agent-introspection-debugging

affaan-m/ECC · Agent Skill

Yapay zeka ajan hatalarının hata ayıklamasını otomatikleştirir, veri yakalar, sorunları teşhis eder ve tekrarlanabilir analiz için raporlar oluşturur.

Kuruluma geç ↓

Genel bakış

Yayıncıaffaan-mTopluluk
Depoaffaan-m/ECC ↗
Skill klasörüagent-introspection-debugging
Yıldız258.203
Fork38.613
Dosyalar2
LisansMIT
Son güncelleme14 Eylül 2026
BağlantılarSkill kaynağı ↗ · Depo ↗

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Güvenlik
orta risk · dikkat gerektiren 1 nokta bulundu
Uyumluluk
yalnızca talimatlar — çoğu ajanla çalışır
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Kurulum

Skill, SKILL.md dosyası içeren bir klasördür. Ajanınızı seçin ve komutları proje kökünde bir terminalde çalıştırın (macOS, Linux veya Windows'ta Git Bash).

Promptu Claude, Codex veya başka bir asistana yapıştırın: skill'i inceleyip sizin için kuracaktır.

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.

Kaynak dosyaları

2 dosya ZIP indir

Kurulumdan önce SKILL.md ve eşlik eden dosyaları inceleyin. Önizleme için dosyaya tıklayın.

SKILL.md içeriği

Yazarın orijinal metni · salt okunur

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-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

Sık sorulan sorular

agent-introspection-debugging skill'i ne yapar?

Yapay zeka ajan hatalarının hata ayıklamasını otomatikleştirir, veri yakalar, sorunları teşhis eder ve tekrarlanabilir analiz için raporlar oluşturur.

agent-introspection-debugging nasıl kurulur?

Bu sayfadaki “Kurulum” bölümünü açın ve ajanınızı seçin — Claude Code, Codex, Cursor veya Gemini CLI: skill klasörünü doğru yere kopyalayan hazır komutlar orada. Ya da “Asistanla” sekmesindeki promptu kopyalayın; asistan skill'i inceleyip kuracaktır.

agent-introspection-debugging hangi yapay zekâlarla çalışır?

Skill'ler düz Markdown talimatlarıdır (Agent Skills formatı), bu yüzden agent-introspection-debugging SKILL.md okuyan her ajanla çalışır: Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode ve diğerleri. Model talimatları yalnızca görev skill açıklamasına uyduğunda yükler.

agent-introspection-debugging ücretsiz mi?

Skill, affaan-m tarafından açık olarak yayımlandı (MIT lisansı). Skill'in kendisi ücretsizdir — yalnızca kullandığınız token'lar için yapay zekâ sağlayıcınıza ödeme yaparsınız.

Skill kurmak güvenli mi?

Skill, ajanınızın izleyeceği talimatlardır; kurmadan önce SKILL.md ve eşlik eden dosyaları okuyun. Skill klasöründeki betikler yalnızca ajanınız çalıştırırsa çalışır — ne yaptığını izleyin ve gereksiz izin vermeyin.

Skill, MCP sunucusu veya eklentiden nasıl farklıdır?

MCP sunucusu veya eklenti modele yeni araçlar verir — çalışıp sonuç döndüren kod. Skill ise bilgi ve süreç verir: göreve nasıl yaklaşılacağı, hangi adımların izleneceği ve iyi sonucun neye benzediği. Sunucu veya API anahtarı gerekmez.

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