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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when the user wants to know what will break if they change something, or needs safety analysis before editing code. Examples: "Is it safe to change X?", "What depends on this?", "What will break?"
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
실행 점수 미검증
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Impact analysis is the gate that authorizes an edit, so it must answer for the repository you are about to edit.
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 — every result below an ambiguous
identity inherits the ambiguity. list_repos is paginated, so page with
offset: pagination.nextOffset until hasMore is false before concluding a
repository is absent.
detect_changes takes worktree when your changes are in a linked worktree
the MCP server was not launched from. The server auto-detects a worktree only
when it was launched from inside one; otherwise git diff runs in the wrong
checkout and reports zero changed symbols — a false clean check that carries
none of the degradation flags described below. In the CLI fallbacks, --repo .
means the current checkout; pass the intended repository path instead when you
are not standing in it.
State the bound identity with your risk report:
Repository: <name> (<path>) Worktree: <path> Index: <commit>, <n> behind HEAD
0. list_repos {} → Bind repo (and worktree)
1. impact({target: "X", direction: "upstream"}) or `node .gitnexus/run.cjs impact "X" --direction upstream --repo .`
2. READ gitnexus://repo/{name}/processes → Check affected execution flows
3. detect_changes({scope: "all"}) or `node .gitnexus/run.cjs detect-changes --scope all --repo .`
4. Assess risk and report to user, echoing repo/worktree/index identity
If "Index is stale" → run
node .gitnexus/run.cjs analyzein terminal. If.gitnexus/run.cjsis missing, replacenode .gitnexus/run.cjswithnpx gitnexusin the fallback commands.
- [ ] list_repos {} — bind repo; explicit repo when >1 indexed, ask if ambiguous
- [ ] impact({target, direction: "upstream"}) or CLI fallback to find dependents
- [ ] Review d=1 items first (these WILL BREAK)
- [ ] Check high-confidence (>0.8) dependencies
- [ ] READ processes to check affected execution flows
- [ ] detect_changes({scope: "all"}) or CLI fallback for pre-commit check
- [ ] Confirm the checkout you edited is the checkout that was diffed
- [ ] Assess risk level and report, stating repo/worktree/index identity
| Depth | Risk Level | Meaning |
|---|---|---|
| d=1 | WILL BREAK | Direct callers/importers |
| d=2 | LIKELY AFFECTED | Indirect dependencies |
| d=3 | MAY NEED TESTING | Transitive effects |
| Affected | Risk |
|---|---|
| <5 symbols, few processes | LOW |
| 5-15 symbols, 2-5 processes | MEDIUM |
| >15 symbols or many processes | HIGH |
| Critical path (auth, payments) | CRITICAL |
| Zero callers found | UNKNOWN |
UNKNOWN is not a low rung on this scale — it means the walk could not answer.
An empty caller set is equally consistent with "genuinely unused" and "the
callers are not resolvable by the index" (plain-object property access, dynamic
dispatch, cross-language calls), so few-callers ⇒ LOW does not apply. The
result carries a riskNote saying so. Confirm with a text search before
treating the symbol as safe to change or delete.
risk is the edit gate: warn on HIGH/CRITICAL and stop on UNKNOWN until the
uncertainty is resolved. Within single-repo mode, compare File and symbol
targets with local riskSharedAxes (direct/total only). Within group mode,
compare only group results: their riskSharedAxes overlays resolved
cross-repo crossings on that local value. Never use either field to waive the
edit gate. Check riskScale.unusedAxes before comparing kinds: MCP File walks
omit process/module axes, while web Graph-RAG expands File targets to in-file
symbols before enrichment.
impact — the primary tool for symbol blast radius. If MCP is unavailable, use node .gitnexus/run.cjs impact <symbol> --direction upstream --repo . instead:
impact({
target: "validateUser",
repo: "my-app", // required once >1 repository is indexed
direction: "upstream",
minConfidence: 0.8,
maxDepth: 3
})
→ d=1 (WILL BREAK):
- loginHandler (src/auth/login.ts:42) [CALLS, 100%]
- apiMiddleware (src/api/middleware.ts:15) [CALLS, 100%]
→ d=2 (LIKELY AFFECTED):
- authRouter (src/routes/auth.ts:22) [CALLS, 95%]
detect_changes — git-diff based impact analysis. If MCP is unavailable, use node .gitnexus/run.cjs detect-changes --scope all --repo . instead:
detect_changes({scope: "all"})
→ Changed: 5 symbols in 3 files
→ Affected: LoginFlow, TokenRefresh, APIMiddlewarePipeline
→ Risk: MEDIUM
Add repo once more than one repository is indexed, and worktree: "<abs path>" when your changes are in a linked worktree the server was not launched
from.
partial: true (a graph query failed) or truncated: true (the changed-symbol
listing was capped) means the result is short of the truth, and reads like
UNKNOWN above: a zero there means unseen, not unaffected. Re-run it rather
than tick the pre-commit check.
A wrong-worktree zero carries neither flag and is shape-identical to a genuine clean result, so confirm the checkout you edited is the one that was diffed before treating an empty change set as a passed check.
0. list_repos {}
→ total: 2 (my-app, billing-api) — both define validateUser, so bind explicitly
1. impact({target: "validateUser", repo: "my-app", direction: "upstream"}) or `node .gitnexus/run.cjs impact "validateUser" --direction upstream --repo .`
→ d=1: loginHandler, apiMiddleware (WILL BREAK)
→ d=2: authRouter, sessionManager (LIKELY AFFECTED)
2. READ gitnexus://repo/my-app/processes
→ LoginFlow and TokenRefresh touch validateUser
3. Risk: 2 direct callers, 2 processes = MEDIUM
Repository: my-app (/abs/path/my-app) Worktree: same Index: current
With a single indexed repository, step 0 returns total: 1 and the repo
argument drops out of every call above.
아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.
아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.