systematic-debugging
obra/superpowers · Agent Skill
系統性除錯取代混亂編輯:假設、最小化重現、二分搜尋根本原因。
前往安裝 ↓總覽
| 發布者 | |
|---|---|
| 儲存庫 | obra/superpowers ↗ |
| 技能資料夾 | systematic-debugging |
| 星 | 286,038 |
| 附帶檔案 | 僅 SKILL.md |
| 授權 | MIT |
| 連結 | 技能原始碼 ↗ · 儲存庫 ↗ |
Mozgi 檢查
我們在技能檔案中發現的內容。詳細資訊需訂閱。
安裝
技能就是一個含 SKILL.md 的資料夾。選擇你的代理,在專案根目錄的終端機(macOS、Linux 或 Windows 的 Git Bash)執行指令。
把提示貼到 Claude、Codex 或其他助理:它會自行檢查並安裝技能。
Install the “systematic-debugging” skill from mozgi.io (https://mozgi.io/skills/debugging/) for me. 1. Take the skill folder from the source: https://github.com/obra/superpowers/tree/main/skills/systematic-debugging 2. Read SKILL.md and the other files. Check the skill is safe: no hidden commands, no sending data elsewhere, no scripts doing something other than described. If anything looks suspicious, stop and tell me. 3. Put the “systematic-debugging” folder into your agent's skills folder: .claude/skills/ for Claude Code, .agents/skills/ for Codex, .cursor/skills/ for Cursor, .gemini/skills/ for Gemini CLI. Inside the project if we are in one, otherwise in the home folder (~/.claude/skills/ etc.). 4. Make sure <skills folder>/systematic-debugging/SKILL.md exists. 5. Briefly tell me what this skill does and offer to try it on my task. If you cannot do a step yourself, tell me what to click or type.
git clone --depth 1 --branch main https://github.com/obra/superpowers.git /tmp/superpowers mkdir -p .claude/skills cp -r /tmp/superpowers/skills/systematic-debugging .claude/skills/systematic-debugging
重新啟動 Claude Code — 技能就會出現在清單中,手動呼叫:/systematic-debugging。要套用到所有專案,請放在 ~/.claude/skills。
git clone --depth 1 --branch main https://github.com/obra/superpowers.git /tmp/superpowers mkdir -p .agents/skills cp -r /tmp/superpowers/skills/systematic-debugging .agents/skills/systematic-debugging
Codex 會從目前資料夾往上到儲存庫根目錄,在 .agents/skills 中尋找技能;用 /skills 或 $systematic-debugging 呼叫。所有專案:~/.agents/skills。
git clone --depth 1 --branch main https://github.com/obra/superpowers.git /tmp/superpowers mkdir -p .cursor/skills cp -r /tmp/superpowers/skills/systematic-debugging .cursor/skills/systematic-debugging
Cursor 啟動時會載入技能;在 Agent 聊天輸入「/」呼叫。Cursor 也會讀取 .claude/skills 與 .agents/skills。所有專案:~/.cursor/skills。
git clone --depth 1 --branch main https://github.com/obra/superpowers.git /tmp/superpowers mkdir -p .gemini/skills cp -r /tmp/superpowers/skills/systematic-debugging .gemini/skills/systematic-debugging
任務符合時,Gemini CLI 會提議啟用技能,由你確認。所有專案:~/.gemini/skills。
SKILL.md 內容
作者原文 · 唯讀Systematic Debugging
Overview
Core principle: ALWAYS find root cause before attempting fixes. Symptom fixes are failure.
Violating the letter of this process is violating the spirit of debugging.
The Iron Law
NO FIXES WITHOUT ROOT CAUSE INVESTIGATION FIRST
If you haven't completed Phase 1, you cannot propose fixes.
When to Use
Use for ANY technical issue:
- Test failures
- Bugs in production
- Unexpected behavior
- Performance problems
- Build failures
- Integration issues
Use this ESPECIALLY when:
- Under time pressure (emergencies make guessing tempting)
- "Just one quick fix" seems obvious
- You've already tried multiple fixes
- Previous fix didn't work
- You don't fully understand the issue
Don't skip when:
- Issue seems simple (simple bugs have root causes too)
- You're in a hurry (rushing guarantees rework)
- Manager wants it fixed NOW (systematic is faster than thrashing)
The Four Phases
You MUST complete each phase before proceeding to the next.
Phase 1: Root Cause Investigation
BEFORE attempting ANY fix:
1. Read Error Messages Carefully
- Don't skip past errors or warnings
- They often contain the exact solution
- Read stack traces completely
- Note line numbers, file paths, error codes
2. Reproduce Consistently
- Can you trigger it reliably?
- What are the exact steps?
- Does it happen every time?
- If not reproducible → gather more data, don't guess
3. Check Recent Changes
- What changed that could cause this?
- Git diff, recent commits
- New dependencies, config changes
- Environmental differences
4. Gather Evidence in Multi-Component Systems
WHEN system has multiple components (CI → build → signing, API → service → database):
BEFORE proposing fixes, add diagnostic instrumentation:
```
For EACH component boundary:
- Log what data enters component
- Log what data exits component
- Verify environment/config propagation
- Check state at each layer
Run once to gather evidence showing WHERE it breaks
THEN analyze evidence to identify failing component
THEN investigate that specific component
```
Example (multi-layer system):
```bash
# Layer 1: Workflow
echo "=== Secrets available in workflow: ==="
echo "IDENTITY: ${IDENTITY:+SET}${IDENTITY:-UNSET}"
# Layer 2: Build script
echo "=== Env vars in build script: ==="
env | grep IDENTITY || echo "IDENTITY not in environment"
# Layer 3: Signing script
echo "=== Keychain state: ==="
security list-keychains
security find-identity -v
# Layer 4: Actual signing
codesign --sign "$IDENTITY" --verbose=4 "$APP"
```
This reveals: Which layer fails (secrets → workflow ✓, workflow → build ✗)
5. Trace Data Flow
WHEN error is deep in call stack:
See root-cause-tracing.md in this directory for the complete backward tracing technique.
Quick version:
- Where does bad value originate?
- What called this with bad value?
- Keep tracing up until you find the source
- Fix at source, not at symptom
Phase 2: Pattern Analysis
Find the pattern before fixing:
1. Find Working Examples
- Locate similar working code in same codebase
- What works that's similar to what's broken?
2. Compare Against References
- If implementing pattern, read reference implementation COMPLETELY
- Don't skim - read every line
- Understand the pattern fully before applying
3. Identify Differences
- What's different between working and broken?
- List every difference, however small
- Don't assume "that can't matter"
4. Understand Dependencies
- What other components does this need?
- What settings, config, environment?
- What assumptions does it make?
Phase 3: Hypothesis and Testing
Scientific method:
1. Form Single Hypothesis
- State clearly: "I think X is the root cause because Y"
- Write it down
- Be specific, not vague
2. Test Minimally
- Make the SMALLEST possible change to test hypothesis
- One variable at a time
- Don't fix multiple things at once
3. Verify Before Continuing
- Did it work? Yes → Phase 4
- Didn't work? Form NEW hypothesis
- DON'T add more fixes on top
4. When You Don't Know
- Say "I don't understand X"
- Don't pretend to know
- Ask for help
- Research more
Phase 4: Implementation
Fix the root cause, not the symptom:
1. Create Failing Test Case
- Simplest possible reproduction
- Automated test if possible
- One-off test script if no framework
- MUST have before fixing
- Use the
superpowers:test-driven-developmentskill for writing proper failing tests
2. Implement Single Fix
- Address the root cause identified
- ONE change at a time
- No "while I'm here" improvements
- No bundled refactoring
3. Verify Fix
- Test passes now?
- No other tests broken?
- Issue actually resolved?
- Use the
superpowers:verification-before-completionskill before claiming success
4. If Fix Doesn't Work
- STOP
- Count: How many fixes have you tried?
- If < 3: Return to Phase 1, re-analyze with new information
- If ≥ 3: STOP and question the architecture (step 5 below)
- DON'T attempt Fix #4 without architectural discussion
5. If 3+ Fixes Failed: Question Architecture
Pattern indicating architectural problem:
- Each fix reveals new shared state/coupling/problem in different place
- Fixes require "massive refactoring" to implement
- Each fix creates new symptoms elsewhere
STOP and question fundamentals:
- Is this pattern fundamentally sound?
- Are we "sticking with it through sheer inertia"?
- Should we refactor architecture vs. continue fixing symptoms?
Discuss with your human partner before attempting more fixes
This is NOT a failed hypothesis - this is a wrong architecture.
Red Flags - STOP and Follow Process
If you catch yourself thinking:
- "Quick fix for now, investigate later"
- "Just try changing X and see if it works"
- "Add multiple changes, run tests"
- "Skip the test, I'll manually verify"
- "It's probably X, let me fix that"
- "I don't fully understand but this might work"
- "Pattern says X but I'll adapt it differently"
- "Here are the main problems: [lists fixes without investigation]"
- Proposing solutions before tracing data flow
- "One more fix attempt" (when already tried 2+)
- Each fix reveals new problem in different place
ALL of these mean: STOP. Return to Phase 1.
If 3+ fixes failed: Question the architecture (see Phase 4.5)
your human partner's Signals You're Doing It Wrong
Watch for these redirections:
- "Is that not happening?" - You assumed without verifying
- "Will it show us...?" - You should have added evidence gathering
- "Stop guessing" - You're proposing fixes without understanding
- "Ultra-think this" - Question fundamentals, not just symptoms
- "We're stuck?" (frustrated) - Your approach isn't working
When you see these: STOP. Return to Phase 1.
Common Rationalizations
| Excuse | Reality |
|--------|---------|
| "Issue is simple, don't need process" | Simple issues have root causes too. Process is fast for simple bugs. |
| "Emergency, no time for process" | Systematic debugging is FASTER than guess-and-check thrashing. |
| "Just try this first, then investigate" | First fix sets the pattern. Do it right from the start. |
| "I'll write test after confirming fix works" | Untested fixes don't stick. Test first proves it. |
| "Multiple fixes at once saves time" | Can't isolate what worked. Causes new bugs. |
| "Reference too long, I'll adapt the pattern" | Partial understanding guarantees bugs. Read it completely. |
| "I see the problem, let me fix it" | Seeing symptoms ≠ understanding root cause. |
| "One more fix attempt" (after 2+ failures) | 3+ failures = architectural problem. Question pattern, don't fix again. |
Quick Reference
| Phase | Key Activities | Success Criteria |
|-------|---------------|------------------|
| 1. Root Cause | Read errors, reproduce, check changes, gather evidence | Understand WHAT and WHY |
| 2. Pattern | Find working examples, compare | Identify differences |
| 3. Hypothesis | Form theory, test minimally | Confirmed or new hypothesis |
| 4. Implementation | Create test, fix, verify | Bug resolved, tests pass |
When Process Reveals "No Root Cause"
If systematic investigation reveals issue is truly environmental, timing-dependent, or external:
1. You've completed the process
2. Document what you investigated
3. Implement appropriate handling (retry, timeout, error message)
4. Add monitoring/logging for future investigation
But: 95% of "no root cause" cases are incomplete investigation.
Supporting Techniques
These techniques are part of systematic debugging and available in this directory:
root-cause-tracing.md- Trace bugs backward through call stack to find original triggerdefense-in-depth.md- Add validation at multiple layers after finding root causecondition-based-waiting.md- Replace arbitrary timeouts with condition polling
常見問題
systematic-debugging 技能有什麼用途?
系統性除錯取代混亂編輯:假設、最小化重現、二分搜尋根本原因。
如何安裝 systematic-debugging?
打開本頁的「安裝」區塊並選擇你的代理 — Claude Code、Codex、Cursor 或 Gemini CLI:那裡有把技能資料夾複製到正確位置的現成指令。也可以複製「透過助理」分頁中的提示,讓助理自行檢查並安裝技能。
systematic-debugging 適用哪些 AI 模型?
技能是純 Markdown 指示(Agent Skills 格式),因此 systematic-debugging 適用任何讀取 SKILL.md 的代理:Claude Code、Codex、Cursor、Gemini CLI、GitHub Copilot、OpenCode 等。只有在任務符合技能描述時,模型才會載入指示。
systematic-debugging 是免費的嗎?
此技能由 obra 公開發布,採用 MIT 授權。技能本身免費 — 你只需向 AI 供應商支付實際使用的 token 費用。
安裝技能安全嗎?
技能是代理會遵循的指示,安裝前請閱讀 SKILL.md 及相關檔案。技能資料夾中的腳本只有在代理執行時才會運行 — 留意它的動作,不要授予多餘權限。
技能和 MCP 伺服器或外掛有何不同?
MCP 伺服器或外掛為模型提供新工具 — 會執行並回傳結果的程式碼。技能提供的是知識與流程:如何處理任務、要遵循哪些步驟、好的成果長什麼樣子。它不需要伺服器或 API 金鑰。