Agents & OrchestrationSûr100100/100
agent-introspection-debugging
Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports.
ou envoie-le directement à ton agent.
Installer dans ton projet
$ npx arboris-cli install agent-introspection-debuggingContenu à copier
--- name: agent-introspection-debugging description: Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. metadata: origin: ECC --- # 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. | 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: ```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
Colle ce Markdown dans ton agent ou utilise les boutons ci-dessus pour l’écrire dans ton projet.
Ce que fait agent-introspection-debugging
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
Comment utiliser agent-introspection-debugging
1
Copie le prompt
Un clic copie le prompt packagé (ou l'envoie à ton agent).
2
L'agent installe le skill
Il ajoute le SKILL.md et ses ressources à ton projet.
3
Activation automatique
Le skill s'active dès que le contexte correspond.
Déclencheurs pour agent-introspection-debugging
Dis simplement à ton agent quelque chose comme :
Applique le skill agent-introspection-debugging à cette tâche
Utilise agent-introspection-debugging pour améliorer cette implémentation
Passe en revue ce sujet avec agent-introspection-debugging
Skills liés à agent-introspection-debugging
acceleration__microsoft-aibast-agents-library__solutions-deal-progression-manual-skills-4c8a74dab5dd
Compare synthetic acceleration options and clearly separate scenario value from forecast commitments.account-overview__microsoft-aibast-agents-library__solutions-account-intelligence-manual-skills-3546ce8f6420
Create a synthetic Acme account overview and identify evidence that merits seller attention.acroform-writer__microsoft-cat-agent-skills__submissions-e44ef69423b5
Use this skill whenever the user wants values written INTO an existing PDF form that lives in the connected SharePoint knowledge source — filling out, completing, populating, or submitting a fillable PDF (application, intake sheet, contract, government form) from data they supply, a spreadsheet row, or the conversation. Triggers include "fill out the job application form", "complete the intake form for [person]", "populate our standard NDA template", and "make this ready to send / non-editable" (flatten). The source form should be found via knowledge search; only ask the user to upload it if knowledge search turns up nothing plausible. different task), and do NOT use it on scanned/image-only PDFs that have no real fillable fields — those need OCR or manual overlay instead, which this skill explicitly detects and reports rather than silently failing.