1password
FreeSet up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in, and reading/injecting secrets for commands.
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Use when the user asks how code works, wants to understand architecture, trace execution flows, or explore unfamiliar parts of the codebase. Examples: "How does X work?", "What calls this function?", "Show me the auth flow"
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See how to installReview the instructions the agent will receive before you install.
1. READ gitnexus://repos → Discover indexed repos
2. READ gitnexus://repo/{name}/context → Codebase overview, check staleness
3. gitnexus_query({query: "<what you want to understand>"}) → Find related execution flows
4. gitnexus_context({name: "<symbol>"}) → Deep dive on specific symbol
5. READ gitnexus://repo/{name}/process/{name} → Trace full execution flow
If step 2 says "Index is stale" → run
npx gitnexus analyzein terminal.
- [ ] READ gitnexus://repo/{name}/context
- [ ] gitnexus_query for the concept you want to understand
- [ ] Review returned processes (execution flows)
- [ ] gitnexus_context on key symbols for callers/callees
- [ ] READ process resource for full execution traces
- [ ] Read source files for implementation details
| Resource | What you get |
|---|---|
gitnexus://repo/{name}/context | Stats, staleness warning (~150 tokens) |
gitnexus://repo/{name}/clusters | All functional areas with cohesion scores (~300 tokens) |
gitnexus://repo/{name}/cluster/{name} | Area members with file paths (~500 tokens) |
gitnexus://repo/{name}/process/{name} | Step-by-step execution trace (~200 tokens) |
gitnexus_query — find execution flows related to a concept:
gitnexus_query({query: "payment processing"})
→ Processes: CheckoutFlow, RefundFlow, WebhookHandler
→ Symbols grouped by flow with file locations
gitnexus_context — 360-degree view of a symbol:
gitnexus_context({name: "validateUser"})
→ Incoming calls: loginHandler, apiMiddleware
→ Outgoing calls: checkToken, getUserById
→ Processes: LoginFlow (step 2/5), TokenRefresh (step 1/3)
1. READ gitnexus://repo/my-app/context → 918 symbols, 45 processes
2. gitnexus_query({query: "payment processing"})
→ CheckoutFlow: processPayment → validateCard → chargeStripe
→ RefundFlow: initiateRefund → calculateRefund → processRefund
3. gitnexus_context({name: "processPayment"})
→ Incoming: checkoutHandler, webhookHandler
→ Outgoing: validateCard, chargeStripe, saveTransaction
4. Read src/payments/processor.ts for implementation details
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Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in, and reading/injecting secrets for commands.
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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".
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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.
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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.
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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.
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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.
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