# Agentpack Skill Review

> Review an existing agent skill and generate a balanced trigger/non-trigger eval set with a reproducible iteration workspace. Use when improving, benchmarking, or checking whether a SKILL.md triggers correctly.

- Skill: `vishal2612200/agentpack-skill-review` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds add vishal2612200/agentpack-skill-review`
- Raw SKILL.md: https://api.skillmd.com/api/skills/vishal2612200/agentpack-skill-review/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- License: AGPL-3.0-only
- Author: vishal2612200 (https://skillmd.com/u/vishal2612200)
- Updated: 2026-09-10
- Page: https://skillmd.com/skills/vishal2612200/agentpack-skill-review

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# AgentPack Skill Review

Use when the user invokes `$agentpack-skill-review <skill path or name>` or asks to review, improve, benchmark, or generate evals for a skill.

## Workflow

1. Run `agentpack skill-review --skill "$ARGUMENTS" --json` and read the generated `review.md`, `findings.json`, and `evals.json`.
2. Inspect the target `SKILL.md` directly. Treat the deterministic findings as evidence-backed checks, not a complete quality judgment.
3. Review the candidate eval set. Keep an even split of realistic should-trigger and near-miss should-not-trigger queries; edit or add cases when the generated wording is artificial.
4. Run the evals against both the current skill and a baseline without the skill when the host can run isolated agent tasks. Store transcripts, outputs, timing, and token metadata under the workspace's next `iteration-N/` directory.
5. Grade objective assertions programmatically where possible and record human feedback separately. Compare trigger precision/recall, output quality, latency, and token cost.
6. Propose targeted changes to `SKILL.md` and rerun the same evals as a new iteration. Do not claim improvement from a single example or from generated candidates that the user has not reviewed.
7. Report the workspace paths, findings, eval-set balance, baseline comparison, and validation status. If a browser viewer is available, use the host's standard eval viewer rather than writing a custom one.

The CLI command only writes local artifacts. It does not call a hosted model API or edit the skill automatically.