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grahama1970/agents-registry

Scannt .pi/agents/*/AGENTS.md, analysiert YAML-Frontmatter und erzeugt agents-registry.json – optional synchronisiert mit /memory für semantische Suche.

Was ist agents-registry?

Diese Fähigkeit stellt ein zentrales, abfragbares Agentenregister bereit. Sie scannt alle AGENTS.md-Dateien im Verzeichnis .pi/agents/*/, extrahiert das YAML-Frontmatter und erzeugt eine materialisierte JSON-Ansicht (agents-registry.json). Das Register kann entweder lokal generiert oder optional mit der ArangoDB-/memory-Umgebung synchronisiert werden, um semantische Suchen über Agenten-Persona-Metadaten zu ermöglichen – z. B. „Welcher Agent kann Qualitätsbewertungen durchführen?“. Es unterstützt CLI-Befehle wie `generate`, `list` und `query` mit Filtern nach Fähigkeiten oder Agentennamen. Die Architektur folgt einem klaren „Source of Truth → Materialized View → Queryable Index“-Schema, wobei die materialisierte JSON-Datei von TypeScript-Komponenten wie dem D-Bus-Bridge, Skill-Selector oder Orchestrator schnell geladen werden kann. Dies ist nützlich für Entwickler, die in verteilten Agentensystemen arbeiten und eine effiziente, zentrale Agentenregistrierung und -abfrage benötigen – insbesondere in Kombination mit semantischen Suchfunktionen auf ArangoDB-Basis.

Funktioniert mit✓Claude Code~Codex CLI~Cursor✓Antigravity
Part ofAgent Workflows
npx skills add https://github.com/grahama1970/agent-skills/tree/main/skills/agents-registry

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Dokumentation

STOP. LESEN SIE DIESE GANZE SKILL.MD, BEVOR SIE EINEN ENDPUNKT AUFRUFEN.

Agentenregister

Zentrale, abfragbare Persona-Metadaten. Scannt .pi/agents/*/AGENTS.md, analysiert YAML-Frontmatter und erzeugt eine materialisierte JSON-Ansicht für das schnelle Laden durch TypeScript-Code (D-Bus-Bridge, Skill-Selector, Orchestrator).

Verwendung

# Register generieren (schreibt .pi/agents-registry.json)
./run.sh generate

# Generieren und mit /memory synchronisieren (ArangoDB-Agenten-Sammlung)
./run.sh generate --sync-memory

# Alle Agenten auflisten
./run.sh list

# Nach Fähigkeit abfragen
./run.sh query --capability sparta-quality-assessment

# Nach Agentennamen abfragen
./run.sh query --agent brandon-bailey

Architektur

Quelle der Wahrheit          Materialisierte Ansicht          Abfragbarer Index
─────────────────          ─────────────────────          ───────────────────
.pi/agents/*/              .pi/agents-registry.json       ArangoDB `agents`-Sammlung
  AGENTS.md         ──→    (schnelles Laden für           (semantische Suche via
                             TS-Bridge, Erweiterungen)      /memory-Abruf)

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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