Clade goal-driven autonomous improvement loop (Blueprint architecture — deterministic pre/post phases + LLM supervisor/worker nodes, converges when goal met or max-iter hit). NOT the Claude Code built-in /loop (which polls a prompt on an interval like `/loop 5m /foo`) — if the user wants interval polling, route to the built-in.
Scanned 6/11/2026
Install via CLI
openskills install shenxingy/Clade---
name: loop
description: Clade goal-driven autonomous improvement loop (Blueprint architecture — deterministic pre/post phases + LLM supervisor/worker nodes, converges when goal met or max-iter hit). NOT the Claude Code built-in /loop (which polls a prompt on an interval like `/loop 5m /foo`) — if the user wants interval polling, route to the built-in.
when_to_use: "run loop with goal file, autonomous supervisor+worker loop, keep fixing until tests pass in background workers, iterate until converged, goal.md, Blueprint loop, 自动循环, run until converged — NOT for TODO.md tasks (use /batch-tasks), NOT for task decomposition (use /orchestrate), NOT for in-session iteration in the current context (use /iloop), NOT for interval polling a prompt (that's the CC built-in /loop)"
argument-hint: 'GOAL_FILE [--model haiku|sonnet|opus] [--worker-model MODEL] [--max-iter N] [--max-workers N] [--dry-run] [--status] [--stop] [--resume]'
user_invocable: true
---
# Loop Skill
Runs an autonomous improvement loop driven by a **goal file** (ideal end state description).
## Architecture
```
You write: goal.md (what the system should do — NOT a task list)
↓
PRE (deterministic):
pre_flight — goal exists, no blockers
hydrate_context — git log + status
parse_todo — extract unchecked items from goal file
↓
LLM CORE:
Supervisor — plans 1–4 tasks (output: JSON task array)
Workers — execute ALL tasks IN PARALLEL
↓
POST (deterministic + conditional LLM):
syntax_check — validate all changed files
fix_syntax — [LLM] one attempt to fix failures
test_sample — run verify_cmd from CLAUDE.md
mid-iter fix — [LLM] if test fails → fix → re-test (Stripe pattern)
commit_changes — commit all worker output
↓
Deterministic convergence check:
- remaining unchecked items = 0 → CONVERGED
- max iterations hit → exit
- stuck (N× no-commits) → exit
↓
Repeat until CONVERGED or max iterations
```
## What you write (goal.md)
```markdown
# Goal: Improve orchestrator loop mode
## Requirements
- Oracle rejection re-queues task with rejection reason as context
- Worker context budget warning auto-injected at 80%
- Workers get AGENTS.md prepended automatically
## Success criteria
- python -m py_compile server.py passes
- Existing features unaffected
```
The **supervisor** does the task breakdown — not you.
## Usage
```
/loop goal.md # Start loop (sonnet, max 10 iter, 4 parallel workers)
/loop goal.md --model haiku # Cheaper/faster supervisor+workers
/loop goal.md --max-iter 3 # Short run to test
/loop goal.md --max-workers 2 # Limit parallel workers
/loop goal.md --max-consecutive-failures 5 # Stop after 5 consecutive worker failures (default: 3)
/loop --status # Check current loop progress
/loop --stop # Stop loop after current iteration
/loop --dry-run goal.md # Preview without running
/loop --resume goal.md # Resume interrupted loop
```
## After convergence
Run `/commit` to push any remaining uncommitted changes from workers.
Run `/review` to verify all behavior anchors still pass after autonomous changes.
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