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grahama1970/skill-installer

从精选列表或 GitHub 仓库路径安装 Codex 技能到 $CODEX_HOME/skills。当用户要求列出可安装技能、安装精选技能或从其他仓库(包括私有仓库)安装技能时使用。

skill-installer 是什么?

这是一个为 Codex 设计的技能安装工具,主要功能是从预定义的精选列表或用户指定的 GitHub 仓库路径安装技能。默认情况下,技能源来自 OpenAI 官方技能仓库的 `.curated` 目录,同时 `.experimental` 目录提供实验性技能。该工具支持列出可安装技能(并标识已安装项)、安装单个或多个技能,以及从公共或私有 GitHub 仓库安装。工作流程上,它首先尝试直接下载公共仓库资源,若遇到认证或权限错误则回退到 Git sparse checkout 模式;私有仓库可通过已有 Git 凭证或可选的 `GITHUB_TOKEN`/`GH_TOKEN` 环境变量访问。如果目标技能目录已存在则会终止操作,安装完成后将技能存放至 `$CODEX_HOME/skills`(默认 `~/.codex/skills`)。该工具适用于 Codex 平台,帮助开发者高效管理技能扩展。

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

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打开一个已预加载此 Agent Skill 的新对话。

文档

技能安装器

帮助安装技能。默认情况下,这些技能来自 https://github.com/openai/skills/tree/main/skills/.curated,但用户也可以提供其他位置。实验性技能位于 https://github.com/openai/skills/tree/main/skills/.experimental,可以通过相同方式安装。

根据任务使用辅助脚本:

  • 当用户询问可用的技能,或使用此技能而未指定具体操作时,列出技能。默认列表为 .curated,但用户询问实验性技能时可以传递 --path skills/.experimental。
  • 当用户提供技能名称时,从精选列表中安装。
  • 当用户提供 GitHub 仓库/路径(包括私有仓库)时,从该仓库安装。

使用辅助脚本安装技能。

沟通

列出技能时,根据用户请求的上下文,大致输出如下内容。如果用户询问实验性技能,则从 .experimental 而不是 .curated 列出,并相应标注来源:

来自 {repo} 的技能:
1. skill-1
2. skill-2(已安装)
3. ...
您想安装哪些?

安装技能后,告知用户该技能将在下一次对话回合中可用。

脚本

所有这些脚本都需要网络连接,因此在沙箱中运行时,在执行前请求提升权限。

  • scripts/list-skills.py(打印技能列表,包含已安装标注)
  • scripts/list-skills.py --format json
  • 示例(实验性列表):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>
  • 示例(实验性技能):scripts/install-skill-from-github.py --repo openai/skills --path skills/.experimental/<skill-name>

行为与选项

  • 默认对于公共 GitHub 仓库采用直接下载。
  • 如果下载因认证/权限错误而失败,则回退到 Git sparse checkout。
  • 如果目标技能目录已存在,则中止操作。
  • 安装到 $CODEX_HOME/skills/<skill-name>(默认为 ~/.codex/skills)。
  • 多个 --path 值可以一次安装多个技能,除非提供 --name,否则每个技能以路径的基本名称命名。
  • 选项:--ref <ref>(默认 main),--dest <path>,--method auto|download|git。

注意事项

  • 精选列表通过 GitHub API 从 https://github.com/openai/skills/tree/main/skills/.curated 获取。如果无法获取,解释错误并退出。
  • 私有 GitHub 仓库可通过现有的 Git 凭证或可选的 GITHUB_TOKEN/GH_TOKEN(用于下载)访问。
  • Git 回退首先尝试 HTTPS,然后尝试 SSH。
  • 位于 https://github.com/openai/skills/tree/main/skills/.system 的技能是预安装的,因此无需帮助用户安装这些技能。如果用户询问,只需解释这一点。如果用户坚持,可以下载并覆盖。
  • 已安装标注来自 $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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