Best use case
execute-feedback is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
It is a strong fit for teams already working in Codex.
Execute tests on generated code and iterate until passing
Teams using execute-feedback should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/execute-feedback/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How execute-feedback Compares
| Feature / Agent | execute-feedback | Standard Approach |
|---|---|---|
| Platform Support | Codex | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Execute tests on generated code and iterate until passing
Which AI agents support this skill?
This skill is designed for Codex.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# 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 foundationRelated Skills
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