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Start an autonomous implementation loop with test verification
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Based on SOC occupation classification
| name | wrought-implement |
| description | Start an autonomous implementation loop with test verification |
| disable-model-invocation | false |
| wrought | {"version":"1.0","platforms":{"claude-code":{"disable-model-invocation":false}}} |
You are starting a Ralph Wiggum inner loop for implementation. A Stop hook will automatically verify your work after each attempt to stop.
Read .claude/wrought-loop-state.json. If it exists and active is true:
/cancel-wrought-loop first, or continue the existing loop."The user should provide a plan path. If not provided:
docs/plans/*.mdRead the .wrought marker file and extract:
verifier_type — if none, ask the user: "What verifier command should I use? (e.g., uv run pytest tests/ -q)"verifier_test_path — default test pathverifier_max_loops — iteration budgetverifier_timeout — per-run timeout in secondsWrite .claude/wrought-loop-state.json:
{
"active": true,
"preset": "wrought-implement",
"finding_id": "{from plan or Findings Tracker}",
"tracker_path": "{path to active FINDINGS_TRACKER.md}",
"verifier_command": "{verifier command}",
"max_iterations": {from marker},
"current_iteration": 0,
"started_at": "{ISO timestamp}",
"history": []
}
When the loop completes (verifier passes):
.claude/wrought-loop-state.json to summarize iterationsdocs/capsules/{finding_id}/"/forge-review --scope=diff to verify code quality."Strategic system analysis for health assessment, pattern recognition, component deep dives, risk assessment, and architecture review. Use for proactive system understanding beyond reactive incident response.
Transform a design into an implementation spec. Creates a blueprint document and an implementation prompt for /plan mode. Use after /design or /research.
Cancel the active Wrought implementation/RCA loop
Interactive design analysis for architecture decisions. Combines codebase analysis, documentation review, and external research to provide evidence-based recommendations. Use for tradeoff analysis, pattern evaluation, migration planning, or design validation.
Log a proactive discovery that needs attention. Creates a factual record of WHAT was found with classification and a Findings Tracker. Use for analysis discoveries, review findings, or inspection results.
Deep multi-agent code review with algorithmic complexity analysis, data structure review, paradigm enforcement, and efficiency analysis. Complements /simplify with deeper dimensions.