How do I install the Execute Feedback skill?

Run npx skillmds@latest add jmagly-ai-writing-guide/execute-feedback in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

What does the Execute Feedback skill do?

Execute tests on generated code and iterate until passing It is listed under AI & ML on SkillMD.

Is Execute Feedback safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: docs only. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Execute Feedback?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Execute Feedback free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Execute Feedback?

jmagly (@jmagly-ai-writing-guide) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Execute Feedback by jmagly-ai-writing-guide

Name: Execute Feedback
Rating: 4.2 (5 reviews)
Author: jmagly

File contents

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:

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
Execute Tests
- Run the specified test command (or auto-detect)
- Capture full output (stdout, stderr, exit code)
- Parse test results: passed, failed, errors, skipped
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
Apply Fixes
- Generate targeted fix based on root cause analysis
- Apply fix to source code
- Increment attempt counter
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
Escalate if Needed
- After max attempts (default: 3), escalate to human
- Include: all test results, failure analyses, fix attempts
- Save debug memory session
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

---name: execute-feedbackdescription: Execute tests on generated code and iterate until passing---# Execute Feedback CommandRun executable feedback loop on generated code: execute tests, analyze failures, fix, and retry.## InstructionsWhen 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 exist2. **Execute Tests** - Run the specified test command (or auto-detect) - Capture full output (stdout, stderr, exit code) - Parse test results: passed, failed, errors, skipped3. **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 failures4. **Apply Fixes** - Generate targeted fix based on root cause analysis - Apply fix to source code - Increment attempt counter5. **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 36. **Escalate if Needed** - After max attempts (default: 3), escalate to human - Include: all test results, failure analyses, fix attempts - Save debug memory session7. **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

Frequently asked questions

Execute Feedback