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.
Step 1 discovers what is indexed; every call after it must say which of those
it means. With one indexed repository, use the examples below as written. With
more than one, pass repo on every call: an omitted repo normally errors,
but under an MCP policy with a configured default it resolves to that default
silently. If you cannot tell which repository is meant, stop and ask. Report
the bound repository and index freshness alongside your explanation.
list_repos is paginated, so page with offset: pagination.nextOffset until
hasMore is false before concluding a repository is absent.
1. list_repos {} or READ gitnexus://repos → Discover indexed repos
2. READ gitnexus://repo/{name}/context → Codebase overview, check staleness
3. query({search_query: "<what you want to understand>"}) → Find related execution flows
4. 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
node .gitnexus/run.cjs analyzein terminal.
- [ ] list_repos {} — bind repo; explicit repo when >1 indexed, ask if ambiguous
- [ ] READ gitnexus://repo/{name}/context
- [ ] query for the concept you want to understand
- [ ] Review returned processes (execution flows)
- [ ] context on key symbols for callers/callees
- [ ] READ process resource for full execution traces
- [ ] Read source files for implementation details
- [ ] State the repository and index freshness with the explanation
| 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) |
query — find execution flows related to a concept:
query({search_query: "payment processing", repo: "my-app"})
→ Processes: CheckoutFlow, RefundFlow, WebhookHandler
→ Symbols grouped by flow with file locations
context — 360-degree view of a symbol:
context({name: "validateUser", repo: "my-app"})
→ Incoming calls: loginHandler, apiMiddleware
→ Outgoing calls: checkToken, getUserById
→ Processes: LoginFlow (step 2/5), TokenRefresh (step 1/3)
repo is required once more than one repository is indexed, and may be omitted
with a single one.
1. list_repos {} → total: 1 (my-app) — bind it
READ gitnexus://repo/my-app/context → 918 symbols, 45 processes
2. query({search_query: "payment processing"})
→ CheckoutFlow: processPayment → validateCard → chargeStripe
→ RefundFlow: initiateRefund → calculateRefund → processRefund
3. context({name: "processPayment"})
→ Incoming: checkoutHandler, webhookHandler
→ Outgoing: validateCard, chargeStripe, saveTransaction
4. Read src/payments/processor.ts for implementation details
5. Answer, noting: Repository my-app, index current
Had step 1 returned two repositories, every call above would carry
repo: "my-app".
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Use when the user wants to learn, be taught, be quizzed, or understand an idea across any domain — science, math, philosophy, code, law, politics, psychology, medicine, arts, or history — with active scaffolding.
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This skill helps with using the @ax-llm/ax TypeScript library for building LLM applications. Use when the user asks about ax(), ai(), f(), s(), agent(), flow(), AxGen, AxAgent, AxFlow, signatures, streaming, or mentions @ax-llm/ax.
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This skill helps an LLM generate correct AxAgent code using @ax-llm/ax. Use when the user asks about agent(), child agents, namespaced functions, discovery mode, shared fields, llmQuery(...), RLM code execution, recursionOptions, or agent runtime behavior. For tuning and eval with agent.optimize(...), use ax-agent-optimize.
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This skill helps an LLM generate correct AxAgent tuning and evaluation code using @ax-llm/ax. Use when the user asks about agent.optimize(...), judgeOptions, eval datasets, optimization targets, saved optimizedProgram artifacts, or recursive optimization guidance.
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This skill helps an LLM generate correct AI provider setup and configuration code using @ax-llm/ax. Use when the user asks about ai(), providers, models, presets, embeddings, extended thinking, context caching, or mentions OpenAI/Anthropic/Google/Azure/Groq/DeepSeek/Mistral/Cohere/Together/Ollama/HuggingFace/Reka/OpenRouter with @ax-llm/ax.
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This skill helps an LLM generate correct AxFlow workflow code using @ax-llm/ax. Use when the user asks about flow(), AxFlow, workflow orchestration, parallel execution, DAG workflows, conditional routing, map/reduce patterns, or multi-node AI pipelines.
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