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.
中文简介正在准备中。当前显示原文说明。
Use when the user is debugging a bug, tracing an error, or asking why something fails. Examples: "Why is X failing?", "Where does this error come from?", "Trace this bug"
中文简介正在准备中。当前显示原文说明。
实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
A root cause traced in the wrong repository is a wrong root cause.
Call list_repos {} before the first tool call. 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. This matters most for cypher, whose
statement carries no in-band hint of which database it ran against.
list_repos is paginated, so page with offset: pagination.nextOffset until
hasMore is false before concluding a repository is absent.
A stale index describes the code from before your bug, so refresh before trusting a trace, and state the repository and index freshness with the diagnosis.
0. list_repos {} → Bind repo
1. query({search_query: "<error or symptom>"}) → Find related execution flows
2. context({name: "<suspect>"}) → See callers/callees/processes
3. READ gitnexus://repo/{name}/process/{name} → Trace execution flow
4. cypher({statement: "MATCH path..."}) → Custom traces if needed
If "Index is stale" → run
node .gitnexus/run.cjs analyzein terminal.
- [ ] list_repos {} — bind repo; explicit repo when >1 indexed, ask if ambiguous
- [ ] Understand the symptom (error message, unexpected behavior)
- [ ] query for error text or related code
- [ ] Identify the suspect function from returned processes
- [ ] context to see callers and callees
- [ ] Trace execution flow via process resource if applicable
- [ ] cypher for custom call chain traces if needed
- [ ] Read source files to confirm root cause
- [ ] State the repository and index freshness with the diagnosis
| Symptom | GitNexus Approach |
|---|---|
| Error message | 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 — pass worktree for a linked worktree |
| "How does A reach B?" | trace between the two symbols — shortest call chain in one call |
query — find code related to error:
query({search_query: "payment validation error", repo: "my-app"})
→ Processes: CheckoutFlow, ErrorHandling
→ Symbols: validatePayment, handlePaymentError, PaymentException
context — full context for a suspect:
context({name: "validatePayment", repo: "my-app"})
→ Incoming calls: processCheckout, webhookHandler
→ Outgoing calls: verifyCard, fetchRates (external API!)
→ Processes: CheckoutFlow (step 3/7)
cypher — custom call chain traces. Pass repo alongside the statement; the
Cypher text itself names no repository, so the result is unattributable without
it:
MATCH path = (a)-[:CodeRelation {type: 'CALLS'}*1..2]->(b:Function {name: "validatePayment"})
RETURN [n IN nodes(path) | n.name] AS chain
trace — shortest call chain between two symbols ("how does A reach B?"), one call instead of chaining context hops:
trace({ from: "processCheckout", to: "fetchRates", repo: "my-app" })
→ status: ok, hopCount: 3
→ hops: processCheckout → validatePayment → verifyCard → fetchRates
→ edges: CALLS (1.0), CALLS (0.95), CALLS (1.0)
When no path exists, trace reports the furthest reachable node — exactly where the chain breaks (dynamic dispatch, reflection, or an external boundary).
0. list_repos {}
→ total: 2 (my-app, billing-api) — bind my-app explicitly on every call
1. query({search_query: "payment error handling", repo: "my-app"})
→ Processes: CheckoutFlow, ErrorHandling
→ Symbols: validatePayment, handlePaymentError
2. context({name: "validatePayment", repo: "my-app"})
→ 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
Repository: my-app Index: current
With a single indexed repository, step 0 returns total: 1 and the repo
argument drops out of every call above.
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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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。