agent-introspection-debugging 精选
Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.
安装方式
cp -r .agents/skills/agent-introspection-debugging ~/.claude/skills/agent-introspection-debugging把该 skill 目录放进客户端 skills 目录。不同客户端的目录可能不同,Claude Code 默认是 ~/.claude/skills/
/plugin marketplace add affaan-m/ECC⚠️ 该命令形态随客户端版本变化,仅在检测到 .claude-plugin/marketplace.json 时展示;不可用时请用「目录复制」。
https://raw.githubusercontent.com/affaan-m/ECC/b92f181b3b748d1abbc2afd94af6aa7d8c8fca29/.agents/skills/agent-introspection-debugging/SKILL.md下载后另存为 ~/.claude/skills/agent-introspection-debugging/SKILL.md。链接锁定采集时的 commit,内容与本站展示一致。
整仓一次性获取:git clone --depth 1 https://github.com/affaan-m/ECC.git
依赖与权限
- 无需 API Key
- 无需联网
- 依赖外部命令:
git - 以上为按正文与 frontmatter 自动推断的信号,仅供参考;安装前建议先阅读原文。
SKILL.md 正文
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:
- Restate the real objective in one sentence.
- Verify the world state instead of trusting memory.
- Shrink the failing scope.
- Run one discriminating check.
- 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
质量与来源
- 所属仓库
- affaan-m/ECC
- 仓库星标
- 28 万
- 最近更新
- 今天
- 采集时间
- 今天
- commit
- b92f181b3b74
- 内容指纹
- sha256:5357d123cf8cee6
- 文件路径
- .agents/skills/agent-introspection-debugging/SKILL.md