execute-feedback

execute-feedback is a skill for Claude Code, Codex from jmagly/aiwg. It costs 12 tokens per session (645 once invoked), scanned A, original, MIT.

An automated test-and-fix loop for generated code. TDD means developing with tests as a guide; this add-on instead describes running existing or generated tests, analysing failures, applying fixes, and trying again.

In plain words
What is it for?
Detecting the project’s test framework, running tests, explaining failures, fixing the related code, retrying, and recording successful results.
Why use it?
It removes the need to stop after the first failed test and manually repeat the same debugging cycle.

Skill for Claude CodeCodex

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/jmagly/aiwg/execute-feedback
Any agent
npx skills add jmagly/aiwg --skill execute-feedback
Clone the repo
git clone --depth 1 https://github.com/jmagly/aiwg

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 execute-feedback

README.md
[![agentmods](https://agentmods.dev/badge/skills/jmagly/aiwg/execute-feedback.svg)](https://agentmods.dev/skills/jmagly/aiwg/execute-feedback)
Your own site
<a href="https://agentmods.dev/skills/jmagly/aiwg/execute-feedback"><img src="https://agentmods.dev/badge/skills/jmagly/aiwg/execute-feedback.svg" alt="Measured on agentmods" height="20"></a>
Per session 12 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 645 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found 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 $0.00012 $0.00645
Opus 5 $0.00006 $0.00322
Sonnet 5 $0.00002 $0.00129
Haiku 4.5 $0.00001 $0.00064

Measured 4d ago against content hash 45a332467383, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

execute-feedback 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 4d 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.

agentic/code/addons/agent-loop/skills/execute-feedback/SKILL.md · 72 lines

How it starts

The opening of the file, as written. The whole thing — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Execute Feedback Command

Run executable feedback loop on generated code: execute tests, analyze failures, fix, and retry.

Instructions

When invoked, perform the executable feedback loop per REF-013 MetaGPT:

  1. Identify Target

    • Load the specified file or recently modified code files
    • Determine test framework (jest, pytest, cargo test, go test, etc.)
    • Find existing tests or generate test stubs if none exist
  2. Execute Tests

    • Run the specified test command (or auto-detect)
    • Capture full output (stdout, stderr, exit code)
    • Parse test results: passed, failed, errors, skipped
  3. Analyze Failures

    • For each failing test:
      • Extract error type and message
      • Identify root cause (null check, type error, logic error, etc.)
      • Map to source code location
    • Check debug memory for similar past failures
  4. Apply Fixes

    • Generate targeted fix based on root cause analysis
    • Apply fix to source code
    • Increment attempt counter
  5. Re-Execute

    • Run tests again after fix
    • Compare results to previous attempt
    • If all pass: record success in debug memory, return
    • If still failing: repeat from step 3
  6. Escalate if Needed

    • After max attempts (default: 3), escalate to human
    • Include: all test results, failure analyses, fix attempts
    • Save debug memory session
  7. Update Debug Memory

    • Record execution session in .aiwg/ralph/debug-memory/sessions/
    • Extract learned patterns to .aiwg/ralph/debug-memory/patterns/
    • Update success metrics

Arguments

  • [file-path] - Source file to test (default: recently modified files)
  • --test-command [cmd] - Test command to run (default: auto-detect)
  • --max-attempts [n] - Maximum fix attempts (default: 3)
  • --coverage [%] - Minimum coverage target (default: 80)
  • --no-fix - Run tests only, report without fixing
  • --verbose - Show full test output

References

  • @$AIWG_ROOT/agentic/code/frameworks/sdlc-complete/rules/executable-feedback.md - Executable feedback rules
  • @$AIWG_ROOT/agentic/code/addons/ralph/docs/executable-feedback-guide.md - Implementation guide
  • @$AIWG_ROOT/agentic/code/addons/ralph/schemas/debug-memory.yaml - Debug memory schema
  • @$AIWG_ROOT/agentic/code/frameworks/sdlc-complete/schemas/flows/executable-feedback.yaml - Workflow schema
  • @.aiwg/research/findings/REF-013-metagpt.md - Research foundation

Read the full file on GitHub · 72 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. 4d ago First seen · 72 lines · 12 tokens per session scan A 45a332467383

Subscribe to this mod's changes

execute-feedback is a skill published in the GitHub repository jmagly/aiwg (208 stars, last pushed today), licensed MIT. It adds 12 tokens to every session and 645 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.