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.
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.
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
jev-eval if installed, rather
than treating a relevance score as a code-review result.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.
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.
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.
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.