agent-introspection-debugging
affaan-m/ECC · Agent Skill
自動化人工智慧代理故障除錯,透過捕獲資料、診斷問題並生成報告以進行可重現分析。
前往安裝 ↓總覽
| 發布者 | |
|---|---|
| 儲存庫 | affaan-m/ECC ↗ |
| 技能資料夾 | agent-introspection-debugging |
| 星 | 258,203 |
| 分支 | 38,613 |
| 附帶檔案 | 2 |
| 授權 | MIT |
| 最近更新 | 2026年9月14日 |
| 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 “agent-introspection-debugging” skill from mozgi.io (https://mozgi.io/skills/agent-introspection-debugging/) for me. 1. Download the archive: https://mozgi.io/skills/agent-introspection-debugging/download If it fails, take the skill folder from the source: https://github.com/affaan-m/ECC/tree/main/.agents/skills/agent-introspection-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 “agent-introspection-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>/agent-introspection-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/affaan-m/ECC.git /tmp/ECC mkdir -p .claude/skills cp -r /tmp/ECC/.agents/skills/agent-introspection-debugging .claude/skills/agent-introspection-debugging
重新啟動 Claude Code — 技能就會出現在清單中,手動呼叫:/agent-introspection-debugging。要套用到所有專案,請放在 ~/.claude/skills。
git clone --depth 1 --branch main https://github.com/affaan-m/ECC.git /tmp/ECC mkdir -p .agents/skills cp -r /tmp/ECC/.agents/skills/agent-introspection-debugging .agents/skills/agent-introspection-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 $agent-introspection-debugging 呼叫。所有專案:~/.agents/skills。
git clone --depth 1 --branch main https://github.com/affaan-m/ECC.git /tmp/ECC mkdir -p .cursor/skills cp -r /tmp/ECC/.agents/skills/agent-introspection-debugging .cursor/skills/agent-introspection-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/affaan-m/ECC.git /tmp/ECC mkdir -p .gemini/skills cp -r /tmp/ECC/.agents/skills/agent-introspection-debugging .gemini/skills/agent-introspection-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。
安裝前請先檢視 SKILL.md 與相關檔案。點擊檔案即可預覽。
SKILL.md 內容
作者原文 · 唯讀Agent Introspection Debugging
Use this skill when an agent run is failing repeatedly, consuming tokens without progress, looping on the same tools, or drifting away from the intended task.
This is a workflow skill, not a hidden runtime. It teaches the agent to debug itself systematically before escalating to a human.
When to Activate
- Maximum tool call / loop-limit failures
- Repeated retries with no forward progress
- Context growth or prompt drift that starts degrading output quality
- File-system or environment state mismatch between expectation and reality
- Tool failures that are likely recoverable with diagnosis and a smaller corrective action
Scope Boundaries
Activate this skill for:
- capturing failure state before retrying blindly
- diagnosing common agent-specific failure patterns
- applying contained recovery actions
- producing a structured human-readable debug report
Do not use this skill as the primary source for:
- feature verification after code changes; use
verification-loop - framework-specific debugging when a narrower ECC skill already exists
- runtime promises the current harness cannot enforce automatically
Four-Phase Loop
Phase 1: Failure Capture
Before trying to recover, record the failure precisely.
Capture:
- error type, message, and stack trace when available
- last meaningful tool call sequence
- what the agent was trying to do
- current context pressure: repeated prompts, oversized pasted logs, duplicated plans, or runaway notes
- current environment assumptions: cwd, branch, relevant service state, expected files
Minimum capture template:
## Failure Capture
- Session / task:
- Goal in progress:
- Error:
- Last successful step:
- Last failed tool / command:
- Repeated pattern seen:
- Environment assumptions to verify:
Phase 2: Root-Cause Diagnosis
Match the failure to a known pattern before changing anything.
| Pattern | Likely Cause | Check |
| --- | --- | --- |
| Maximum tool calls / repeated same command | loop or no-exit observer path | inspect the last N tool calls for repetition |
| Context overflow / degraded reasoning | unbounded notes, repeated plans, oversized logs | inspect recent context for duplication and low-signal bulk |
| ECONNREFUSED / timeout | service unavailable or wrong port | verify service health, URL, and port assumptions |
| 429 / quota exhaustion | retry storm or missing backoff | count repeated calls and inspect retry spacing |
| file missing after write / stale diff | race, wrong cwd, or branch drift | re-check path, cwd, git status, and actual file existence |
| tests still failing after “fix” | wrong hypothesis | isolate the exact failing test and re-derive the bug |
Diagnosis questions:
- is this a logic failure, state failure, environment failure, or policy failure?
- did the agent lose the real objective and start optimizing the wrong subtask?
- is the failure deterministic or transient?
- what is the smallest reversible action that would validate the diagnosis?
Phase 3: Contained Recovery
Recover with the smallest action that changes the diagnosis surface.
Safe recovery actions:
- stop repeated retries and restate the hypothesis
- trim low-signal context and keep only the active goal, blockers, and evidence
- re-check the actual filesystem / branch / process state
- narrow the task to one failing command, one file, or one test
- switch from speculative reasoning to direct observation
- escalate to a human when the failure is high-risk or externally blocked
Do not claim unsupported auto-healing actions like “reset agent state” or “update harness config” unless you are actually doing them through real tools in the current environment.
Contained recovery checklist:
## Recovery Action
- Diagnosis chosen:
- Smallest action taken:
- Why this is safe:
- What evidence would prove the fix worked:
Phase 4: Introspection Report
End with a report that makes the recovery legible to the next agent or human.
## Agent Self-Debug Report
- Session / task:
- Failure:
- Root cause:
- Recovery action:
- Result: success | partial | blocked
- Token / time burn risk:
- Follow-up needed:
- Preventive change to encode later:
Recovery Heuristics
Prefer these interventions in order:
1. Restate the real objective in one sentence.
2. Verify the world state instead of trusting memory.
3. Shrink the failing scope.
4. Run one discriminating check.
5. Only then retry.
Bad pattern:
- retrying the same action three times with slightly different wording
Good pattern:
- capture failure
- classify the pattern
- run one direct check
- change the plan only if the check supports it
Integration with ECC
- Use
verification-loopafter recovery if code was changed. - Use
continuous-learning-v2when the failure pattern is worth turning into an instinct or later skill. - Use
councilwhen the issue is not technical failure but decision ambiguity. - Use
workspace-surface-auditif the failure came from conflicting local state or repo drift.
Output Standard
When this skill is active, do not end with “I fixed it” alone.
Always provide:
- the failure pattern
- the root-cause hypothesis
- the recovery action
- the evidence that the situation is now better or still blocked
常見問題
agent-introspection-debugging 技能有什麼用途?
自動化人工智慧代理故障除錯,透過捕獲資料、診斷問題並生成報告以進行可重現分析。
如何安裝 agent-introspection-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。
agent-introspection-debugging 適用哪些 AI 模型?
技能是純 Markdown 指示(Agent Skills 格式),因此 agent-introspection-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。
agent-introspection-debugging 是免費的嗎?
此技能由 affaan-m 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。