Development
Skill
/execute-feedback
Execute tests on generated code and iterate until passing
Claude Code
$ npx -y skills add jmagly/aiwg --skill execute-feedback --agent claude-code
Codex
$ npx skills add jmagly/aiwg --skill execute-feedback -a codex -y
Cursor
$ npx skills add jmagly/aiwg --skill execute-feedback -a cursor -y
Gemini CLI
$ npx skills add jmagly/aiwg --skill execute-feedback -a gemini-cli -y
GitHub Copilot
$ npx skills add jmagly/aiwg --skill execute-feedback -a github-copilot -y
OpenCode
$ npx skills add jmagly/aiwg --skill execute-feedback -a opencode -y
Windsurf
$ npx skills add jmagly/aiwg --skill execute-feedback -a windsurf -y
SKILL.md
execute-feedback.SKILL.mdnamespace: aiwg
name: execute-feedback
platforms: [all]
description: Execute tests on generated code and iterate until passing
commandHint:
category: code-quality
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/agent-loop/docs/executable-feedback-guide.md - Implementation guide
- @$AIWG_ROOT/agentic/code/addons/agent-loop/schemas/debug-memory.yaml - Debug memory schema
- @$AIWG_ROOT/agentic/code/frameworks/sdlc-complete/schemas/flows/executable-feedback.yaml - Workflow schema
- @.aiwg/research/findings/REF-013-metagpt.md - Research foundation
Read more
namespace: aiwg name: execute-feedback platforms: [all] description: Execute tests on generated code and iterate until passing commandHint: category: code-quality
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/agent-loop/docs/executable-feedback-guide.md - Implementation guide
- @$AIWG_ROOT/agentic/code/addons/agent-loop/schemas/debug-memory.yaml - Debug memory schema
- @$AIWG_ROOT/agentic/code/frameworks/sdlc-complete/schemas/flows/executable-feedback.yaml - Workflow schema
- @.aiwg/research/findings/REF-013-metagpt.md - Research foundation
Stats
229
Stars
36
Forks
Active
Maintenance
TypeScript
Language
MIT
License
Last commit
Created
Ships withaiwg
Reusable project context and specialist workflows for the AI tools you already use. Plan software, coordinate specialist reviews, prepare campaigns, investigate incidents, organize research, curate media, and maintain operational knowledge.
229200 skills199 agents
Stars on GitHub
Momentum
- stars in total
- 229
Other skills on aiwg.
More skills from other plugins.
Auto-invoked
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, modifying behavior, or…
@obraInstall Skillask-matt
Ask which skill or flow fits your situation. A router over the skills in this repo.
@mattpocockInstall SkillAuto-invoked
Questions about execute-feedback.
- Which coding agents can use execute-feedback?
- execute-feedback installs in Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and Windsurf with the skills CLI, using each agent's flag shown on this page. Flowy's auto-invocation, which fires a skill when your prompt matches, runs in Claude Code only.