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Agent skill

Agent Introspection Debugging

by affaan-m in affaan-m/ECC

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports.

MITAuto-check passedDevelopment

Install Agent Introspection Debugging

skills CLI
$ npx skills add affaan-m/ECC --skill agent-introspection-debugging -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install affaan-m/ECC agent-introspection-debugging --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/agent-introspection-debugging .claude/skills/agent-introspection-debugging && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
agent-introspection-debugging
GitHub stars
276k
Used in
2 other repos
Token cost
~1.4k tokens
SKILL.md length
665 words
Files
1
Skills in repo
673
Repo updated
First seen
Licence
MIT

At a glance

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports.

  • Works in 4 steps: Failure Capture → Root-Cause Diagnosis → Contained Recovery → …
  • An agent run fails and you need a reproducible diagnosis instead of a retry
  • SKILL.md covers When to Activate, Scope Boundaries, Four-Phase Loop and Recovery Heuristics, plus 2 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Agent Introspection Debugging is an agent skill from affaan-m/ECC. 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.

Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Development, covering Debugging. The repository describes itself as: The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond. The licence is MIT.

When your agent uses it

  • An agent run fails and you need a reproducible diagnosis instead of a retry
  • Tasks that involve Debugging

Example prompts

  • “/agent-introspection-debugging”

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Failure Capture
  2. Root-Cause Diagnosis
  3. Contained Recovery
  4. Introspection Report

What it can do on your machine

Read from SKILL.md and the folder at commit ef648e0. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Agent Introspection Debugging loads about 1.4k tokens when it runs. Until then it costs about 61 tokens; SKILL.md has 665 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~61
When it runs · the whole SKILL.md, loaded when a task matches
~1.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from affaan-m/ECC at commit ef648e0, republished under its MIT licence (© affaan-m). 665 words, ~1,403 tokens.

Download SKILL.mdSave it as .claude/skills/agent-introspection-debugging/SKILL.md (or your agent's skills folder).
name
agent-introspection-debugging
description
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.
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.

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?
Show full SKILL.md (281 more words)Show less
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

© affaan-m, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/agent-introspection-debugging of affaan-m/ECC.

Open the folder on GitHubat commit ef648e0

Used in 2 other repositories

We found 4 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in affaan-m/ECC, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Agent Introspection Debugging next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Agent Introspection Debugging compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Agent Introspection Debugging this skillaffaan-m/ECC276k2 repos~1.4kAutomated safety check: PassMIT
Trellis Session Insightmindfold-ai/Trellis15k4 repos~1.7kAutomated safety check: PassAGPL-3.0
Native Data FetchingCherryHQ/cherry-studio-app4k6 repos~2.9kAutomated safety check: NotesMIT
Debugging Executionsn8n-io/n8n207k—~2.6kAutomated safety check: PassCustom licence
Aoti Debugpytorch/pytorch104k1 repos~1.7kAutomated safety check: PassCustom licence
Herdr Throwaway Reproductionherdrdev/herdr43k—~2.4kAutomated safety check: PassApache-2.0

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Categories

Questions about Agent Introspection Debugging

What does Agent Introspection Debugging do?

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Agent Introspection Debugging is an agent skill from affaan-m/ECC. Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports.

When should I use Agent Introspection Debugging?

Agent Introspection Debugging fits situations like: an agent run fails and you need a reproducible diagnosis instead of a retry; tasks that involve Debugging.

How do I install Agent Introspection Debugging in Claude Code?

Run `npx skills add affaan-m/ECC --skill agent-introspection-debugging -a claude-code`. Or copy the skill folder (skills/agent-introspection-debugging in affaan-m/ECC) into .claude/skills/agent-introspection-debugging in your project. Claude Code loads it when a task matches its description.

How do I install Agent Introspection Debugging in Codex?

Run `npx skills add affaan-m/ECC --skill agent-introspection-debugging -a codex`. Or copy the skill folder (skills/agent-introspection-debugging in affaan-m/ECC) into .agents/skills/agent-introspection-debugging in your project. Codex loads it when a task matches its description.

Can I use Agent Introspection Debugging in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add affaan-m/ECC --skill agent-introspection-debugging -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/agent-introspection-debugging, .gemini/skills/agent-introspection-debugging, .github/skills/agent-introspection-debugging and .opencode/skills/agent-introspection-debugging in your project.

What does Agent Introspection Debugging need to run?

SKILL.md names no scripts, command-line tools or credentials: Agent Introspection Debugging is instructions for the agent only.

Does Agent Introspection Debugging access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Agent Introspection Debugging safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Agent Introspection Debugging use?

Agent Introspection Debugging is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Agent Introspection Debugging use?

About 1.4k tokens (SKILL.md is roughly 5.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Agent Introspection Debugging?

Skills that share tags, products or a category with Agent Introspection Debugging: Trellis Session Insight (mindfold-ai/Trellis, 15k stars), Native Data Fetching (CherryHQ/cherry-studio-app, 4k stars), Debugging Executions (n8n-io/n8n, 207k stars) and Aoti Debug (pytorch/pytorch, 104k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Agent Introspection Debugging?

affaan-m (a GitHub user) maintains it in affaan-m/ECC, which has 275,546 GitHub stars. The repository holds 673 skills in this directory. The repository was last updated on October 5, 2026.

Source: affaan-m/ECC on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.