Use when the user asks about GitNexus itself — available tools, how to query the knowledge graph, MCP resources, graph schema, or workflow reference. Examples: "What GitNexus tools are available?", "How do I use GitNexus?"
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See how to installReview the instructions the agent will receive before you install.
Quick reference for all GitNexus MCP tools, resources, and the knowledge graph schema.
For any task involving code understanding, debugging, impact analysis, or refactoring:
gitnexus://repo/{name}/context — codebase overview + check index freshnessIf step 1 warns the index is stale, run
npx gitnexus analyzein the terminal first.
| Task | Skill to read |
|---|---|
| Understand architecture / "How does X work?" | gitnexus-exploring |
| Blast radius / "What breaks if I change X?" | gitnexus-impact-analysis |
| Trace bugs / "Why is X failing?" | gitnexus-debugging |
| Rename / extract / split / refactor | gitnexus-refactoring |
| Tools, resources, schema reference | gitnexus-guide (this file) |
| Index, status, clean, wiki CLI commands | gitnexus-cli |
| Tool | What it gives you |
|---|---|
query | Process-grouped code intelligence — execution flows related to a concept |
context | 360-degree symbol view — categorized refs, processes it participates in |
impact | Symbol blast radius — what breaks at depth 1/2/3 with confidence |
detect_changes | Git-diff impact — what do your current changes affect |
rename | Multi-file coordinated rename with confidence-tagged edits |
cypher | Raw graph queries (read gitnexus://repo/{name}/schema first) |
list_repos | Discover indexed repos |
Lightweight reads (~100-500 tokens) for navigation:
| Resource | Content |
|---|---|
gitnexus://repo/{name}/context | Stats, staleness check |
gitnexus://repo/{name}/clusters | All functional areas with cohesion scores |
gitnexus://repo/{name}/cluster/{clusterName} | Area members |
gitnexus://repo/{name}/processes | All execution flows |
gitnexus://repo/{name}/process/{processName} | Step-by-step trace |
gitnexus://repo/{name}/schema | Graph schema for Cypher |
Nodes: File, Function, Class, Interface, Method, Community, Process Edges (via CodeRelation.type): CALLS, IMPORTS, EXTENDS, IMPLEMENTS, DEFINES, MEMBER_OF, STEP_IN_PROCESS
MATCH (caller)-[:CodeRelation {type: 'CALLS'}]->(f:Function {name: "myFunc"})
RETURN caller.name, caller.filePath
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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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