Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add anton-abyzov/specweave --skill jevgit clone --depth 1 https://github.com/anton-abyzov/specweaveWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/anton-abyzov/specweave/jev)<a href="https://agentmods.dev/skills/anton-abyzov/specweave/jev"><img src="https://agentmods.dev/badge/skills/anton-abyzov/specweave/jev/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/anton-abyzov/specweave/jev"><img src="https://agentmods.dev/badge/skills/anton-abyzov/specweave/jev.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00038 | $0.02565 |
| Opus 5.5 | $0.00015 | $0.01026 |
| Sonnet 5.5 | $0.00008 | $0.00513 |
| Haiku 4.5 | $0.00004 | $0.00257 |
Grade C, and why
jev scanned grade C with 2 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 14d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
`specweave jev guard "rm -rf ./build"` exits 0 allow, 2 warn, 3 deny, 4 unavailable. Call it yourself before Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
not found → **manual path** (the same POST by hand; curl and PowerShell forms below). How it starts
The opening of the file, as written. The whole thing — 149 lines — stays where its author put it; the contents beside it link to each section on GitHub.
sw-jev — bounded decisions
These selection models evaluate state against questions whose answers are enumerated in advance and return
probabilities. Measure latency, cost and error rates on your own fixtures before enabling routing. It cannot answer
outside your schema and never writes code, prose or explanations. Run specweave --version: exit 0 → CLI path;
not found → manual path (the same POST by hand; curl and PowerShell forms below).
The one test
Can every possible answer be written down before the call? Yes → Jev. No → keep it in the frontier model. That
is the whole rule. Three primitives: choice picks one of 2–255 named options and returns probabilities plus a
confidence; noul returns the probability a statement is true (0–1), for yes/no gates; score places the state on
2–10 described, ordered levels, for graded severity or risk.
What leaves the machine
Every call sends its state — prompt text, task titles and acceptance criteria, the shell command, test-output
tails, screened text, page text and element names — to the configured provider (OpenRouter, TypeSafe or OpenAI)
under your own key. Secret-shaped values (tokens, --password/--token flags, KEY=value assignments, bearer
headers, URL credentials) are masked heuristically first and the count is reported, but masking is best-effort,
never a guarantee.
Setup
Key in the environment: OPENROUTER_API_KEY (endpoint https://openrouter.ai/api/v1/systemone, model jev-1.13)
or TYPESAFE_API_KEY (https://api.typesafe.ai/v1/systemone, model jev-latest), or OPENAI_API_KEY
(https://api.openai.com/v1/decisions, model gpt-6-luna). JEV_API_KEY is a fallback for all three providers.
Never print, echo, log or commit the value — report the variable name only. CLI: specweave jev setup pings
live, then writes jev.enabled: true into .specweave/config.json. Manual path: confirm the variable exists
without printing it (grep -q OPENROUTER_API_KEY .env, PowerShell Select-String -Quiet), then set "jev": { "enabled": true } in .specweave/config.json yourself.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 14d ago Changed · -2 lines · -8 tokens per session a9efb39df53c
- 27d ago Changed · -53 lines · +46 tokens per session scan A → C 587194b8b165
- 1mo ago First seen · 204 lines · 0 tokens per session scan A 0ec12b3d3542
jev is a skill published in the GitHub repository anton-abyzov/specweave (167 stars, last pushed today), licensed MIT. It adds 38 tokens to every session and 2,565 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it C with 2 findings (recursive force delete, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-22.
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