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에이전트 레지스트리

중앙 집중식 에이전트 아이덴티티 레지스트리를 생성하고 조회합니다. .pi/agents/*/AGENTS.md 파일을 스캔하여 프런트매터를 파싱하고 agents-registry.json을 출력하며, 선택적으로 /memory로 동기화하여 시맨틱 검색을 지원합니다.

에이전트 레지스트리란 무엇인가요?

이 스킬은 .pi/agents/*/AGENTS.md 파일에 정의된 각 에이전트의 YAML 프런트매터 메타데이터를 중앙에서 스캔하고 파싱하여 `.pi/agents-registry.json`이라는 단일 JSON 파일로 집계합니다. 이를 통해 TS 기반 D-Bus 브리지, 스킬 선택기, 오케스트레이터 등이 빠르게 에이전트 정보를 로드할 수 있습니다. 또한 `--sync-memory` 플래그를 사용하면 ArangoDB의 `agents` 컬렉션으로 동기화하여 `/memory`를 통한 시맨틱 검색(벡터 유사도 검색)을 지원합니다. 이 스킬은 `./run.sh generate`, `./run.sh list`, `./run.sh query` 등의 CLI 명령어를 제공하며, 에이전트 이름이나 특정 기능(capability)으로 필터링할 수 있어 확장 가능하고 쿼리 가능한 에이전트 생태계를 구축합니다.

지원 대상✓Claude Code~Codex CLI~Cursor✓Antigravity
npx skills add https://github.com/grahama1970/agent-skills/tree/main/skills/agents-registry

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즐겨 사용하는 AI에게 물어보기

이 에이전트 스킬이 미리 로드된 새 채팅을 엽니다.

문서

중지. 엔드포인트를 호출하기 전에 이 SKILL.MD를 전체 읽으십시오.

에이전트 레지스트리은(는) 무엇을 하나요?

중앙 집중식으로 조회 가능한 페르소나 메타데이터입니다. .pi/agents/*/AGENTS.md를 스캔하고, YAML 프런트매터를 파싱하여 TS 코드(D-Bus 브리지, 스킬 선택기, 오케스트레이터)에서 빠르게 동기 로딩할 수 있는 구체화된 JSON 뷰를 생성합니다.

사용법

# 레지스트리 생성 (.pi/agents-registry.json에 기록)
./run.sh generate

# 생성 후 /memory로 동기화 (ArangoDB agents 컬렉션)
./run.sh generate --sync-memory

# 모든 에이전트 나열
./run.sh list

# 기능(capability)으로 조회
./run.sh query --capability sparta-quality-assessment

# 에이전트 이름으로 조회
./run.sh query --agent brandon-bailey

아키텍처

진실의 원천                구체화된 뷰                조회 가능한 인덱스
─────────────          ─────────────────          ───────────────
.pi/agents/*/          .pi/agents-registry.json   ArangoDB `agents` 컬렉션
  AGENTS.md     ──→    (TS 브리지, 확장을 위한     (/memory 리콜을 통한
                         빠른 동기 로딩)             시맨틱 검색)

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

grahama1970/acceptance-contract

Turn a client brief, zip bundle, directory, or single requirements file into a typed acceptance-contract bundle with extracted requirements, acceptance checks, open questions, an immutable-goal draft, and a create-report-backed decision report. Use when users say acceptance contract, brief to requirements, freeze the goal, create immutable goal, amend immutable goal, build a Battle requirements bundle, or extract requirements from this bundle.

grahama1970/agent-ecosystem

Canonical map and shared contracts for the agent-governance ecosystem: the pi.receipt_envelope.v1 boundary envelope, the component graph, and the rules for which component owns which schema. Use when wiring a skill or extension into the shared receipt world, when asking how shame, triage-error, tau, ask, project-watchdog, ops-herdr, ponytail, and Memory fit together, or when validating an envelope.

grahama1970/agentic-evals

Agentic evaluation of skills using multi-trial fixtures, deterministic command assertions, trajectory checks, safety constraints, and evidence-backed readiness scoring. Use when users ask for agentic evals, multi-trial skill evaluation, skill trajectory validation, or readiness scoring for a skill workflow.

grahama1970/agent-inbox

File-based inter-agent messaging with headless dispatch. Check inbox, send bugs/requests to other projects, automatically spawn headless agents to fix bugs, and track progress via task-monitor.

grahama1970/agent-status

Artifact-driven status surfaces for long-running project-agent work. Maintains status.json, events.jsonl, proof manifests, and a stale-aware STATUS.html so humans can tell where the agent is, what passed, what is still unproven, and what decision or action is next — without dashboard theater.

grahama1970/align

Round-based context alignment before execution. Use when the human, project agent, WebGPT, scillm, ask, dogpile, memory, or project-knowledge may each hold different facts about a task; especially before ambiguous design, infographic, product workflow, high-stakes implementation, plan-iterate, project-infographic, or multi-review work.

grahama1970/analytics

Flexible data science analytics for any dataset. Auto-discovers schema, recommends charts, exports to create-figure. Works with JSONL, JSON, CSV from any source.

grahama1970/analyze-chatterbox-emotions

Evaluate generated Chatterbox voice files as voice-quality artifacts: affect match, arousal/valence proxies, pause placement, intelligibility inputs, clipping, loudness, and discontinuity flags. Use when reviewing Chatterbox emotional tags, pauses, Turbo/base affect delivery, Persona Dream utterance renders, or whether generated speech matches an intended product-facing affect.

grahama1970/analyze-elf

Reverse-engineer features from ELF binaries. Extracts CLI commands, state machines, protocols, Zod schemas, and data models. Automatically generates a /create-walkthrough prosecution brief with Mermaid diagrams. Uses /treesitter for AST analysis of bundled JS/TS source.

grahama1970/animation-vocabulary

Reverse-lookup glossary that turns a vague description of a web animation or motion effect into its exact term ("the bouncy thing when a popover opens" → Pop in; "the iOS rubber-band scroll" → Rubber-banding). Use when the user asks "what's it called when…", or describes a motion effect without knowing its name and wants the right word to prompt an AI or designer with. For naming an effect, not designing or building one.

grahama1970/anonymize-data

Anonymize supported CSV, JSON, UTF-8 text, and SQLite files using an explicit policy through the oai-trial project. Use for anonymize data, pseudonymize exports, redact policy literals, or discover and explicitly approve fuzzy name aliases. The skill is a thin CLI/Docker interface, not another engine.

grahama1970/anvil

Heavy-duty "No-Vibes" debugging and hardening orchestrator. Use this for complex, stubborn bugs where `review-code` has failed, or for "Red Teaming" (hardening) a codebase. Runs multiple agents in parallel (Thunderdome) using git worktree isolation.

grahama1970/apple-design

Apple's approach to interface design and fluid, physical motion, translated for the web. Use when building or reviewing gesture-driven UI, spring animations, drag/swipe/sheet interactions, momentum and interruptible transitions, translucent materials and depth, typography (optical sizing, tracking, leading), reduced-motion, or the design foundations (feedback, spatial consistency, restraint) behind Apple-style interfaces.

grahama1970/argue

Multi-persona structured debate orchestrator. Personas research via /dogpile, consult colleagues via /ask, and argue toward nuanced synthesis on complex questions.

grahama1970/arxiv

Search arXiv for papers and extract knowledge into memory. Use `search` to find papers, `learn` to extract knowledge.

grahama1970/ask

Use when the user asks to query project memory, ask an oracle, use supported browser-backed reviewers, run Tau roundtable/single-handler workflows, ask Pi-native subagents from within Pi, run persona/deep-review workflows, generate image prompts, check OS/project health through composed skills, or run an ask DAG. This skill is the executable /ask runtime; do not replace it with an informal subagent, plain web search, or hand-written review; inside Pi, explicit Pi-native subagent targets are routed through the pi-subagents tool as an Ask target type.

grahama1970/assess

Step back and critically reassess project state. Use when asked to "assess", "step back", "fresh eyes", "check alignment", "sanity check", "health check", "prune documentation", or "evaluate what's working". Offers documentation pruning and doc-code alignment analysis. Offer to run after major changes (don't auto-run).

grahama1970/assistant

Shared GPT + classifier inference gateway for persona monitor tasks. Routes validation and classification through a 4-tier cascade: heuristic → classifier → local GPT → scillm.

grahama1970/assistant-lab

Self-improvement workbench for /assistant. All the tools needed to diagnose, train, evaluate, and promote models in a continuous loop. The "warm pond" where /assistant evolves its own inference stack.

grahama1970/batch-quality

Pre-flight validation and quality gates for batch LLM operations. ACTUALLY tests samples through LLM before burning tokens. Uses SPARTA contracts for DuckDB validation queries. Integrates with task-monitor for enforced quality gates.

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