# Jev Documents > Locate, select, extract and verify evidence in documents or observed code inventories. Use for source-span extraction, passage reranking, claim checks or choosing code locations to inspect. Preserve citations and no-match outcomes; use required graph tools for exact code lookup. - Skill: `wuyoscar/jev-documents` (Agent Skill, multi-file: 5 files) - Install (CLI): `npx skillmds@latest add wuyoscar/jev-documents` - Raw SKILL.md: https://api.skillmd.com/api/skills/wuyoscar/jev-documents/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: wuyoscar (https://skillmd.com/u/wuyoscar) - Updated: 2026-09-22 - Page: https://skillmd.com/skills/wuyoscar/jev-documents --- # Find and verify source evidence ## Use safely Choose the service once and keep that choice. If unset, ask **A: real Jev** via OpenRouter (`OPENROUTER_API_KEY`) or TypeSafe (`TYPESAFE_API_KEY`), or **B: simulation** with this agent or an explicitly chosen available model such as DeepSeek. Wait for consent; errors do not authorize switching. Check key presence only, never values. Real calls send evidence and cost money; get approval before sending private data. For B, skip CLI/API calls. Mark `agent_simulation` or `model_simulation`, identify the actual model when available, set `jev_called: false`, `probability: null` and `confidence: null`. Return a value, evidence-based reason and `needs_review`; use null/review when evidence is missing. Do not invent Jev output or probabilities. Choice uses supplied labels, Noul uses booleans, Score uses integer rubric indices. For A, use the existing `jev-decide` CLI with the chosen `--provider openrouter` or `--provider typesafe`. If absent, explain the dependency; do not silently install. `--dry-run` is offline validation, not a judgment. Exit 0 means selected/scored, 2 means review, 1 means error. Read each value: false Noul remains false. Selection is not permission, and confidence is not accuracy. Keep unknown/review paths. ## First request Adapt [the example](assets/example.json). The shared CLI needs Python 3.10+; no sibling skill is needed. Host tools still own collection and actions. Resolve `` to this installed folder: ```bash jev-decide decide /assets/example.json --dry-run # After approval, send the edited request with the selected provider: jev-decide decide /path/to/request.json --provider openrouter ``` ## Choose the evidence workflow - **Documents:** follow the workflow below for original spans, passage relevance and claim checks; adapt [the document template](assets/example.json). - **Code locations:** read [code-location selection](references/find-code.md) and adapt [the code-location template](assets/find-code.json). Use the project's required graph/index tools first. Inspect selected code before making claims. - **Judging whether a change is correct:** use `jev-eval` if installed, rather than treating a relevance score as a code-review result. ## Workflow 1. Read the authorized source and retain document/page/line identifiers. Have parsers or regex produce exact candidate spans when possible. 2. Define the requested role precisely: invoice destination is not any email address. Include none when no candidate fits. 3. Use independent relevance questions when ranking all passages; winning a relative Choice does not establish an answer exists. 4. Copy the original span selected by ID. Do not ask Jev to synthesize the extracted field or fabricate a quotation. 5. Check each claim against its cited evidence separately. Report unsupported/contradicted statements and preserve source links for human checking. ## Context and parallelism Jev does not inherit the agent's history. Give every request sufficient context: the user's information need, exact claim, source IDs, surrounding passages, definitions and relevant exceptions. Supply the text, not just a URL or your own summary verdict. Keep needed cross-references; omit unrelated material and secrets. Batch independent claim checks or per-passage relevance scores over shared state instead of serial LLM calls. For separate document groups, use bounded concurrency with stable document/question IDs, rate limits and a cost/time budget. The host schedules calls; the CLI has no parallel scheduler. Questions cannot read other answers in the same request: fetch a selected source before asking about unseen contents. Use Jev's low latency for repeated judgments, not document generation. ## Make it yours Replace the example's evidence, candidate IDs and criteria together. Preserve a no-match route when the real task can fall outside the labels. Agree on how the host or person consumes each answer before enabling any automatic effect. ## Precedent [Related project or author example](https://github.com/jkudish/jev-mcp). Our workflow is an adaptation, not that project's code, an automatic installer, or a reproduced benchmark. [OpenRouter request contract](https://openrouter.ai/docs/api/api-reference/alphadecisions/submit-a-decisions-questions-and-answers-request). ## Examples [Rerank search and retrieval results](https://github.com/wuyoscar/jev-skill#sc-a19) · [Repository navigation](https://github.com/wuyoscar/jev-skill#sc-a20) · [Find meaning on a page, not just matching words](https://github.com/wuyoscar/jev-skill#sc-semantic-find) [More tasks and local templates](references/scenarios.md). Open only the matching row; there is no need to read the full README before a judgment.