Sort messages and records by custom criteria

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.

🚦 Before bulk work: smoke_test

Default smoke_test=true for large labeling jobs. This is an instruction to the host agent to write task-specific code, not a Jev API field or a required bundled runner. Accept it in a natural-language request or the host's supported skill invocation arguments; never shell-evaluate raw argument text.

Read the pilot workflow. Agree sample scope and model IDs, write the sampling and bounded concurrent paired-call code for this user's app, test it locally, then run the approved pilot. Both arms receive the same full relevant context and criteria; keep independent gold labels out of both inputs. Show actual IO, disagreements, coverage, failures and costs. Without gold, call it agreement, not accuracy. Stop for review before scaling; a successful pilot is not permission to label the full population or modify accounts.

smoke_test=false is an explicit user waiver, recorded as skipped, never passed. In simulation mode do not invent a paired API result. Offer real setup or a waiver and wait. See the reference for copyable prompts and optional task parameters.

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

Workflow

1. Agree on categories with short inclusions/exclusions. Use independent Nouls when records can have several labels; use Choice for one queue.
2. Preserve original record IDs. For multiple records, name the exact record ID in every question or send one request per record; one Choice over an entire inbox is not per-message classification.
3. Collect text only from files or accounts the user authorized. Classify before writing tags, moving messages or sending replies.
4. Return a reviewable table: record ID, category, urgency, uncertainty and intended next consumer. Keep other/missing-evidence records visible.
5. Before bulk work, apply smoke_test above. Include near-miss categories and user-labeled examples; retest when criteria, models or context organization change.

Context and parallelism

Jev does not inherit the agent's history. Give every request sufficient context: the user's categories and priority policy, each record's text and relevant thread, product/account facts, and known missing evidence. A last-message fragment is not enough when earlier messages change its meaning; omit unrelated history and secrets.

For bulk triage, batch independent category, escalation and urgency questions over shared state instead of serial LLM calls. Name the record ID in every question. Use bounded concurrency for independent requests, with stable 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: gather any newly needed account evidence before a dependent follow-up. Low latency is a reason to use Jev for the judgment stage, not to skip quality checks or automate mailbox changes.

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

Mark sponsor segments in a video · Support queue routing · Urgency screening

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