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 when the user wants to know what will break if they change something, or needs safety analysis before editing code. Examples: "Is it safe to change X?", "What depends on this?", "What will break?"
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。
1. gitnexus_impact({target: "X", direction: "upstream"}) → What depends on this
2. READ gitnexus://repo/{name}/processes → Check affected execution flows
3. gitnexus_detect_changes() → Map current git changes to affected flows
4. Assess risk and report to user
If "Index is stale" → run
npx gitnexus analyzein terminal.
- [ ] gitnexus_impact({target, direction: "upstream"}) to find dependents
- [ ] Review d=1 items first (these WILL BREAK)
- [ ] Check high-confidence (>0.8) dependencies
- [ ] READ processes to check affected execution flows
- [ ] gitnexus_detect_changes() for pre-commit check
- [ ] Assess risk level and report to user
| Depth | Risk Level | Meaning |
|---|---|---|
| d=1 | WILL BREAK | Direct callers/importers |
| d=2 | LIKELY AFFECTED | Indirect dependencies |
| d=3 | MAY NEED TESTING | Transitive effects |
| Affected | Risk |
|---|---|
| <5 symbols, few processes | LOW |
| 5-15 symbols, 2-5 processes | MEDIUM |
| >15 symbols or many processes | HIGH |
| Critical path (auth, payments) | CRITICAL |
gitnexus_impact — the primary tool for symbol blast radius:
gitnexus_impact({
target: "validateUser",
direction: "upstream",
minConfidence: 0.8,
maxDepth: 3
})
→ d=1 (WILL BREAK):
- loginHandler (src/auth/login.ts:42) [CALLS, 100%]
- apiMiddleware (src/api/middleware.ts:15) [CALLS, 100%]
→ d=2 (LIKELY AFFECTED):
- authRouter (src/routes/auth.ts:22) [CALLS, 95%]
gitnexus_detect_changes — git-diff based impact analysis:
gitnexus_detect_changes({scope: "staged"})
→ Changed: 5 symbols in 3 files
→ Affected: LoginFlow, TokenRefresh, APIMiddlewarePipeline
→ Risk: MEDIUM
1. gitnexus_impact({target: "validateUser", direction: "upstream"})
→ d=1: loginHandler, apiMiddleware (WILL BREAK)
→ d=2: authRouter, sessionManager (LIKELY AFFECTED)
2. READ gitnexus://repo/my-app/processes
→ LoginFlow and TokenRefresh touch validateUser
3. Risk: 2 direct callers, 2 processes = MEDIUM
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。