Agent Introspection Debugging

作者 affaan-mef648e01899bMIT275K 個星標收錄於 2026年10月8日更新於 2026年10月8日儲存庫3 天前更新

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.

僅含說明AI & Agents
AI 產生的概覽

一套用於診斷與復原 AI 代理執行失敗的結構化四階段工作流程。

功能
此技能為執行失敗的 AI 代理提供自我除錯工作流程。它引導代理走過四個階段:擷取失敗狀態、對照已知失敗模式診斷根因、執行受控的復原動作,並產出結構化的自省報告。它會產生擷取範本、診斷表、復原檢查清單與最終除錯報告。
適用情境
當代理執行反覆失敗、在同一工具上迴圈、消耗權杖卻沒有進展,或偏離原定任務時使用。它適用於可透過診斷再重試來處理的工具或環境故障。不適用於程式碼變更後的功能驗證,或已有更專門技能涵蓋的框架特定除錯。
執行需求
不需要指令碼或執行環境,是僅含指示的工作流程技能。它引用其他技能(verification-loop、continuous-learning-v2、council、workspace-surface-audit)進行整合,但除代理本身外不需要任何套件、憑證或網路存取。

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:

markdown
## 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.

PatternLikely CauseCheck
Maximum tool calls / repeated same commandloop or no-exit observer pathinspect the last N tool calls for repetition
Context overflow / degraded reasoningunbounded notes, repeated plans, oversized logsinspect recent context for duplication and low-signal bulk
ECONNREFUSED / timeoutservice unavailable or wrong portverify service health, URL, and port assumptions
429 / quota exhaustionretry storm or missing backoffcount repeated calls and inspect retry spacing
file missing after write / stale diffrace, wrong cwd, or branch driftre-check path, cwd, git status, and actual file existence
tests still failing after “fix”wrong hypothesisisolate 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:

markdown
## 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.

markdown
## 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-loop after recovery if code was changed.
  • Use continuous-learning-v2 when the failure pattern is worth turning into an instinct or later skill.
  • Use council when the issue is not technical failure but decision ambiguity.
  • Use workspace-surface-audit if 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

來源與署名

來源:affaan-m/ecc位於.agents/skills/agent-introspection-debugging提交ef648e0

授權條款: MIT

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