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Found 25 Skills
Analyse agent execution to find wasted tool calls, wrong turns, and blind alleys. Optimise agents to reach their goal in the fewest turns, tokens, and least time. Recommend harness/model changes — never apply without user approval.
Start a repo-local OptimizeSpec self-improvement change. Use when the user wants to create evals, optimize an agent with GEPA, define an agent self-improvement loop, or begin an ASI-first evaluation workflow.
Skill Evolver (Taotie) — Strengthen the target skill by "devouring" and analyzing the advantages of other skills. This skill must be triggered when users intend to: integrate two skills, optimize one skill with another, compare and analyze the pros and cons of two skills, extract the strengths of one skill into another, or express intentions like "feed X to Y", "use X to optimize Y", "integrate these two skills", "devour this skill", "skill evolution", "skill upgrade", "merge skills", etc. Even if users don't explicitly mention "Taotie", this skill should be used as long as it involves capability transfer, comparative analysis, or advantage extraction between two skills.
Design tools that agents can use effectively, including when to reduce tool complexity. Use when creating, optimizing, or reducing agent tool sets.
Summarize lessons learned from ccbox session logs (projects/sessions/history/skills) so the agent can do better next time. Produce copy-ready instruction updates (project + global) backed by evidence, with optional skill-span context to attribute failures to specific skills. Use when asked to run /ccbox:insights, generate a "lessons learned" memo, or propose standing instructions from session history.
This skill guides the agent in identifying and replacing AI model-specific cliches and formulaic expressions with more natural, human-like language, grounded in external search for better alternatives.
Meta-skill for making the agent self-improving. Covers updating AGENTS.md, creating new skills from repeated workflows, and deciding what to systematize. Invoke after completing tasks, when noticing repeated friction, or at session end.
Use when measuring or improving agent quality and performance — set up evaluators, online monitoring, CI/CD quality gates, observability, or cost optimization. Triggers on: "evaluate my agent", "add evaluator", "measure quality", "quality gate", "run evals", "agent too slow", "why is it slow", "reduce latency", "set up observability", "CloudWatch dashboard", "how much does my agent cost", "cost optimization", "logs not showing up", "logs missing", "spans not found", "eval failing", "eval error", "dev traces", "local traces", "agentcore dev traces", "traces to CloudWatch". Not for debugging errors or crashes — use agents-debug. Slow but correct routes here; broken routes to debug.
Use when improving agent prompts, frontmatter, and tool restrictions.
Create, optimize, update, and validate AGENTS.md files with maximum token efficiency. Use when the user asks to (1) create new AGENTS.md files for any repository, (2) optimize/condense existing AGENTS.md to reduce token count, (3) update/refresh AGENTS.md to sync with codebase changes, (4) validate AGENTS.md quality and completeness, or (5) improve AGENTS.md files to be more effective for AI agents. Always generates token-efficient, condensed output focused on actionable commands and patterns while maintaining model-agnostic language.
Analyzes Claude Code session transcripts to evaluate skill portfolio health — routing errors, attention competition between descriptions, and coverage gaps. Generates an interactive HTML report with per-skill health cards, competition matrix, attention budget analysis, and actionable patches. Unlike skill-creator which optimizes individual skills in isolation, skill-auditor optimizes the portfolio as a system, detecting cross-skill attention theft and cascade risks. Use when user says "audit my skills", "skill audit", "run skill-auditor", "analyze skill routing", "check skill competition", "portfolio health", "スキル監査", "スキルの精度を分析", "スキルルーティング分析".
Encodes a continuous improvement loop for goal-seeking agents: EVAL, ANALYZE, RESEARCH (hypothesis + evidence + counter-arguments), IMPROVE, RE-EVAL, DECIDE. Auto-commits improvements (+2% net, no regression >5%) and reverts failures. Works with all 4 SDK implementations. Auto-activates on "improve agent", "self-improving loop", "agent eval loop", "benchmark agents", "run improvement cycle".