Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in, and reading/injecting secrets for commands.
日本語の概要は準備中です。原文の説明を表示しています。
The PRIMARY development workflow for the Archon project (remote-coding-agent). Use this skill instead of any PRP skills when working on Archon code. Routes to 10 specialized cookbooks based on what the user is trying to do: RESEARCH — "how does the orchestrator work?", "where is session state defined?", "trace the workflow execution flow", "what is IWorkflowStore?" INVESTIGATE — "should we use Drizzle or Prisma?", "what's the best way to add WebSockets?", "can we migrate to Turso?", "how do other projects handle rate limiting?" PRD — "write a PRD for dark mode", "spec out the notification feature", "product requirements for webhook retry" PLAN — "plan the auth refactor", "design the caching layer", "create an implementation plan for #42" IMPLEMENT — "implement the plan", "execute .claude/archon/plans/auth.plan.md", "build the feature from the plan", "code this up" REVIEW — "review PR #123", "review my changes", "code review the diff" DEBUG — "debug the failing test", "why is streaming broken?", "root cause analysis on the timeout issue" COMMIT — "commit these changes", "commit the auth refactor" PR — "create a PR", "open a pull request for this branch" ISSUE — "report this to gh", "create a gh issue", "log it in github", "file a bug for this", "create a feature request" This skill triggers on ANY development task: researching, investigating, planning, building, reviewing, debugging, committing, or shipping code. NOT for: Running Archon CLI workflows in worktrees (use /archon instead).
インストール方法を見るインストールする前に、エージェントに与えられる指示の中身を確認できます。
Development workflow — research, plan, build, review, ship.
git branch --show-current 2>/dev/null || echo "not in git repo"ls .claude/archon/ 2>/dev/null || echo "none yet"ls .claude/archon/plans/*.plan.md 2>/dev/null | head -5 || echo "none"Read $ARGUMENTS and determine which cookbook to load.
If the user explicitly names a cookbook (e.g., "plan", "implement"), use that. Otherwise, match intent from keywords:
| Intent | Keywords | Cookbook |
|---|---|---|
| Codebase questions, document what exists | "research", "how does", "what is", "where is", "trace", "find" | cookbooks/research.md |
| Strategic research, library eval, feasibility | "investigate", "should we", "can we", "compare", "evaluate", "feasibility", "best way to", "best approach" | cookbooks/investigate.md |
| Write product requirements | "prd", "requirements", "spec", "product requirement" | cookbooks/prd.md |
| Create implementation plan | "plan", "design", "architect", "write a plan" | cookbooks/plan.md |
| Execute an existing plan | "implement", "execute", "build", "code this", path to .plan.md | cookbooks/implement.md |
| Review code or PR | "review", "review PR", "code review", "review changes" | cookbooks/review.md |
| Debug or root cause analysis | "debug", "rca", "root cause", "why is", "broken", "failing" | cookbooks/debug.md |
| Commit changes | "commit", "save changes", "stage" | cookbooks/commit.md |
| Create pull request | "pr", "pull request", "create pr", "open pr" | cookbooks/pr.md |
| Report to GitHub | "issue", "report to gh", "log in github", "file a bug", "feature request", "create issue", "gh issue" | cookbooks/issue.md |
If ambiguous: Ask the user which cookbook to use.
After routing: Read the matched cookbook file and follow its instructions exactly.
Cookbooks feed into each other. After completing one, suggest the next:
research ──► investigate ──► prd ──► plan ──► implement ──► commit ──► pr
▲ │
debug ───────────┘ review ◄──────┘
│
▼
issue ──► plan (if feature) or debug (if bug)
All artifacts go to .claude/archon/. Create subdirectories as needed on first use.
.claude/archon/
├── prds/ # Product requirement documents
├── plans/ # Implementation plans
│ └── completed/ # Archived after implementation
├── reports/ # Implementation reports
├── issues/ # GitHub issue investigations
│ └── completed/
├── reviews/ # PR review reports
├── debug/ # Root cause analysis
└── research/ # Research findings
Do NOT hardcode project-specific commands. Detect dynamically:
bun.lockb → bun, pnpm-lock.yaml → pnpm, yarn.lock → yarn, else npmpackage.json scripts for validate, check, or verifytest script in package.jsonfile:lineまだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in, and reading/injecting secrets for commands.
日本語の概要は準備中です。原文の説明を表示しています。
Use this skill when the user requests to review, analyze, critique, or summarize academic papers, research articles, preprints, or scientific publications. Supports comprehensive structured reviews covering methodology assessment, contribution evaluation, literature positioning, and constructive feedback generation. Trigger on queries involving paper URLs, uploaded PDFs, arXiv links, or requests like "review this paper", "analyze this research", "summarize this study", or "write a peer review".
日本語の概要は準備中です。原文の説明を表示しています。
Add descriptions for new models from the HuggingFace router to chat-ui configuration. Use when new models are released on the router and need descriptions added to prod.yaml and dev.yaml. Triggers on requests like "add new model descriptions", "update models from router", "sync models", or when explicitly invoking /add-model-descriptions.
日本語の概要は準備中です。原文の説明を表示しています。
Automates browser interactions for web testing, form filling, screenshots, and data extraction. Use when the user needs to navigate websites, interact with web pages, fill forms, take screenshots, test web applications, or extract information from web pages.
日本語の概要は準備中です。原文の説明を表示しています。
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
日本語の概要は準備中です。原文の説明を表示しています。
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
日本語の概要は準備中です。原文の説明を表示しています。