/loop — Iterative Improvement
What It Does
/loop runs the Algorithm as a loop — multiple full Algorithm cycles on the same target, each iteration building on the last. By default a human reviews and redirects between iterations. Unlike /optimize (an autonomous mutation loop), /loop runs full Algorithm passes with that human review in the seam.
The Problem
Some work doesn't finish in one pass. A skill, a prompt, a diagram, a piece of writing gets meaningfully better each time you run a full cycle on it — but only if each cycle remembers what the last one learned and what it already tried. Run the cycles by hand and you lose that thread: you re-explore dead ends, forget which approaches got rejected, and have no record of whether the score actually moved. /loop carries ISC criteria and a dead-ends ledger across iterations so each pass starts from where the last one ended.
How It Works
Each iteration is a full Algorithm cycle (OBSERVE → LEARN). The LEARN phase of one cycle feeds the OBSERVE phase of the next, the ISA tracks iteration count and cumulative improvements, and a human approves or redirects between iterations unless autoresearch mode is enabled.
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 (articulate → climb → verify → learn) with:
- ISC criteria that evolve between iterations
- Each cycle's learnings inform the next cycle's scaffold
- 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
The iteration field tracks cycle count. (mode: is retired — never write it.) 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
--iterationsreached, target met, or explicit interrupt. - Dead-ends ledger — ISA maintains a
## Dead Endssection. 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 logmarginal; do not update baseline. SeeLIFEOS/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.
