Skills Artificial Intelligence Jev Document Evidence Verification

Jev Document Evidence Verification

v20260928
jev-documents
Locate, select, and verify evidence in documents or code inventories. Supports source-span extraction, passage reranking, and claim checks. Preserves citations and handles no-match outcomes using graph tools for exact code lookup. Ideal for AI agents performing evidence-based reasoning.
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Overview

Find and verify source evidence

Learn from the workflows

For design requests, browse the scenario index, read the relevant guides and input/output examples, and compare or combine patterns. Adapt what you learn to the user's task; the collection is inspiration, not a closed menu. A familiar, straightforward decision can use its recipe directly.

Friendly reminder: Jev can help with initial, repeated or bulk judgments while you lead the overall work. Read the evidence, design the workflow, spot-check results (including confident or agreeing labels), and bring your own analysis and synthesis. This is guidance for collaboration, not an agent harness or a fixed call/token quota; existing user permissions and budgets still apply.

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. The shared CLI needs Python 3.10+; no sibling skill is needed. Host tools still own collection and actions. Resolve <skill-dir> to this installed folder:

jev-decide decide <skill-dir>/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.
  • Code locations: read code-location selection and adapt the code-location template. 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. The host checks claims against their cited evidence and writes the synthesis; Jev may help screen many claims, not replace reading. Report unsupported/contradicted statements and preserve source links.

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. Our workflow is an adaptation, not that project's code, an automatic installer, or a reproduced benchmark. OpenRouter request contract.

Examples

Rerank search and retrieval results · Repository navigation · Find meaning on a page, not just matching words

More workflows and local templates. Browse across examples when designing a solution; follow the guides and sources that help.

Info
Name jev-documents
Version v20260928
Size 6.67KB
Updated At 2026-09-28
Language