agent-introspection-debugging

agent-introspection-debugging is a skill for Claude Code, Codex from ufy2024/AuC. It costs 28 tokens per session (1,233 once invoked), scanned A, a copy of agent-introspection-debugging, MIT.

A step-by-step method for finding and recovering from failures in AI agent runs. It examines what went wrong before trying another fix.

In plain words
What is it for?
Use it when an agent keeps failing, repeats the same actions, reaches tool limits, loses track of the task, or encounters a possibly recoverable environment problem.
Why use it?
It helps prevent repeated retries, tool loops, wasted tokens, and growing confusion about the task or system state.

Skill for Claude CodeCodex

About the project

AuC is a Python framework for running a single AI agent with an asynchronous, pluggable reasoning loop, language-model adapters, permission levels, and observable events. It is used to build coding and conversational agents with tools, security checks, web interfaces, background jobs, evaluations, and isolated execution. The catalogue entries are skills for extending its agent workflow.

ufy2024/AuC · 1,091 stars · on GitHub

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/ufy2024/auc/agent-introspection-debugging
Any agent
npx skills add ufy2024/AuC --skill agent-introspection-debugging
Clone the repo
git clone --depth 1 https://github.com/ufy2024/AuC

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for agent-introspection-debugging

README.md
[![agentmods](https://agentmods.dev/badge/skills/ufy2024/auc/agent-introspection-debugging.svg)](https://agentmods.dev/skills/ufy2024/auc/agent-introspection-debugging)
Your own site
<a href="https://agentmods.dev/skills/ufy2024/auc/agent-introspection-debugging"><img src="https://agentmods.dev/badge/skills/ufy2024/auc/agent-introspection-debugging.svg" alt="Measured on agentmods" height="20"></a>
Per session 28 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,233 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 95% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00028 $0.01233
Opus 5 $0.00014 $0.00616
Sonnet 5 $0.00006 $0.00247
Haiku 4.5 $0.00003 $0.00123

Measured 5d ago against content hash 178868b7ae59, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

agent-introspection-debugging scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

95% identical to agent-introspection-debugging — 25 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

auc/skill_library/bundled/agent-introspection-debugging/SKILL.md · 174 lines

How it starts

The opening of the file, as written. The whole thing — 174 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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

Read the full file on GitHub · 174 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 5d ago First seen · 174 lines · 28 tokens per session scan A 178868b7ae59

Subscribe to this mod's changes

agent-introspection-debugging is a skill published in the GitHub repository ufy2024/AuC (1,091 stars, last pushed 1mo ago), licensed MIT. It adds 28 tokens to every session and 1,233 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to agent-introspection-debugging, differing in 25 lines, and is treated as a copy.

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