skillhone-optimization
Optimize a skill by planning, exploring available tools, diagnosing failures, and implementing fixes via PR. Use this skill as soon as an optimization loop starts, especially on the first iteration when community tools or reference approaches should be explored before implementation.
npx skills add Tencent/SkillHone --skill skillhone-optimization --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# SkillHone Optimization You are the **optimization orchestrator**. Your goal is to raise the probe score by expanding capabilities, diagnosing failures, and landing focused PRs. ## Harness Mental Model SkillHone's harness separates four things that are easy to confuse: - **Skill repo** — the public behavior being improved: `SKILL.md`, scripts, references, and tests owned by the skill. - **Eval repo** — the private measurement contract: datasets, verifier, task contract, and compiler/audit helpers. Treat it as measurement infrastructure, not something to copy into the skill. - **Solver workdir** — per-item execution sandboxes created by the evaluator. These hold produced artifacts such as `answer.mmd` plus `trajectory.jsonl`. - **Observation surface** — redacted probe results, trajectory diagnosis, compiler/validator diagnosis, issues, PRs, and wiki pages that explain what happened without exposing gold data. Optimization work should be driven by the observation surface, not by guessing from the final score alone. The harness already creates the places where evidence lives; your job is to inspect the right layer before choosing what to change. ## What To Inspect Start by understand
- Harness Mental Model
- What To Inspect
- Available subagents
- Available scripts
- Goal
- Constraints
- Key insight: infrastructure vs skill failures
- Key insight: compiler feedback is improvement signal
- Key insight: artifact must compile before PR / before submit
- References (load when needed)
Current Forgejo repo state (issues + PRs) python3 ~/.skillhone/skills/skillhone/scripts/status.py Structured failure analysis (redacted, safe output) python3 ~/.skillhone/skills/skillhone-optimization/scripts/analyze_probe.py _data/probe_result.json Render durable observation markdown for Forgejo wiki python3 ~/.skillhone/skills/skillhone-optimization/scripts/write_observation.py --probe _data/probe_result.json --title "Iteration-N-Observation" Forgejo summary (issues, PRs, wiki pages) python3 ~/.skillhone/skills/forgejo/scripts/summary.py
What does the skillhone-optimization skill do?
Optimize a skill by planning, exploring available tools, diagnosing failures, and implementing fixes via PR. Use this skill as soon as an optimization loop starts, especially on the first iteration when community tools or reference approaches should be explored before implementation.
How do I install it?
Run `npx skills add Tencent/SkillHone --skill skillhone-optimization --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From Tencent/SkillHone, a repository with 107 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.