Evals Run Skill

> !IMPORTANT] > Workflow skill for evals run.

Optional args: slug=<feature>, ticket=<id/url>, mode=interactive|autonomous|channel, channel=<id>, auto_continue=true|false, profile=business|hybrid|technical.

Instructions

When the user asks to perform this workflow, execute the following steps:

description: Run blinded live skill evals and publish reproducible v2 results.

Goal

Measure whether a skill changes agent behavior with isolated, immutable, outcome-based eval evidence.

Steps

1. Choose or resume a run

• For ordinary maintenance after a complete catalog baseline exists, run pnpm evals:baseline first. It creates or resumes a selective manifest, reuses only compatible evidence, and prints the model, reasoning level, concurrency, and fresh-answer count without starting workers.
• Review that plan before spending quota. Start workers only with pnpm evals:baseline -- --execute; the default is gpt-5.6-luna with high reasoning and one worker. Override intentionally with EVALS_MODEL, EVALS_REASONING_EFFORT, or EVALS_CONCURRENCY (maximum four workers).
• If usage is exhausted, keep the run directory and rerun the identical --execute command after access resumes; completed answers are reused automatically.

• Use pnpm evals:manifest -- --category <category> for one category or pnpm evals:manifest -- --all for the complete catalog.
• Use pnpm evals:manifest -- --resume <runId> only when deliberately continuing an existing run; a new invocation always creates a collision-safe run ID.
• Record the printed run ID. The manifest records source hashes, the v2 schema, and the generation protocol.

2. Answer each blinded case

• Run each baseline and with-skill arm in a separate worker/context.
• Baseline receives only the prompt. With-skill receives the same prompt plus that skill's SKILL.md.
• Trigger cases receive only the skill name and one-line description; never open the full skill body or expose the expected label.
• Trigger prompt filenames use opaque case IDs; never infer the expected label from filenames or ordering.
• For all runs, write answers under answers/<category>/<skill>/<case>; category runs use answers/<skill>/<case>.
• Mark known compromised baselines in the manifest and do not use them for delta calculations until clean reruns replace them.

3. Complete and score

• Fill metadata.agent, metadata.model, and metadata.completedAt after every required answer exists.
• Run pnpm evals:score -- --run <runId>.
• Scoring refuses to write results.json while any arm is pending, verifies source hashes, and writes one immutable inputs.json snapshot before publishing v2 results.

4. Report and verify

• Run pnpm evals:report to project aggregate runs into the newest complete category partitions and update physical history/archive records.
• Run pnpm evals:verify -- --run <runId> and, before handoff, pnpm evals:verify -- --all.
• Confirm case pass rate, assertion pass rate, trigger recall, trigger specificity, and balanced trigger accuracy. Treat baseline and delta as n/a for compromised arms.
• Never hand-edit results.json, transcripts, history, or archives. Fix inputs or eval definitions and regenerate.

Output

Run Summary

Evidence

Known Risks

Outcome Report

feature_status: implemented | partially_implemented | blocked requirement_trace: manifest -> inputs -> results -> report -> verification completed_evidence: ] missing_evidence: ] decision_needed: ] recommended_next_workflow: verify-work