, and published the conclusion not to adopt it.
  _{Benchmark · ★100k+ · Py · noul · call site, read 2026-09-22 · author's conclusion: unfavourable (author-stated, not reproduced here)}

  The single most credible row in this catalog. Recall came out below their existing summariser, and at a matched context budget it tied plain recency ordering. Cost was genuinely far lower. Publishing a negative result on a hyped model is rare.

• worldmonitor: news threat classification
  Two Choice questions over threat level and category, held in shadow mode after a blind evaluation found Jev merely tied the incumbent model.
  _{Benchmark · ★10k+ · TS · choice · call site, read 2026-09-22 · author's conclusion: unfavourable (author-stated, not reproduced here)}

  Caveats: shadow mode

  Wired in but deliberately inert: by their own statement nothing Jev returns reaches a label, a cache row or an alert. Ships a golden fixture. A model to copy for how to trial a new model without betting production on it.

• no-mistakes: Jev review pre-brief, measured and retired
  One Score per candidate file to pre-brief code review — measured twice, then removed: more billed input for essentially no wall-clock gain, and offline replay showed the candidate list could not reach where review findings land.
  _{Benchmark · ★1k+ · Go · score · author's conclusion: unfavourable (author-stated, not reproduced here)}

  Removed in PR #1165 (2026-09-22). Their offline measurement found the candidate generator excluded changed files by construction while nearly all review findings sit in changed files, and that per-file excerpts made the list less precise at higher token cost. The code is gone from the default branch, so this row cites the change that removed it.

• hermes-jev-skills
  Nine agent skills plus a CLI covering model routing, memory filtering, turn retention, one-of-many skill selection and next-action choice.
  _{Plugin · ★100+ · Py · choice · score · noul · call site, read 2026-09-22}

  Caveats: measured, not adopted

  Notable for publishing a use it dropped: Jev-summarised handoffs had worse recall than raw transcripts.

• jev-skill-router
  Claude Code plugin: asks TypeSafe Jev which installed skill fits each prompt and logs the answer (shadow-first). A working reference for the skill-suggestion cookbook on Claude Code — the README records why it is unlikely to help a strong model as a router. _{(upstream description)
  Plugin · shimo4228 · Py · call site, read 2026-09-22}

  Caveats: measured, not adopted

  Its author ran it on 2026-09-21 and concluded that, as a router, it is unlikely to help a strong model, which already sees every skill's description. Its README: 6 of 6 scripted requests handled sensibly on 0.2.0, 3 of 6 real-session prompts wrong on 0.1.0, which the author calls an anecdote, not a rate. It stays in shadow mode. https://dev.to/shimo4228/i-added-jevs-skill-router-to-claude-code-and-turned-back-just-before-rewriting-the-skill-listing-34in A model wrote this row's Chinese note.

• An early-access test of TypeSafe's Jev: calibrated judgments for half a cent
  The best independent test found: 24 Norwegian documents on one pinned model version, opening with a case the model got wrong while correctly reporting low confidence.
  _{Benchmark · Lindfors}

  Methodology is stated cleanly and scoped honestly as a single-day snapshot. Leading with a failure case is what makes it a real calibration test rather than a testimonial.

• Testing TypeSafe Jev, Mistral and Gemini for local event validation
  The only three-way head-to-head found, with each model's prompt tuned separately and the scope limited to one task rather than a general ranking.
  _{Benchmark · Near Here}

  Self-limits correctly: a use-case study, not a model leaderboard. That restraint is rarer than the numbers.

• hippo-memory
  Biologically-inspired memory for AI agents. Decay, retrieval strengthening, consolidation. Zero runtime deps, SQLite, MCP. Benchmarked retrieval with an opt-in TypeSafe Jev reranker.
  _{Benchmark · ★100+ · kitfunso · TS · call site, read 2026-09-22 · author's conclusion: mixed (author-stated, not reproduced here)}

• jev-arena
  An introduction to Jev with hands-on tests: Choice, Score and Noul turn natural language into typed judgements for classification, scoring and routing, compared with DeepSeek on comment labelling, speed and results, with CSV import, replay and offline reports.
  _{Benchmark · ★100+ · nanmicoder · JS · call site, read 2026-09-24}

• jevbench
  JevBench v1 - a benchmark for Jev-class typed decision models: smart, cheap, fast, reliable, open. _{(upstream description)
  Benchmark · ★100+ · fstandhartinger · Py · call site, read 2026-09-22}

10 of 73 shown: the negative results first, then the picks of the curated independent reports path, in its order, then the first of the others in list order · all 73 on one page, with every note → · filter on the site

The primary index. Each heading is a decision an agent has to make; the rows are examples of making it. Caveats appear as short tags — the full note for each row is in catalog.json and on the site.

★ gives a repository's GitHub stars as a band — ★10+, ★100+, ★1k+, ★10k+ and ★100k+; rows with no repository or under 10 stars show no band. Rows run official first, then with code, then by band, then by title. A band is a popularity signal, not a quality verdict; the exact count, as last read from GitHub, is in catalog.json and on the site.

A call site link opens the one file a row cites (evidence.path) at HEAD of the repository's default branch; the date after it is the day a person last read that file (evidence.read_on): a reading, not a run of the code. A cited file link is the same for a file that shows the project speaking Jev's request shape rather than building on Jev, or only an example it ships (evidence.kind). Neither is pinned to a commit, so it opens the file as it is now, which may differ from what was read, and stops resolving once the file moves; the weekly claims check reports that.

Which tool or action the agent should call next.

• Cookbook: Function calling ⭐
  Maps natural-language trading requests onto ordinary typed functions by turning function names and closed-set arguments into confidence-aware questions.
  _{Official docs · Py · choice}

• Cookbook: Skill suggestion ⭐
  Picks at most one skill out of 182 for an agent turn: one request ranks every skill and asks whether the turn needs one at all, a second reads the top three.
  _{Official docs · Py · choice · noul}

• Demo: Smart home assistant ⭐
  Runnable demo code for a smart home assistant that evaluates user requests with typed decisions.
  _{Official docs · Py}

• ai-hedge-fund
  An AI Hedge Fund Team _{(upstream description)
  Integration · ★10k+ · virattt · Py · call site, read 2026-09-24}

• claude-code-templates: three Jev plugins
  Three independently installable Claude Code plugins — guardrails, model router and skill suggestion — each with its own hooks and tests.
  _{Plugin · ★10k+ · Py · TS · choice · score · noul · call site, read 2026-09-22}

• Composio TypeSafe provider
  Compiles a tool catalogue into questions and reconstructs tool calls from the answers, with typed errors for abstention and confirmation-required cases.
  _{Project · ★10k+ · Py · choice · call site, read 2026-09-22}

• Cua driver: jev-use example
  Computer-use action selection in Python and TypeScript: Jev picks the next browser action from an immutable candidate set, with reobserve and abstain as reserved options.
  _{Project · ★10k+ · Py · TS · choice · call site, read 2026-09-22}

• FastMCP jev_search transform
  Two-stage MCP tool search: a wide Choice coarse-ranks the whole catalogue, then a shortlist gets full descriptions plus one Noul each to decide whether it does the job at all.
  _{Project · ★10k+ · Py · choice · noul · call site, read 2026-09-22}

• jev-ultrafast
  A high-speed browser agent from Browser Use: Jev decides the operation and which element to act on, and a small LLM is called only when text must be typed.
  _{Project · ★10k+ · Browser Use · Py · choice · call site, read 2026-09-22}

  Caveats: vendor numbers

• json-render
  Vercel Labs' generative UI framework. In its Jev experiment the model does not write JSON token by token — it only picks components, props and layout.
  _{Project · ★10k+ · Vercel Labs · TS · choice · call site, read 2026-09-22}

10 of 230 shown · all 230 on one page → · filter on the site

_{↑ Pattern index}

Classify what the user wants and send the request down the right branch.

• Demo: Smart home assistant ⭐
  Runnable demo code for a smart home assistant that evaluates user requests with typed decisions.
  _{Official docs · Py}

• Pattern: Confidence-gated routing ⭐
  Treat confidence as a second axis: the answer tells you what, the confidence tells you whether to act on it.
  _{Official docs · Py}

• Pattern: Intent routing ⭐
  Classify an incoming request and route it to the cheapest adequate handler: deterministic code, a specialist LLM, or a person.
  _{Official docs · Py · choice}

• AutoGPT TypeSafe blocks
  Seven production blocks — choice, score, yes/no, ask-many, route, pick-best, filter — with a UTF-8 byte budget, verbatim wire capture and eleven test files.
  _{Project · ★100k+ · Py · choice · score · noul · call site, read 2026-09-22}

• Airflow LLMBranchOperator with Jev
  Turns downstream task ids into a choice option set, with a minimum-confidence gate that routes uncertain runs to a human.
  _{Integration · ★10k+ · Py · choice}

• Inbox Zero: seven email decisions
  Seven distinct email decisions, each with its own separately chosen threshold, falling back to the normal LLM on any error.
  _{Project · ★10k+ · TS · choice · noul · call site, read 2026-09-22}

• ai-cookbook: Jev track
  A graded course from a first call through each primitive, state shapes and criteria, to ticket triage and a multi-step workflow, mirroring all four official patterns.
  _{Tutorial · ★1k+ · Py · choice · score · noul · call site, read 2026-09-22}

• jev-chat-jarvis
  An Android reply co-pilot that judges intent, timing and risk from on-screen text, while separate models handle OCR and drafting.
  _{Project · ★1k+ · Java · choice · score · noul · call site, read 2026-09-22}

• Real Python: hello-jev
  A teaching example with a deliberate control group: the same station-enquiry task written in plain Python that only accepts Y/N, next to a Noul that reads intent.
  _{Tutorial · ★1k+ · Real Python · Py · noul · call site, read 2026-09-22}

• foreman
  A software-factory foreman that uses Jev to decide what an agent pipeline should do next.
  _{Project · ★100+ · thruwire · Py · call site, read 2026-09-22}

10 of 35 shown · all 35 on one page → · filter on the site

_{↑ Pattern index}

Decide which tool calls and results still matter so stale context can be dropped.

• Hermes Agent: Jev compaction evaluation
  Ported the Jev compaction approach, measured it against their shipping summariser, and published the conclusion not to adopt it.
  _{Benchmark · ★100k+ · Py · noul · call site, read 2026-09-22 · author's conclusion: unfavourable (author-stated, not reproduced here)}

• jcode: memory recall without embeddings
  Replaces the whole retrieval stack for memory recall — no embeddings, no BM25, no reranker — with one batched Noul per candidate memory.
  _{Project · ★10k+ · Rs · noul · call site, read 2026-09-22}

• fast-jev-compaction
  A Claude Code plugin that replaces the compaction summary with per-item decisions: stale tool calls are dropped or truncated, everything kept stays verbatim.
  _{Plugin · ★1k+ · tamaratran · TS · noul · call site, read 2026-09-22}

• compact-adviser
  "Work appears completed or recorded. Run /compact to save tokens." _{(upstream description)
  Project · ★100+ · kunchenguid · TS · call site, read 2026-09-22}

• hermes-jev-skills
  Nine agent skills plus a CLI covering model routing, memory filtering, turn retention, one-of-many skill selection and next-action choice.
  _{Plugin · ★100+ · Py · choice · score · noul · call site, read 2026-09-22}

  Caveats: measured, not adopted

• jev-pruner
  Trims long shell output before the model sees it, asking one Noul per chunk.
  _{Plugin · ★100+ · tamaratran · TS · noul · call site, read 2026-09-22}

• Winnow
  Context garbage collection for Claude Code: when Read, Bash or Grep dump a wall of output, each chunk is judged for relevance to the current task.
  _{Plugin · ★100+ · Py · noul · call site, read 2026-09-22}

• claude-jev
  Claude Code plugin: Jev for rule checks, verbatim compaction, and prompt routing _{(upstream description)
  Plugin · ★10+ · 0x7067 · Py · call site, read 2026-09-22}

• dsh-jev-tools
  Jev judgment, not generation: prune long tool output, screen fetched pages for injected instructions, and gate completion claims inside DeepSeek Harness. _{(upstream description)
  Plugin · ★10+ · horusjiang · TS · call site, read 2026-09-24}

• fast-dev-compaction
  Codex plugin: verbatim Jev-guided context restoration around session compaction. Port of tamaratran/fast-jev-compaction to Codex lifecycle hooks. _{(upstream description)
  Plugin · ★10+ · leonaaardob · TS · call site, read 2026-09-24}

10 of 34 shown · all 34 on one page → · filter on the site

_{↑ Pattern index}

Decide whether an action is safe to run. Defence in depth, never a security boundary.

• Cookbook: Classifying RAG passages ⭐
  Scores each retrieved passage, then decides in code which reach the answering model — keeping contradictory ones flagged and dropping ones carrying prompt injection.
  _{Official docs · Py}

• Cookbook: Guardrails for LLMs ⭐
  Screens every message in and out of an LLM app in one request, naming hazards and scoring how much harm complying would do.
  _{Official docs · Py · noul · score}

• @langchain/typesafe
  The JavaScript counterpart of the LangChain integration, with the same classifier and middleware shapes.
  _{Integration · ★10k+ · TS · choice · score · noul · call site, read 2026-09-22}

• claude-code-templates: three Jev plugins
  Three independently installable Claude Code plugins — guardrails, model router and skill suggestion — each with its own hooks and tests.
  _{Plugin · ★10k+ · Py · TS · choice · score · noul · call site, read 2026-09-22}

• sub2api: Jev as a moderation endpoint
  Drops in as a moderation API by asking many parallel Noul questions in one request, one per hazard category, with an anti-injection prefix on every instruction.
  _{Project · ★10k+ · Go · noul · call site, read 2026-09-22}

• agentgateway: CI-validated LLM guardrail
  Three Score questions on a shared severity scale, blocking the request when two or more cross the line, and failing closed.
  _{Project · ★1k+ · Rs · score · call site, read 2026-09-22}

• DeepChat: agent tool-permission review
  Reviews each tool call on three axes — risk level, whether the user authorised it, and an explicit prompt-injection pressure check.
  _{Project · ★1k+ · TS · choice · noul · call site, read 2026-09-22}

• atomic
  The verifiable coding agent runtime. Define your coding agent's process in natural language with stages, checks, and approval gates instead of hoping it follows your instructions.
  _{Project · ★100+ · bastani-inc · TS · call site, read 2026-09-24}

• Jev-cu
  A computer-use agent that asks which accessibility-tree element to act on, plus a separate noul for whether the action needs explicit user confirmation.
  _{Project · ★100+ · JS · choice · noul · call site, read 2026-09-22}

• jev-drone
  Camera-only simulated drone where Jev makes tactical judgements at a low rate while stabilisation and safety reflexes stay in ordinary fast code.
  _{Project · ★100+ · Py · choice · score · noul · call site, read 2026-09-22}

  Caveats: unverified claims

10 of 139 shown · all 139 on one page → · filter on the site

_{↑ Pattern index}

Check a model's output against a rubric before it reaches a user.

• Cookbook: Double-checking citations ⭐
  Catches wrong or invented citations against the source document with one Choice, using its confidence to flag borderline cases for review.
  _{Official docs · Py · choice}

• Cookbook: Guardrails for LLMs ⭐
  Screens every message in and out of an LLM app in one request, naming hazards and scoring how much harm complying would do.
  _{Official docs · Py · noul · score}

• latitude-llm
  Open-source observability for AI agents. Find where your agents fail, dispatch your coding agent to fix it, and verify the fix against real traces. _{(upstream description)
  Project · ★1k+ · latitude-dev · TS · call site, read 2026-09-22}

• reticle
  AI agents can generate code, but still struggle to understand what they build. Reticle brings Jev-style machine-native runtime perception to web & desktop applications. _{(upstream description)
  Project · ★1k+ · reticlehq · TS · call site, read 2026-09-24}

• abide
  Make your coding agent abide by all your project rules _{(upstream description)
  Plugin · ★100+ · coldteadotai · TS · call site, read 2026-09-24}

• atomic
  The verifiable coding agent runtime. Define your coding agent's process in natural language with stages, checks, and approval gates instead of hoping it follows your instructions.
  _{Project · ★100+ · bastani-inc · TS · call site, read 2026-09-24}

• Canny
  Guards against a coding agent claiming it finished: reads tool output, the diff and test results, then judges whether the completion claim holds.
  _{Project · ★100+ · TS · noul · score · call site, read 2026-09-22}

• fastbrowse
  A fast browser agent: Jev picks each action from what is on the page, an LLM reads and plans, and every claim in an answer cites a quote from the page. _{(upstream description)
  Project · ★100+ · agent-labs-dev · Py · call site, read 2026-09-22}

• formanator
  Submit Forma https://joinforma.com benefit claims from the command line and Model Context Protocol (MCP) clients, with support for AI-powered receipt analysis with an LLM or Jev _{(upstream description)
  Plugin · ★100+ · timrogers · Rs · call site, read 2026-09-22}

• jev-eval-agent
  An agent that routes evaluation work through typed decisions.
  _{Project · ★100+ · vinilana · TS · call site, read 2026-09-22}

  Caveats: no licence

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_{↑ Pattern index}

Decide whether a failed step is worth retrying.

• jev-harness
  Zero-dependency System One decision harness: 5 semantic gates saving frontier AI agent tokens on trivial errors & doom loops. Python + TypeScript + Rust. MCP-compatible. _{(upstream description)
  Plugin · ★10+ · ismaelsoilet · Py · call site, read 2026-09-24}

• harnessjudge
  Judge agent steps — ok / retry / escalate / stop via TypeSafe Jev _{(upstream description)
  Project · ndolinschi · TS · call site, read 2026-09-22}

  Caveats: one commit · no licence

• Jev by Example
  Ten runnable JavaScript agent decisions, one file each: reconciling a new memory against a stored one, gating whether an HTTP 200 really satisfied the task, retry vs. reconcile after an uncertain write, scoring context against a budget, checking a handoff for dropped prohibitions.
  _{Project · Really Artificial · JS · choice · score · noul · call site, read 2026-09-22}

  Caveats: AI-written

• jev-reasoning-navigator
  JEV Reasoning Navigator: Cognitive supervision, loop prevention, and anti-hallucination engine for autonomous LLM agents using TypeSafe AI
  _{Project · andreuvm · Py · call site, read 2026-09-24}

  Caveats: no licence

• jev-resilience
  Non-blocking Spring Boot Starter for Spring WebFlux that implements a Semantic Circuit Breaker to detect silent HTTP 200 failures using TypeSafe Jev. _{(upstream description)
  Plugin · vicente-md · Java · call site, read 2026-09-22}

  Caveats: no licence

• jevswiftsdk
  An independent, type-safe Swift SDK for TypeSafe Jev, with async/await, batching, retries, and SPM support. _{(upstream description)
  SDK · nsstudent · Swift · call site, read 2026-09-22}

• XavierJev
  A local decision layer in Jev's shape: yes/no, choice and rubric questions read off one token's logprobs from a local model, with a Claude Code permission gate measured on held-out command sets.
  _{Jev-like alternative · Xinyu Liu · TS · noul · choice · score · cited file, read 2026-09-25}

  Caveats: not Jev itself · AI-written

All 7 shown · on its own page · filter on the site

_{↑ Pattern index}

Use calibrated confidence to decide what a person must see.

• Cookbook: Classification using confidence ⭐
  Classifies annual reports into 75 industry groups, then reads the answer's own confidence to decide whether to report that group or the broader division above it.
  _{Official docs · Py · choice}

• Cookbook: Double-checking citations ⭐
  Catches wrong or invented citations against the source document with one Choice, using its confidence to flag borderline cases for review.
  _{Official docs · Py · choice}

• Cookbook: Knowledge graph entity alignment ⭐
  Decides which of 450 candidate pairs from two product catalogues describe the same thing, with one Score whose three levels are the three available actions.
  _{Official docs · Py · score}

• Cookbook: Self-consistency with choices ⭐
  Adds an explicit "uncertain" outcome to moderation decisions and measures label agreement against the share of actions taken automatically.
  _{Official docs · Py · choice}

• Cookbook: Self-consistency with nouls ⭐
  Routes uncertain probabilities to human review while keeping the underlying noul values visible rather than collapsing them to a label.
  _{Official docs · Py · noul}

• Pattern: Confidence-gated routing ⭐
  Treat confidence as a second axis: the answer tells you what, the confidence tells you whether to act on it.
  _{Official docs · Py}

• Confidence ⭐
  How confidence is derived from the probability distribution, and why a threshold tuned on one question type does not transfer to another.
  _{Official docs}

• Airflow LLMBranchOperator with Jev
  Turns downstream task ids into a choice option set, with a minimum-confidence gate that routes uncertain runs to a human.
  _{Integration · ★10k+ · Py · choice}

• Composio TypeSafe provider
  Compiles a tool catalogue into questions and reconstructs tool calls from the answers, with typed errors for abstention and confirmation-required cases.
  _{Project · ★10k+ · Py · choice · call site, read 2026-09-22}

• Inbox Zero: seven email decisions
  Seven distinct email decisions, each with its own separately chosen threshold, falling back to the normal LLM on any error.
  _{Project · ★10k+ · TS · choice · noul · call site, read 2026-09-22}

10 of 69 shown · all 69 on one page → · filter on the site

_{↑ Pattern index}

Pick which downstream model or tier should handle a request.

• Cookbook: Structured data extraction cascade ⭐
  A two-stage mini-then-verify-then-reasoning cascade that reaches most of a big reasoning model's quality at a fraction of the cost.
  _{Official docs · Py}

• Pattern: Intent routing ⭐
  Classify an incoming request and route it to the cheapest adequate handler: deterministic code, a specialist LLM, or a person.
  _{Official docs · Py · choice}

• @langchain/typesafe
  The JavaScript counterpart of the LangChain integration, with the same classifier and middleware shapes.
  _{Integration · ★10k+ · TS · choice · score · noul · call site, read 2026-09-22}

• claude-code-templates: three Jev plugins
  Three independently installable Claude Code plugins — guardrails, model router and skill suggestion — each with its own hooks and tests.
  _{Plugin · ★10k+ · Py · TS · choice · score · noul · call site, read 2026-09-22}

• Astra-Ares
  Adaptive reasoning effort for GPT-6 during Codex tasks, powered by Jev to reduce token usage. _{(upstream description)
  Plugin · ★100+ · miuuyy · JS · call site, read 2026-09-24}

• hermes-jev-skills
  Nine agent skills plus a CLI covering model routing, memory filtering, turn retention, one-of-many skill selection and next-action choice.
  _{Plugin · ★100+ · Py · choice · score · noul · call site, read 2026-09-22}

  Caveats: measured, not adopted

• jev-codex-router
  Judges how hard a coding turn is, then picks the model tier, reasoning depth and speed mode to match.
  _{Plugin · ★100+ · JS · choice · score · call site, read 2026-09-22}

  Caveats: archived

• jev-eval-agent
  An agent that routes evaluation work through typed decisions.
  _{Project · ★100+ · vinilana · TS · call site, read 2026-09-22}

  Caveats: no licence

• jev-review
  Pre-screens code review with Jev to surface high-risk changes for a more expensive model or a person, with a local dashboard.
  _{Project · ★100+ · TS · choice · score · noul · call site, read 2026-09-22}

• jevrouter
  A router for models, tools and subagents.
  _{Project · ★100+ · billionsbobby · TS · call site, read 2026-09-22}

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_{↑ Pattern index}

Pack many questions — including speculative ones — into one request and let code pick what mattered.

• Cookbook: Parallel questions ⭐
  A 13-question regulatory briefing over one long article, showing that batching every question into one call is far cheaper and faster with no change in answers.
  _{Official docs · Py}

• Pattern: Speculative fan-out ⭐
  Pack many questions, including ones you may not need, into a single request and let your code decide afterwards what was relevant.
  _{Official docs · Py}

• Quickstart ⭐
  The canonical first call: one support ticket, one Choice, one Score and one Noul in a single request, in Python, JS and cURL.
  _{Official docs · Py · TS · sh · choice · score · noul}

• AutoGPT TypeSafe blocks
  Seven production blocks — choice, score, yes/no, ask-many, route, pick-best, filter — with a UTF-8 byte budget, verbatim wire capture and eleven test files.
  _{Project · ★100k+ · Py · choice · score · noul · call site, read 2026-09-22}

• jev-ultrafast
  A high-speed browser agent from Browser Use: Jev decides the operation and which element to act on, and a small LLM is called only when text must be typed.
  _{Project · ★10k+ · Browser Use · Py · choice · call site, read 2026-09-22}

  Caveats: vendor numbers

• sub2api: Jev as a moderation endpoint
  Drops in as a moderation API by asking many parallel Noul questions in one request, one per hazard category, with an anti-injection prefix on every instruction.
  _{Project · ★10k+ · Go · noul · call site, read 2026-09-22}

• ai-cookbook: Jev track
  A graded course from a first call through each primitive, state shapes and criteria, to ticket triage and a multi-step workflow, mirroring all four official patterns.
  _{Tutorial · ★1k+ · Py · choice · score · noul · call site, read 2026-09-22}

• jev-chat: a tool-calling chatbot with no LLM
  A chat bot that does tool calling with no language model anywhere: one request asks the request kind, the tool, and every tool's arguments at once.
  _{Project · ★100+ · TS · choice · noul · call site, read 2026-09-22}

• jev-forge
  An open training and inference stack for Jev-style decision models. Train models to score dynamic candidate branches from a shared prefix, with support for high-cardinality choice, calibration, and fast batched inference. _{(upstream description)
  Jev-like alternative · ★10+ · zwlijay · Py · cited file, read 2026-09-22}

  Caveats: not Jev itself · one commit

• jev-sift
  Classify first. Read selectively. A portable agent plugin and MCP tool for batch text classification. _{(upstream description)
  Plugin · ★10+ · kbhuw · JS · call site, read 2026-09-22}

  Caveats: no licence

10 of 32 shown · all 32 on one page → · filter on the site

_{↑ Pattern index}

Score or re-rank candidates from a cheaper retrieval step.

• Cookbook: Classifying RAG passages ⭐
  Scores each retrieved passage, then decides in code which reach the answering model — keeping contradictory ones flagged and dropping ones carrying prompt injection.
  _{Official docs · Py}

• Cookbook: Line-by-line search ⭐
  Semantic search over a terms-of-service document: one request scores 218 line ids with a Choice, and a Noul checks whether the document answers at all.
  _{Official docs · Py · choice · noul}

• Cookbook: Re-ranking ⭐
  Re-ranks 30-passage BM25 shortlists for 40 legal queries with one question per query-candidate pair, reporting large top-1 and top-10 gains.
  _{Official docs · Py}

• AutoGPT TypeSafe blocks
  Seven production blocks — choice, score, yes/no, ask-many, route, pick-best, filter — with a UTF-8 byte budget, verbatim wire capture and eleven test files.
  _{Project · ★100k+ · Py · choice · score · noul · call site, read 2026-09-22}

• FastMCP jev_search transform
  Two-stage MCP tool search: a wide Choice coarse-ranks the whole catalogue, then a shortlist gets full descriptions plus one Noul each to decide whether it does the job at all.
  _{Project · ★10k+ · Py · choice · noul · call site, read 2026-09-22}

• jcode: memory recall without embeddings
  Replaces the whole retrieval stack for memory recall — no embeddings, no BM25, no reranker — with one batched Noul per candidate memory.
  _{Project · ★10k+ · Rs · noul · call site, read 2026-09-22}

• LanceDB TypeSafeReranker
  A vector-database reranker that asks one Noul per result and uses the yes-probability as an absolute relevance score, comparable across queries.
  _{Project · ★10k+ · Py · noul · call site, read 2026-09-22}

• OpenViking: retrieval reranking
  One Noul per candidate document in a single batched request, with the yes-probability used directly as the relevance score.
  _{Project · ★10k+ · Py · noul · call site, read 2026-09-22}

• jev-chat-jarvis
  An Android reply co-pilot that judges intent, timing and risk from on-screen text, while separate models handle OCR and drafting.
  _{Project · ★1k+ · Java · choice · score · noul · call site, read 2026-09-22}

• no-mistakes: Jev review pre-brief, measured and retired
  One Score per candidate file to pre-brief code review — measured twice, then removed: more billed input for essentially no wall-clock gain, and offline replay showed the candidate list could not reach where review findings land.
  _{Benchmark · ★1k+ · Go · score · author's conclusion: unfavourable (author-stated, not reproduced here)}

10 of 64 shown · all 64 on one page → · filter on the site

_{↑ Pattern index}

Pull typed fields out of messy text by choosing among candidates rather than generating them.

• Cookbook: Date extraction ⭐
  Extracts absolute and relative dates by asking for the parts a document names, then resolving and validating them in code with confidence-based review.
  _{Official docs · Py}

• Cookbook: Pre-parsed value extraction ⭐
  Regexes find candidate emails, phone numbers and amounts; the model selects the requested span so code can normalise a verbatim value.
  _{Official docs · Py · choice}

• Cookbook: Structure recovery ⭐
  Reconstructs Markdown from plain text that lost its formatting, in two requests: one restitches hard-wrapped lines, one classifies every block.
  _{Official docs · Py}

• Cookbook: Structured data extraction cascade ⭐
  A two-stage mini-then-verify-then-reasoning cascade that reaches most of a big reasoning model's quality at a fraction of the cost.
  _{Official docs · Py}

• jev-macos-loop
  Open-source macOS AI computer use and native GUI automation on Apple silicon. Jev + OmniParser CoreML + Apple Vision OCR. Bring your own OpenRouter, Vercel AI Gateway, or TypesafeAI token. _{(upstream description)
  Project · ★10+ · jcpsimmons · JS · call site, read 2026-09-22}

• jev-reviewer
  Data extraction for systematic reviews, quoted from the papers. Ask a trial report and its supplements your extraction form or a RoB 2, ROBINS-I, QUADAS-2 or TIDieR template; Jev points at the lines, every answer is a verbatim quote with its page, you check it and export the table. Files stay i
  _{Project · ★10+ · choxos · JS · call site, read 2026-09-22}

• jevfill
  A Chrome extension that fills web forms from unstructured notes with Jev: paste your details once as plain text, with no structured profile, then fill forms on demand.
  _{Plugin · ★10+ · imohitmayank · TS · call site, read 2026-09-24}

• smart-paste
  Fills form fields from pasted text: the form's heading, labels and your text go to TypeSafe, and it inserts the values it matches for you to review before submitting.
  _{Plugin · ★10+ · nomanjack · JS · call site, read 2026-09-24}

• ask-jev
  Ultra-fast, fail-open advisory decisions and verbatim extractive reading view for AI coding agents and CLI pipelines _{(upstream description)
  Project · logicrw · Py · call site, read 2026-09-24}

• jev-data-questions
  Bring a dataset and see the right chart: the UI inspects the CSV's shape and proposes insights, and Jev fills in the values.
  _{Project · narulaskaran · TS · call site, read 2026-09-24}

  Caveats: no licence

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_{↑ Pattern index}

Put an item into a taxonomy, including deep hierarchies walked with probabilities.

• Cookbook: Classification using confidence ⭐
  Classifies annual reports into 75 industry groups, then reads the answer's own confidence to decide whether to report that group or the broader division above it.
  _{Official docs · Py · choice}

• Cookbook: Hierarchical classification ⭐
  Walks deep patent, retail, biomedical and source-code taxonomies with a parallel beam search over Choice probabilities.
  _{Official docs · Py · choice}

• Cookbook: Knowledge graph entity alignment ⭐
  Decides which of 450 candidate pairs from two product catalogues describe the same thing, with one Score whose three levels are the three available actions.
  _{Official docs · Py · score}

• Cookbook: Structure recovery ⭐
  Reconstructs Markdown from plain text that lost its formatting, in two requests: one restitches hard-wrapped lines, one classifies every block.
  _{Official docs · Py}

• Inbox Zero: seven email decisions
  Seven distinct email decisions, each with its own separately chosen threshold, falling back to the normal LLM on any error.
  _{Project · ★10k+ · TS · choice · noul · call site, read 2026-09-22}

• json-render
  Vercel Labs' generative UI framework. In its Jev experiment the model does not write JSON token by token — it only picks components, props and layout.
  _{Project · ★10k+ · Vercel Labs · TS · choice · call site, read 2026-09-22}

• worldmonitor: news threat classification
  Two Choice questions over threat level and category, held in shadow mode after a blind evaluation found Jev merely tied the incumbent model.
  _{Benchmark · ★10k+ · TS · choice · call site, read 2026-09-22 · author's conclusion: unfavourable (author-stated, not reproduced here)}

  Caveats: shadow mode

• 332_lab-jev-chat
  A Windows WeChat assistant: Jev judges the intent of each incoming message and DeepSeek suggests replies.
  _{Project · ★100+ · liyucheng1997 · Kt · call site, read 2026-09-24}

• classifier-dev
  Zero-shot text classification over plain HTTP — no API key, no account. One Cloudflare Worker, a CLI, and an MCP server. https://classifier.dev _{(upstream description)
  Plugin · ★100+ · mrmps · TS · call site, read 2026-09-22}

• docjev
  A very fast document classifier/splitter using Jev _{(upstream description)
  Project · ★100+ · jerryjliu · Py · call site, read 2026-09-22}

10 of 120 shown · all 120 on one page → · filter on the site

_{↑ Pattern index}

Turn free text into numeric features for a classical downstream model.

• Cookbook: Autoresearch feature discovery ⭐
  An autoresearch loop that proposes questions, turns free text into numeric features, and uses model error to improve a supervised gradient-boosting regressor.
  _{Official docs · Py}

• nimble
  Local typed decisions, contrastive data curation, and model evaluation. _{(upstream description)
  Project · ★1k+ · bespokelabsai · Py · call site, read 2026-09-22}

  Caveats: no licence

• jev-align
  Builds calibrated decision functions from human feedback.
  _{Project · ★100+ · sutro-sh · Py · call site, read 2026-09-22}

• Prism
  Does not place orders. It judges market conditions such as toxic flow and mean reversion, and hands the assessment to the existing strategy.
  _{Project · ★100+ · TS · choice · score · call site, read 2026-09-22}

• jev-curate
  Curates training data: JSONL and Parquet rows are judged on quality, relevance and risk before deciding what reaches downstream training.
  _{Project · ★10+ · Rs · score · noul · call site, read 2026-09-22}

• jev-board-lab
  Interactive explorer and Jev question workspace for Jev Board datasets. _{(upstream description)
  Project · webgrga · JS · call site, read 2026-09-22}

  Caveats: no licence

• jev-calibrated-narrative-coding
  Calibrated conversion of police crash narratives into probabilistic crash variables with a System One model. Pipeline, schema and aggregated results. _{(upstream description)
  Project · pozapas · Py · call site, read 2026-09-24}

• tiershift
  Shift every LLM call to the cheapest model that can handle it. Routing decided by TypeSafe Jev in ~180 ms. No training data. Policy in plain YAML. TypeScript and Python. _{(upstream description)
  Project · iamvatsalpatel · TS · call site, read 2026-09-22}

All 8 shown · on its own page · filter on the site

_{↑ Pattern index}

Classify and route incoming documents, invoices and forms.

• docjev
  A very fast document classifier/splitter using Jev _{(upstream description)
  Project · ★100+ · jerryjliu · Py · call site, read 2026-09-22}

• formanator
  Submit Forma https://joinforma.com benefit claims from the command line and Model Context Protocol (MCP) clients, with support for AI-powered receipt analysis with an LLM or Jev _{(upstream description)
  Plugin · ★100+ · timrogers · Rs · call site, read 2026-09-22}

• tax-doc-classifier
  Tax document page classifier built on Jev decisions. 100% strict accuracy across 261 IRS forms, ~$0.001 per page. _{(upstream description)
  Project · ★100+ · kyotofin · TS · call site, read 2026-09-22}

• doc-router
  A Document OCR Router to help route pages based on content. _{(upstream description)
  Project · ★10+ · misbahsy · Rs · call site, read 2026-09-22}

• jev-capability-atlas
  Independent, evidence-based map of when TypeSafe's Jev actually holds up vs. breaks down — real API-call receipts, not a leaderboard. 中文為主的雙語 repo。 _{(upstream description)
  Benchmark · ★10+ · zaious · Py · call site, read 2026-09-22 · author's conclusion: mixed (author-stated, not reproduced here)}

• jevmory
  Coding-agent memory where every fact is a verbatim quote graded by TypeSafe Jev's calibrated confidence. Local-first, SQLite receipts, zero dependencies. _{(upstream description)
  Project · ★10+ · romiluz13 · Py · call site, read 2026-09-22}

• pdf-race
  Docling → Jev vs Docling → Gemini 3.8 Flash vs Gemini reading the PDF: same documents, one clock, scored against arXiv's own metadata _{(upstream description)
  Benchmark · ★10+ · goodrahstar · JS · call site, read 2026-09-24}

• decision-first
  Agent skill that spots bounded-judgment steps, tries a typed decision model (TypeSafe's Jev) first, and documents every attempt _{(upstream description)
  Plugin · harrymunro · Py · call site, read 2026-09-22}

• jev-boe-demo
  Daily demo applying TypeSafe's Jev model to Spain's official gazette (BOE). _{(upstream description)
  Project · tatuck · TS · call site, read 2026-09-24}

  Caveats: no licence

• jev-builder
  A browser form for building requests to TypeSafe's Jev: pick a template, fill in the blanks, copy the request. No JSON, no install, runs locally. _{(upstream description)
  Project · collapseindex · JS · call site, read 2026-09-22}

10 of 20 shown · all 20 on one page → · filter on the site

_{↑ Pattern index}

Route support tickets and conversations by intent and urgency.

• Quickstart ⭐
  The canonical first call: one support ticket, one Choice, one Score and one Noul in a single request, in Python, JS and cURL.
  _{Official docs · Py · TS · sh · choice · score · noul}

• ai-cookbook: Jev track
  A graded course from a first call through each primitive, state shapes and criteria, to ticket triage and a multi-step workflow, mirroring all four official patterns.
  _{Tutorial · ★1k+ · Py · choice · score · noul · call site, read 2026-09-22}

• spring-ai-typesafe
  A community Spring AI starter bringing typed decisions to Java, with a builder API over the three question types.
  _{Integration · ★10+ · Java · choice · score · noul · call site, read 2026-09-22}

• Example: three primitives in one request
  A minimal first call asking a choice, a score and a noul together, annotated with the asymmetries that catch people out.
  _{Snippet · Py · choice · score · noul · call site, read 2026-09-22}

  Caveats: code untested

• Jev AI Use Cases
  Walks through use case after use case — agent routing, an in-agent decision layer, ticket triage — each with a concrete option set and a sample response.
  _{Tutorial · Mehul Gupta · Py · choice}

  Caveats: paywall

• Jev on AI/ML API
  Another gateway route, notable because its endpoint path and request envelope differ again from both the native API and Cloudflare's.
  _{Integration · Py · noul · choice · score}

• Jev on Cloudflare Workers AI
  Workers AI binding and REST samples asking a noul, a choice and a score in one call, with the full response including per-answer confidence.
  _{Integration · TS · sh · noul · choice · score}

• jev-triage
  Message triage CLI in Go, built on the Jev decision model from TypeSafe AI. Categorizes messages, scores urgency, and flags low-confidence ones for human review. _{(upstream description)
  Project · boldbug1 · Go · call site, read 2026-09-24}

All 8 shown · on its own page · filter on the site

_{↑ Pattern index}

Score quality, risk or relevance on an ordered scale.

• Cookbook: Self-consistency with choices ⭐
  Adds an explicit "uncertain" outcome to moderation decisions and measures label agreement against the share of actions taken automatically.
  _{Official docs · Py · choice}

• Pattern: Composite scoring ⭐
  Break one broad judgement into atomic scores and combine them with weights that live in your code, not in the prompt.
  _{Official docs · Py · score}

• AutoGPT TypeSafe blocks
  Seven production blocks — choice, score, yes/no, ask-many, route, pick-best, filter — with a UTF-8 byte budget, verbatim wire capture and eleven test files.
  _{Project · ★100k+ · Py · choice · score · noul · call site, read 2026-09-22}

• worldmonitor: news threat classification
  Two Choice questions over threat level and category, held in shadow mode after a blind evaluation found Jev merely tied the incumbent model.
  _{Benchmark · ★10k+ · TS · choice · call site, read 2026-09-22 · author's conclusion: unfavourable (author-stated, not reproduced here)}

  Caveats: shadow mode

• ai-cookbook: Jev track
  A graded course from a first call through each primitive, state shapes and criteria, to ticket triage and a multi-step workflow, mirroring all four official patterns.
  _{Tutorial · ★1k+ · Py · choice · score · noul · call site, read 2026-09-22}

• gptcache
  Semantic cache for LLMs. Fully integrated with LangChain and llama_index. _{(upstream description)
  Project · ★1k+ · zilliztech · Py · call site, read 2026-09-22}

• jev-chat-jarvis
  An Android reply co-pilot that judges intent, timing and risk from on-screen text, while separate models handle OCR and drafting.
  _{Project · ★1k+ · Java · choice · score · noul · call site, read 2026-09-22}

• jev-lint
  lint text in code by jev scorerer _{(upstream description)
  Project · ★100+ · mizchi · TS · call site, read 2026-09-22}

• jev-review
  A local-first MCP plugin for continuous code-quality review by coding agents.
  _{Plugin · ★100+ · niazmorshed2007 · TS · call site, read 2026-09-22}

• jev-review
  Pre-screens code review with Jev to surface high-risk changes for a more expensive model or a person, with a local dashboard.
  _{Project · ★100+ · TS · choice · score · noul · call site, read 2026-09-22}

10 of 165 shown · all 165 on one page → · filter on the site

_{↑ Pattern index}

Choose what to surface next, fast enough for a live conversation.

• Jevflix
  Jev picks, you watch. A hybrid movie recommender: fast semantic + keyword search narrows 4,800 films to a shortlist, then TypeSafe Jev reads your constraints and picks the one film that fits - with a confidence score that decides whether to answer instantly or ask a follow-up. _{(upstream description)
  Project · arielbubis · Py · call site, read 2026-09-24}

All 1 shown · on its own page · filter on the site

_{↑ Pattern index}

Surveys the model or the space rather than one pattern.

• Official agent skill for Claude Code ⭐
  Installs a TypeSafe skill into Claude Code so an agent can write correct Jev calls without you pasting the API shape each time.
  _{Official docs · ★1k+ · sh}

• @typesafe-ai/sdk (TypeScript / JavaScript) ⭐
  The official TypeScript client. Ships ESM, CJS and type declarations, with lowercase choice()/score()/noul() helper factories.
  _{SDK · ★100+ · TS · JS · choice · score · noul · call site, read 2026-09-22}

• system-one-adapter-python ⭐
  A drop-in TypeSafeClient replacement backed by ordinary LLM APIs, so you can run Jev-shaped code without Jev access.
  _{SDK · ★100+ · Py · cited file}

• typesafe-sdk (Python) ⭐
  The official Python client. Sync and async clients, retry policy with retry-after support, and Choice/Score/Noul helper classes.
  _{SDK · ★100+ · Py · choice · score · noul · call site, read 2026-09-22}

• API reference ⭐
  The one endpoint, POST /v1/systemone, with the exact request and answer shapes for all three question types.
  _{Official docs · sh · Py · TS}

• Models, pricing and limits ⭐
  The authoritative sheet: jev-1.13.0, $0.042 per Mtok input with output free, 64k context, 32k for state plus the longest question, text input only.
  _{Official docs · sh · Py · TS}

• Primitives: Choice, Score, Noul ⭐
  What each primitive is for and how to write criteria, including the 255-option cap on Choice and the 2-10 level range on Score.
  _{Official docs · Py · TS · choice · score · noul}

• Introducing System One models and Jev ⭐
  The launch post: what a System One model is, why decisions were split from generation, and the vendor's latency and cost claims.
  _{Article · Diogo Almeida}

  Caveats: vendor numbers

• Jev 1.13 known limitations ⭐
  The vendor's own list of where the model fails: literal reading, arithmetic and counting, date comparison, indirection, large noisy states, adversarial content.
  _{Official docs}

• Use case map ⭐
  The vendor's own taxonomy: five headline categories, nineteen industry groups, and ten decision shapes from classification through to structured data extraction.
  _{Official docs}

10 of 451 shown · all 451 on one page → · filter on the site

252 of these rows are projects or plugins with code. overview is also where the keyword rules put a description they could not place, and nobody has recorded reading these rows against the patterns (patterns_reviewed), so they are listed apart: at the end of the Overview page, on the site, and in the review queue with the rules' suggestion.

_{↑ Pattern index}

The same rows grouped by what you will find when you open the link.

The parts that are not the catalog.

skills/awesome-jev/ complements TypeSafe's own agent skill, typesafe-ai/skills (its row), rather than replacing it: for the API contract and for designing questions, that skill and the live docs it reads are the reference; this one adds what the public ecosystem shows — worked examples and their caveats, platform differences, and independent and negative results.

• Link checks — 1207 rows carry an HTTP 2xx response and a checked date; 3 carry no dated success record. Dates vary by row and a past success does not guarantee availability today. Stars and licences are repository metadata snapshots.

• Source and code review — evidence.path cites the file read, evidence.read_on records the reported review date, and evidence_none explains missing file evidence. Reading a call site is separate from running it. Summaries include source descriptions and machine translations; see the method and its limits.

• Whose words the summaries are — 882 summaries are the linked project's own GitHub description, word for word, and are marked (upstream description); 9 are marked (earlier upstream description): taken from one that no longer reads the same. Those words are their authors'. 2 summaries are marked as written for this catalogue, and 317 carry no record either way. The weekly refresh compares each summary with its repository's description and labels a match; only a person marks a summary as written here.

• Who wrote the Chinese — 195 of 1210 rows have a Chinese summary a person wrote; a model translated the other 1015, and each of those carries zh_machine and is marked (机翻) in the Chinese README, on the Chinese pattern pages and in the site's Chinese view. The translation queue lists machine translations for a person to replace: every one on the most-starred rows, then, most-starred first, others flagged by at least one of three text signals a script computes (much shorter than the English, a number from the English missing, mostly ASCII). A signal is a comparison, not a verdict on a translation, and no row in the READMEs, the pattern pages or the site shows one. To take some, see Claim a translation; only a translation of your own takes zh_machine off.

• Call-site text checks — 1076 rows record in evidence a file where the project calls Jev, and strings matched in it. Another 56 record a file that shows a project speaking Jev's request shape rather than building on Jev (every alternative, whether it serves that shape or sends Jev the same request to compare, and adapters backed by other models), and 0 only an example the project ships; evidence.kind says which. The weekly claims job checks that those strings remain on the default branch and reports missing text or files. These counts measure recorded evidence, not latest CI passes. A text match does not prove that a call executes, the API is compatible, or the result is correct. Citations a script marks for a person to re-read are listed in the review queue.

• Which primitives — 104 rows name in question_types the primitives a person read the code calling. Apart from those, 684 rows carry primitives_seen, a machine text signal: the weekly refresh found a primitive's request or answer shape ("type": "choice", Noul(, .noul) in the one file the row cites. A shape in a file is not a call, and 632 of those rows carry no question_types, so the signal is all that is recorded about their primitives. No filter, count or rule here reads the signal as a primitive claim.

• Runtime and performance not independently tested here — treat every catalogue entry as untested by this repository, including entries without code-untested. Linked benchmarks describe their authors' measurements; this catalogue has not reproduced them. Repository build checks and package smoke tests do not exercise those integrations or the live Jev API, and inclusion is not a security review.

Links that stopped resolving, kept so a dead reference stays searchable instead of vanishing.

One entry per example, validated against a JSON Schema on every push.

Corrections take priority over additions — a wrong row costs more than a missing one. See CONTRIBUTING.md; the bar is could a reader act on this row without opening the link?

Code in scripts/, site/ and examples/ is MIT. Catalog metadata is CC0-1.0, with a per-row license field. Linked works keep their own licences — repo_license records what each declares. Summaries marked (upstream description) or (earlier upstream description) are the linked projects' own words, which that dedication does not cover (sources and licences).

Maintenance checks: · Scheduled link checks · Call-site text checks

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