| name | Loop |
| description | Iterative improvement loop — revisit and refine a target across multiple Algorithm cycles toward an ideal state. USE WHEN loop, iterate, refine, improve iteratively, multiple passes, keep improving, loop mode, revisit, rework. |
| disable-model-invocation | true |
| effort | medium |
/loop — Iterative Improvement
Run the Algorithm in mode: loop — multiple full Algorithm cycles on the same target, each iteration building on the last. Unlike /optimize (autonomous mutation loop), /loop runs full Algorithm passes with human review between iterations.
Invocation
/loop --target "path/to/target" --iterations 5
/loop --target "~/.claude/skills/Art/Workflows/TechnicalDiagrams.md" --goal "make diagrams more consistent"
/loop --resume # Resume a previous loop
/loop --status # Show iteration history
What Happens
Each iteration is a full Algorithm cycle (OBSERVE → THINK → PLAN → BUILD → EXECUTE → VERIFY → LEARN) with:
- ISC criteria that evolve between iterations
- Each cycle's LEARN phase informs the next cycle's OBSERVE
- ISA tracks iteration count and cumulative improvements
- Human approves/redirects between iterations
Arguments
| Argument | Required | Default | Description |
|---|
--target PATH | yes | | What to improve (file, directory, skill) |
--goal TEXT | | inferred | What "better" means for this target |
--iterations N | | 3 | Maximum number of Algorithm cycles |
--resume | | | Resume a previous loop |
--status | | | Show iteration history |
--autoresearch | | off | Opt-in autonomous mode — see below |
Algorithm Integration
Sets mode: loop in ISA frontmatter. The iteration field tracks cycle count. Each cycle re-enters the Algorithm with accumulated context from prior iterations.
Autoresearch Mode (opt-in)
--autoresearch switches /loop from supervised multi-pass improvement to autonomous iteration, borrowing three patterns from pi-autoresearch (davebcn87, MIT):
- No human review between cycles — each iteration's LEARN feeds directly into the next OBSERVE. Cycle continues until
--iterations reached, target met, or explicit interrupt.
- Dead-ends ledger — ISA maintains a
## Dead Ends section. Every failed iteration appends one line with the rejected approach and reason. Resumes read this to avoid retrying rejected paths.
- MAD confidence on iteration score — if the target has a measurable score, compute
|delta|/MAD(iteration_scores) per cycle. Flag red (<1.0×) iterations as noise-floor and log marginal; do not update baseline. See PAI/ALGORITHM/optimize-loop.md → Confidence Gating.
Invocation:
/loop --target "path" --goal "X" --iterations 20 --autoresearch
Default /loop behavior is unchanged — autoresearch is opt-in only. Intended for overnight runs on targets where human-in-the-loop review between cycles is too slow.
Examples
/loop --target "~/.claude/skills/Research" --goal "improve output quality" --iterations 5
/loop --target "prompts/summarize.md" --goal "more concise, less filler"
Gotchas
- Loop runs multiple full Algorithm cycles. Each cycle is a complete OBSERVE→LEARN pass. This is expensive in time and tokens.
- Set a clear exit condition. Without one, loops can run indefinitely.
- Human review happens between cycles. Don't skip the review step — it's the feedback mechanism.
