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Repo-local harness for evaluating agents, classifying failures, validating regressions, and producing review artifacts.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Use when the user wants the KADE System itself, including Layer 1 and Layer 2 context loading, KADE session structure, HUMAN.md and kade/ overlays, or project initialization and handoff workflows. This packet-owned wrapper is installable into ~/.agents/skills, ~/.codex/skills, and ~/.claude/skills.
Use when the task should consult, capture, validate, evolve, or retire reusable workflow shortcuts through the local `llm-wiki-skills` skill/CLI surface. MCP is optional transport only. This packet-owned wrapper explains the full tool lifecycle in depth and is installable into ~/.agents/skills, ~/.codex/skills, ~/.claude/skills, and ~/.pi/agent/skills.
Use this skill when the repo is an llm-wiki-memory vault and the task is to ingest a source, answer from the persistent wiki, create or review reusable skills, use pk-qmd for repo-local evidence retrieval, consult brv for durable memory, or lint and maintain the wiki.
Use the repo-local harness to benchmark Gym-Anything-compatible agents, cluster failures, and generate human-gated improvement proposals.
Run the repo-local harness to evaluate agents, classify failures, propose human-gated improvements, validate regressions, and generate review packets.
Use when the user wants the KADE plus gstack bridge, including session kickoff with user-profile context, /g-kade install style project scaffolding, repo adaptation, or structured handoff logging tied to llm-wiki-memory packet setup. This packet-owned wrapper is installable into ~/.agents/skills, ~/.codex/skills, and ~/.claude/skills.
| name | agent-self-improvement |
| description | Repo-local harness for evaluating agents, classifying failures, validating regressions, and producing review artifacts. |
Run:
python .agent-improvement\scripts\repo_local_harness.py all --config agent-improvement.config.json