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grahama1970/skill-installer

從策展列表或 GitHub 倉庫路徑安裝 Codex 技能至 $CODEX_HOME/skills。當使用者要求列出可安裝的技能、安裝策展技能或從其他倉庫(包括私有倉庫)安裝技能時使用。

skill-installer 是什麼?

這是一個專為 Codex 環境設計的技能安裝器,協助使用者輕鬆安裝和管理 AI 助理技能。它預設從官方策展列表(https://github.com/openai/skills/tree/main/skills/.curated)安裝技能,也支援從使用者指定的 GitHub 倉庫路徑安裝,包括私有倉庫。技能支援列出可安裝的技能、安裝特定技能以及從其他倉庫安裝進階功能。安裝方式支援直接下載(適用於公開倉庫)和 Git sparse checkout(當直接下載因權限問題失敗時自動切換)。安裝目錄預設為 `~/.codex/skills`(可透過 `$CODEX_HOME` 環境變數自訂)。若目標技能目錄已存在,安裝程序會自動中止。此外,私有倉庫可透過現有 Git 憑證或環境變數 `GITHUB_TOKEN`/`GH_TOKEN` 存取。

相容平台~Claude Code✓Codex CLI~Cursor
npx skills add https://github.com/grahama1970/agent-skills/tree/main/skills/.system/skill-installer

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在你喜歡的 AI 中提問

開啟一個已預先載入此 Agent Skill 的新對話。

說明文件

技能安裝器

協助安裝技能。預設來源為 https://github.com/openai/skills/tree/main/skills/.curated,但使用者也可以提供其他位置。實驗性技能位於 https://github.com/openai/skills/tree/main/skills/.experimental,可以相同方式安裝。

根據任務使用輔助腳本:

  • 當使用者詢問有哪些可用技能,或使用此技能但未指定要做什麼時,列出技能。預設清單為 .curated,但當使用者詢問實驗性技能時,可傳入 --path skills/.experimental。
  • 當使用者提供技能名稱時,從策展列表安裝。
  • 當使用者提供 GitHub 倉庫/路徑(包括私有倉庫)時,從其他倉庫安裝。

使用輔助腳本安裝技能。

溝通

列出技能時,根據使用者請求的上下文輸出大致如下。如果使用者詢問實驗性技能,則從 .experimental 而非 .curated 列出,並相應標註來源:

來自 {repo} 的技能:
1. skill-1
2. skill-2(已安裝)
3. ...
您想安裝哪些?

安裝技能後,告知使用者在下次對話時即可使用。

腳本

所有這些腳本都會使用網路,因此在沙箱中執行時,請請求提升權限。

  • scripts/list-skills.py(列出技能清單並標註已安裝狀態)
  • scripts/list-skills.py --format json
  • 範例(實驗性清單):scripts/list-skills.py --path skills/.experimental
  • scripts/install-skill-from-github.py --repo <owner>/<repo> --path <path/to/skill> [<path/to/skill> ...]
  • scripts/install-skill-from-github.py --url https://github.com/<owner>/<repo>/tree/<ref>/<path>
  • 範例(實驗性技能):scripts/install-skill-from-github.py --repo openai/skills --path skills/.experimental/<skill-name>

行為與選項

  • 公開 GitHub 倉庫預設使用直接下載。
  • 若下載因驗證/權限錯誤失敗,則切換至 Git sparse checkout。
  • 若目標技能目錄已存在,則中止安裝。
  • 安裝至 $CODEX_HOME/skills/<skill-name>(預設為 ~/.codex/skills)。
  • 多個 --path 值可在一次執行中安裝多個技能,除非提供 --name,否則每個技能以路徑的基名稱命名。
  • 選項:--ref <ref>(預設 main)、--dest <path>、--method auto|download|git。

備註

  • 策展清單透過 GitHub API 從 https://github.com/openai/skills/tree/main/skills/.curated 取得。若無法取得,請解釋錯誤並退出。
  • 私有 GitHub 倉庫可透過現有 Git 憑證或可選的 GITHUB_TOKEN/GH_TOKEN 進行下載存取。
  • Git 備用方法會先嘗試 HTTPS,再嘗試 SSH。
  • 位於 https://github.com/openai/skills/tree/main/skills/.system 的技能是預先安裝的,無需協助使用者安裝。如果使用者詢問,只需解釋即可。如果使用者堅持,可以下載並覆寫。
  • 已安裝的標註來自 $CODEX_HOME/skills。

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/agents-registry

Generate and query the centralized agent identity registry. Scans .pi/agents/*/AGENTS.md, parses frontmatter, outputs agents-registry.json and optionally syncs to /memory for semantic search.

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.

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