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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Trace bugs through call chains using knowledge graph
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See how to installReview the instructions the agent will receive before you install.
1. gitnexus_query({query: "<error or symptom>"}) → Find related execution flows
2. gitnexus_context({name: "<suspect>"}) → See callers/callees/processes
3. READ gitnexus://repo/{name}/process/{name} → Trace execution flow
4. gitnexus_cypher({query: "MATCH path..."}) → Custom traces if needed
If "Index is stale" → run
npx gitnexus analyzein terminal.
- [ ] Understand the symptom (error message, unexpected behavior)
- [ ] gitnexus_query for error text or related code
- [ ] Identify the suspect function from returned processes
- [ ] gitnexus_context to see callers and callees
- [ ] Trace execution flow via process resource if applicable
- [ ] gitnexus_cypher for custom call chain traces if needed
- [ ] Read source files to confirm root cause
| Symptom | GitNexus Approach |
|---|---|
| Error message | gitnexus_query for error text → context on throw sites |
| Wrong return value | context on the function → trace callees for data flow |
| Intermittent failure | context → look for external calls, async deps |
| Performance issue | context → find symbols with many callers (hot paths) |
| Recent regression | detect_changes to see what your changes affect |
gitnexus_query — find code related to error:
gitnexus_query({query: "payment validation error"})
→ Processes: CheckoutFlow, ErrorHandling
→ Symbols: validatePayment, handlePaymentError, PaymentException
gitnexus_context — full context for a suspect:
gitnexus_context({name: "validatePayment"})
→ Incoming calls: processCheckout, webhookHandler
→ Outgoing calls: verifyCard, fetchRates (external API!)
→ Processes: CheckoutFlow (step 3/7)
gitnexus_cypher — custom call chain traces:
MATCH path = (a)-[:CodeRelation {type: 'CALLS'}*1..2]->(b:Function {name: "validatePayment"})
RETURN [n IN nodes(path) | n.name] AS chain
1. gitnexus_query({query: "payment error handling"})
→ Processes: CheckoutFlow, ErrorHandling
→ Symbols: validatePayment, handlePaymentError
2. gitnexus_context({name: "validatePayment"})
→ Outgoing calls: verifyCard, fetchRates (external API!)
3. READ gitnexus://repo/my-app/process/CheckoutFlow
→ Step 3: validatePayment → calls fetchRates (external)
4. Root cause: fetchRates calls external API without proper timeout
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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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