Discipline layer (not a capability transplant): verification grounding, completion evidence gate, systematic investigation, early-stop prevention, and value output style packaged for Claude Code, CLI, and local LLM adapters. Performance claims are deferred to bench/RESULTS.md.
Plugin fablelayer from marketplace fablelayer-marketplace in VoidLight00/fablelayer. Commands read from the repo's manifest — 1 month ago.
fablelayer-benchmarkFableLayer blind benchmark, Fable similarity, 비용 절감, Sonnet/Opus/local LLM 비교, RESULTS.md 자동 생성, rubric 설계 요청이면 반드시 사용한다.
fablelayer-pipelineFableLayer PRD, FableLayer MVP, Claude 모델을 Fable 5급 절차 레이어로 업그레이드하는 툴킷, source ledger, benchmark, local LLM adapter, Claude Code plugin, 재실행, 수정, 보완, status, qa 요청이면 반드시 사용한다. 에이전트 팀으로 FableLayer 하네스를 실행한다.
fablelayer-qa-gatesFableLayer 산출물 검증, 게이트 실행, 유출 프롬프트 금칙 검사, 요구사항 완전성 검사, source ledger와 architecture 교차 검증 요청이면 반드시 사용한다.
fablelayer-source-ledgerFableLayer 참고 저장소, Anthropic 공식 문서, Simon Willison 분석, Fable 관련 오픈소스의 source ledger와 license/provenance 감사를 수행한다. 유출 prompt 원문은 복사하지 않고 reference-only 또는 blocked로 기록한다.
early-stop-preventionevidence-gateinvestigation-protocolverification-groundingHarness-native ECC operator layer - 71 agents, 314 skills, 98 legacy command shims, reusable hooks, rules, selective install profiles, and production-ready workflows for Claude Code, Codex, OpenCode, Cursor, and related agent harnesses
200 skills · 98 commands · 71 agents · 1 MCP server · hooks
Persistent memory for Claude Code: the next session starts where the last one left off.
29 skills · 1 MCP server · hooks
Open-source AI job search that runs locally in your AI coding CLI: scan job portals, evaluate listings into a structured A-H report with a 1-5 score, tailor your CV, track applications
5 skills · hooks
Agentic QE (external: maintained in proffesor-for-testing/agentic-qe, pinned to v3.15.17): a quality-engineering fleet with 11 QE agents, 9 skills, 9 commands, the agentic-qe MCP server pinned to the plugin version, and the aqe-mod guard that refuses destructive operations on AQE learning data
9 skills · 9 commands · 11 agents · 1 MCP server
Skill: add headroom-ai compress() to a Python or TypeScript app to cut LLM input tokens (tool outputs, logs, JSON, chat history). No API key.
1 skill
Build powerful, stateful CLI interfaces for any GUI application using the cli-anything harness methodology.
5 commands
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