domains / families / agent-tooling / skill-optimizer

skill-optimizer

combined

Optimizes AI skills for activation, clarity, and cross-model reliability. Use when creating or editing skill packs, diagnosing weak skill uptake, reducing regressions, tuning instruction salience, improving examples, shrinking context cost, or setting benchmark/release gates for skills. Trigger terms: skill optimization, activation gap, benchmark skill, with/without skill delta, regression, context budget, prompt salience. Diagnose and optimize Agent Skills (SKILL.md) with real session data and research-backed static analysis. Works with Claude Code, Codex, and any Agent Skills-compatible agent.

When to use

When not to use

Skill page

Conflict

The two copies describe different procedures for the same skill name. mcollina/skills defines a forward-looking optimization loop (baseline, failure pattern, salience edits, re-run, ship) and points to rule files. sickn33/agentic-awesome-skills defines a read-only diagnostic workflow that scans session JSONL files, runs 8 analysis dimensions, and outputs a P0/P1/P2 report. They overlap in the goal of improving skill activation and context cost, but each contains material steps the other lacks, and they disagree on whether the skill should modify files or only report.