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[Code Intelligence] Use when investigating what happens when code executes, understanding blast radius, or tracing frontend-to-backend flows.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/duc01226/EasyPlatform --skill graph-trace命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
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[Architecture] Use when designing solution architecture across backend, frontend, deployment, monitoring, testing, and code quality.
[Utilities] Use when you need to answer technical and architectural questions.
[Content] Use when you need to brainstorm as a PO/BA — structured ideation for problem-solving, new product creation, or feature enhancement.
基于 SOC 职业分类
| name | graph-trace |
| description | [Code Intelligence] Use when investigating what happens when code executes, understanding blast radius, or tracing frontend-to-backend flows. |
| version | 1.0.0 |
Goal: [Code Intelligence] Trace full system flow from a target file or function through all edge types (CALLS, events, bus messages, API endpoints). Supports downstream, upstream, or bidirectional tracing. Use when investigating what happens when code executes, understanding blast radius, or tracing frontend-to-backend flows.
Workflow:
Key Rules:
file:line) with confidence >80% to act.--direction downstream--direction upstream--direction both (best when entry point is a middle file like a controller or command handler)Graph must exist (.code-graph/graph.db). If missing, run /graph-build first.
If the user specifies a file path, use it directly. If the query is semantic:
| Direction | When to Use | Example |
|---|---|---|
downstream (default) | What does this code trigger? | "What happens after an order is created?" |
upstream | What calls this code? | "What triggers this event handler?" |
both | Full picture through a middle point | "Show full flow through this controller" |
Bug/failure rule: start with upstream or both from the final reader/output file before tracing producers downstream. This prevents starting from a guessed origin path and missing alternate writers.
# Downstream trace (default) — what does this trigger?
python .claude/scripts/code_graph trace <target> --json
# Upstream trace — what calls/triggers this?
python .claude/scripts/code_graph trace <target> --direction upstream --json
# Bidirectional — full flow through this point
python .claude/scripts/code_graph trace <target> --direction both --json
# End-to-start bug trace — begin at final reader/output, then enumerate upstream producers
python .claude/scripts/code_graph trace <final-reader-or-output-file> --direction upstream --depth 5 --json
python .claude/scripts/code_graph trace <writer-or-consumer-file> --direction both --depth 5 --json
# Custom depth (default: 3)
python .claude/scripts/code_graph trace <target> --direction both --depth 5 --json
# Filter to specific edge types
python .claude/scripts/code_graph trace <target> --edge-kinds CALLS,MESSAGE_BUS --json
The trace returns a multi-level BFS tree:
{
"status": "ok",
"direction": "both",
"levels": [
{ "depth": 0, "nodes": [...], "edges": [] },
{ "depth": 1, "nodes": [...], "edges": [{ "kind": "CALLS", ... }] },
{ "depth": 2, "nodes": [...], "edges": [{ "kind": "MESSAGE_BUS", ... }] }
]
}
Present results grouped by depth level. Highlight cross-service MESSAGE_BUS edges — these show the flow spreading to other microservices.
If trace returns status: "ambiguous", multiple nodes match the target name. Use search to find the exact qualified name:
python .claude/scripts/code_graph search <keyword> --kind Function --json
Then retry with the full qualified name.
When a grep/glob surfaces an important entry-point file — an entity, command, query, event/command handler, controller, bus message/consumer, component, store, or api-service — immediately run a graph trace on it before concluding. Grep finds files; the trace reveals callers, consumers, bus messages, event chains, and tests that grep CANNOT find:
python .claude/scripts/code_graph trace <key-entry-file> --direction both --json
Pattern: grep finds files → graph trace reveals full system flow → grep verifies specific details.
| Edge Kind | Meaning |
|---|---|
CALLS | Direct function/method calls |
TRIGGERS_EVENT | Entity CRUD triggers event handler |
PRODUCES_EVENT | Event handler triggers bus message producer |
MESSAGE_BUS | Bus message producer to consumer (cross-service) |
TRIGGERS_COMMAND_EVENT | Command triggers command event handler |
API_ENDPOINT | Frontend HTTP call to backend route |
trace <target> [--direction downstream|upstream|both] [--depth N] [--edge-kinds KIND1,KIND2] [--node-mode file|function|class|all] [--json]
| Flag | Default | Description |
|---|---|---|
--direction | downstream | Trace direction |
--depth | 3 | Maximum BFS depth |
--edge-kinds | all | Comma-separated edge kinds to follow |
--node-mode | all | Granularity: file (10-30x less noise), function, class, all |
--json | off | Structured JSON output |
# What happens when a user is created? (trace from command handler downstream — substitute paths from project config)
python .claude/scripts/code_graph trace {path/to/command-handler-file} --json
# What calls this API controller? (trace upstream to find frontend callers)
python .claude/scripts/code_graph trace {path/to/controller-file} --direction upstream --json
# Full flow through an entity event handler (upstream triggers + downstream consumers)
python .claude/scripts/code_graph trace {path/to/event-handler-file} --direction both --json
# File-level overview (10-30x less noise — great first pass before drilling into functions)
python .claude/scripts/code_graph trace {path/to/controller-file} --direction both --node-mode file --json
--json — structured output is needed for parsingcallers_of or importers_of instead (faster)/graph-query — Individual query patterns (callers_of, importers_of, etc.)/graph-blast-radius — Change-driven impact analysis from git diff/graph-build — Build or rebuild the graph/graph-connect-api — Frontend-to-backend API endpoint matchingTrace connections from a target node through multiple edge types using BFS. Shows the complete chain: API endpoints → commands → entity events → bus messages → cross-service consumers.
End-to-Start Debugger Trace — For non-trivial bugs, failed verification, regression fixes, behavior-changing code, or unclear code flow, start from the observed final state and walk backward before proposing a fix.
- Frame 0: observed end state — Name the exact user-visible output, failing assertion, log line, persisted value, API response, rendered UI, or aggregate bucket. Record the reader/query/renderer that produced it with
file:lineevidence.- Walk backward one hop at a time — Trace final reader -> projection/cache/storage -> writer -> consumer/handler/job -> producer/caller -> original trigger. At every hop record: input, transformation, output, owner, and evidence.
- Enumerate all feeder paths — Find every upstream producer/caller/event/job that can write into the final path, including retry, async, cache, background, and alternate UI/API paths. Mark each path verified, ruled out, or still unknown.
- Build the hypothesis matrix — For each plausible cause, list evidence for, evidence against, how to reproduce/verify, blast radius, and status (
primary,contributing,ruled out,latent). Do not fix until competing causes are explicitly resolved or bounded.- Choose the owning fix layer — Identify the invariant owner and the lowest shared point that protects all downstream consumers. A fix at the symptom site is rejected unless the symptom site owns the invariant.
- Prove convergence forward — After choosing the fix, walk start -> end again and show how the corrected state reaches the observed final output. Map each root cause to a fix part and each fix part to a test/proof.
BLOCKED until: final state named · backward trace written · all feeder paths enumerated · hypothesis matrix completed · owning fix layer justified · forward convergence proof mapped to tests.
NEVER: Start at the first suspicious code path. Collapse multiple producers into one "flow". Treat duplicate symptoms as duplicate records without proving the read model. Skip ruled-out hypotheses.
AI Mistake Prevention — Failure modes to avoid on every task:
Re-read files after context changes. Context compaction, resume, or long-running work can make memory stale; verify current files before acting. Verify generated content against source evidence. AI hallucinates APIs, names, claims, and document facts. Check the relevant source before documenting or referencing. Check downstream references before deleting or renaming. Removing an artifact can stale docs, generated mirrors, configs, and callers; map references first. Trace the full impact chain after edits. Changing a definition can miss derived outputs and consumers. Follow the affected chain before declaring done. Verify ALL affected outputs, not just the first. One green check is not all green checks; validate every output surface the change can affect. Assume existing values are intentional — ask WHY before changing. Before changing a constant, limit, flag, wording, or pattern, read nearby context and history. Surface ambiguity before acting — don't pick silently. Multiple valid interpretations require an explicit question or stated assumption with risk. Keep shared guidance role-relevant. Universal guidance must help every receiving skill or agent; code-specific obligations belong only in code-specific protocols.
Critical Thinking Mindset — Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence >80% to act. Anti-hallucination: Never present guess as fact — cite sources for every claim, admit uncertainty freely, self-check output for errors, cross-reference independently, stay skeptical of own confidence — certainty without evidence root of all hallucination.
MUST ATTENTION apply critical + sequential thinking — every claim needs appropriate traced evidence (file:line for repo/code claims; source URL or artifact section for research, product, content, and docs claims); confidence >80% to act, <60% DO NOT recommend. Anti-hallucination: never present guess as fact, admit uncertainty freely, cross-reference independently, stay skeptical of own confidence.
MUST ATTENTION apply AI mistake prevention — verify generated content against evidence, trace downstream references before deleting or renaming, verify all affected outputs, re-read files after context loss, and surface ambiguity before acting.
IMPORTANT MUST ATTENTION debugger trace gate: for non-trivial bug/fix/investigation/review work, start at the observed final output and trace backward through reader -> storage/projection -> writer -> consumer/job -> producer/trigger. Enumerate all feeder paths and hypotheses before fixing. BLOCKED until trace, hypothesis matrix, owning fix layer, and forward convergence proof exist.
IMPORTANT MUST ATTENTION — Protocols in force (concise digest of the SYNC/shared blocks this skill carries):
End-to-Start Debugger Trace: trace backward from final output, enumerate feeders before fixing.
AI Mistake Prevention: verify generated content against evidence, trace downstream references, verify all affected outputs, re-read after context loss, surface ambiguity.
Critical Thinking: trace every claim, confidence >80% to act, never guess.
MANDATORY IMPORTANT MUST ATTENTION break work into small todo tasks using TaskCreate BEFORE starting
MANDATORY IMPORTANT MUST ATTENTION search codebase for 3+ similar patterns before creating new code
MANDATORY IMPORTANT MUST ATTENTION cite file:line evidence for every claim (confidence >80% to act)
MANDATORY IMPORTANT MUST ATTENTION add a final review todo task to verify work quality
[TASK-PLANNING] Before acting, analyze task scope and systematically break it into small todo tasks and sub-tasks using TaskCreate.