Skill
agent-loop-ext
Crash-resilient external agent loop with state persistence and CI/CD integration
Development
Skill
/execute-feedback
Execute tests on generated code and iterate until passing
Install
$ npx -y skills add jmagly/aiwg --skill execute-feedback --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/execute-feedback
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The summary Claude sees to decide when to auto-load this skill.
Execute tests on generated code and iterate until passing
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/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 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/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
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.
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Maintenance
TypeScript
Language
MIT
License
1d ago
Last commit
1y ago
Created
Repo: jmagly/aiwg
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