# awesome-jev

> A catalogue of public resources for Jev, TypeSafe AI's System One decision model, indexed by decision pattern. It contains <!--n:entries-->1207<!--/n--> entries, every row attributable to a named source. In all, <!--n:link_ok-->1204<!--/n--> have a dated 2xx response record; the other <!--n:link_unstamped-->3<!--/n--> have no dated success record. The newest successful check is <!--n:last_sweep-->2026-09-24<!--/n-->, and dates vary by row. These counts do not describe runtime tests or current availability.

Jev (https://docs.typesafe.ai/) returns typed decisions rather than text: a `choice` among up to 255 options, a `score` on an ordered 2-to-10 level scale, or a `noul` yes-no probability. This repository is an index of how people use it. It is not the product, not an SDK, and not affiliated with TypeSafe AI.

## Machine-readable data

Catalog (<!--n:entries-->1207<!--/n--> entries): https://raw.githubusercontent.com/kydlikebtc/awesome-jev/main/catalog.json
Retired entries (dead links): https://raw.githubusercontent.com/kydlikebtc/awesome-jev/main/retired.json
JSON Schema for one entry: https://raw.githubusercontent.com/kydlikebtc/awesome-jev/main/schema/entry.schema.json

Each entry carries the fields defined in the JSON Schema: <!--n:fields-->slug, title, summary, summary_zh, zh_machine, url, kind, patterns, question_types, languages, platforms, author, repo, stars, repo_license, package, evidence, evidence_none, official, has_code, published, first_seen, checked, link_status, flags, notes, notes_zh, sources, license<!--/n-->. `summary` is English and `summary_zh` Chinese; `evidence` names the file a claim was read in, and `evidence_none` says why no file can be cited.

## Two axes

`kind` is the form of the resource: <!--n:kinds-->official-docs, integration, project, plugin, sdk, snippet, tutorial, article, video, case-study, benchmark, discussion, alternative<!--/n-->.

`patterns` is what decision the example makes, and is the primary index: <!--n:pattern_keys-->tool-selection, intent-routing, context-compaction, safety-gating, output-validation, retry-control, human-escalation, model-routing, fan-out, search-ranking, data-extraction, classification, feature-extraction, document-triage, support-triage, content-scoring, recommendation, overview<!--/n-->.

## Human pages

English index: https://github.com/kydlikebtc/awesome-jev/blob/main/README.md
Chinese index: https://github.com/kydlikebtc/awesome-jev/blob/main/README.zh-CN.md
Decision patterns, with when NOT to use each: https://github.com/kydlikebtc/awesome-jev/blob/main/docs/patterns.md
Every example of one decision, on its own page: https://github.com/kydlikebtc/awesome-jev/blob/main/docs/by-pattern/<pattern>.md (for example safety-gating.md; append .zh-CN before .md for Chinese). The README shows the first ten of each.
Cross-platform compatibility table: https://github.com/kydlikebtc/awesome-jev/blob/main/docs/compatibility.md
What to check before trusting an entry: https://github.com/kydlikebtc/awesome-jev/blob/main/docs/vetting.md
How the catalog was built: https://github.com/kydlikebtc/awesome-jev/blob/main/docs/method.md
Sources and licences: https://github.com/kydlikebtc/awesome-jev/blob/main/docs/sources.md
Runnable examples: https://github.com/kydlikebtc/awesome-jev/blob/main/examples/README.md

## For agents

MCP server: in Claude Code, `/plugin marketplace add kydlikebtc/awesome-jev` then `/plugin install awesome-jev@awesome-jev` (installs this and the agent skill together); elsewhere, `pip install awesome-jev-mcp` and run `awesome-jev-mcp`. Source at https://github.com/kydlikebtc/awesome-jev/tree/main/src/awesome_jev_mcp — query the catalogue by decision pattern, language and primitive rather than reading it. Tools: search_examples, get_example, list_patterns, compatibility, check_model_string. Every result carries the row's caveat flags; rows that do not call Jev at all are excluded by default.

Agent skill: https://github.com/kydlikebtc/awesome-jev/blob/main/skills/awesome-jev/SKILL.md — the facts generated Jev code most often gets wrong, and the design rules worth following.

Decision taxonomy: https://raw.githubusercontent.com/kydlikebtc/awesome-jev/main/patterns.json
Kind and flag labels (EN/中文): https://raw.githubusercontent.com/kydlikebtc/awesome-jev/main/taxonomy.json
Platform matrix: https://raw.githubusercontent.com/kydlikebtc/awesome-jev/main/compat.json

## What is verified

Link records: `link_status` and `checked` together record a dated HTTP response. A success establishes availability at that time, not code execution or access to protected page content. Repository stars and licences are metadata snapshots.

Human review: `evidence.path` cites the file reviewed, and `evidence.read_on` records the reported reading date. Reading code is separate from running it. Summaries include source-project descriptions and machine translations; `zh_machine` marks the latter. See `docs/method.md` for the collection method and limits.

Call-site text records: in all, <!--n:evidence_rows-->1121<!--/n--> rows cite a file and matching strings. A weekly job fetches the default-branch file and reports missing strings or files. This is a count of stored citations, not the latest CI passes. Inspect the workflow report and resulting issues for current results. A string match does not prove a call executes or the API is compatible. The narrower claim about which primitives are used is made by <!--n:primitive_rows-->100<!--/n--> rows, of which <!--n:primitive_rows_cited-->72<!--/n--> cite a file. Other primitive claims carry `evidence_none` explaining why no readable repository file is cited.

A row without a dated link-success record may have a host that refuses scripted requests. A 403 from Forbes or Medium is not evidence that a page is gone; such rows are left unstamped instead of inventing a successful check.

Runtime and performance: treat all catalogue code as untested by this repository, including entries without `code-untested`. Linked benchmarks are their authors' measurements and have not been independently reproduced here. Repository build checks and package smoke tests do not test catalogued integrations or the live Jev API. Inclusion is not a security review, maintenance guarantee or recommendation.

## Caveats for anyone summarising this data

1. **Identify who measured each result.** Rows flagged `vendor-reported` repeat the vendor's own benchmarks. Entries with `kind: benchmark` can contain independent reports, but the label alone is not a methodological endorsement or a reproduction by this catalogue. Attribute results to their authors, preserve limitations and distinguish vendor measurements from independent ones.

2. **`kind: alternative` rows are NOT Jev.** They are independent reimplementations with a compatible wire format. Several are among the most-starred repositories mentioning Jev, and are frequently miscatalogued elsewhere as Jev usage examples. A compatible API does not imply compatible calibration, so thresholds do not transfer.

3. **Jev cannot be run locally.** No weights are published. Any content titled "run Jev locally" is describing a substitute; one such article is catalogued with the `not-jev` flag specifically as a correction.

4. **jevai.org is not official.** It is an unaffiliated community site running a separate API with a different endpoint and request shape. Its `/jev-api` page documents a shape matching no primary source.

5. **The yes-no primitive is named `noul`, not "binary" or "boolean".** Vercel's AI SDK evaluation API spells it `boolean` and returns `.probability`; every other surface uses `noul` and `.noul`. `noul` answers carry no confidence field at all, unlike `choice` and `score`.

6. **There is no portable model string.** `jev-latest` on the native API, `typesafe-ai/jev` on Vercel, `typesafe/jev` on Cloudflare, `typesafe/jev-1.13` on OpenRouter. `typesafe/jev-1` does not exist anywhere and is a common fabrication.

7. **Star counts are a popularity signal, not a quality verdict.** Many entries were created within days of the model's launch; some carry `single-commit`, `no-license`, `archived` or `shadow-mode-only` flags precisely because popularity and substance diverge sharply here.

8. **English is the model's primary training language.** The official docs state CJK scripts are handled but not equally well. Test on your own content before relying on it for non-English workloads.