Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/danielmiessler/lifeos/loopnpx skills add danielmiessler/LifeOS --skill loopgit clone --depth 1 https://github.com/danielmiessler/LifeOSWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00037 | $0.00988 |
| Opus 5 | $0.00018 | $0.00494 |
| Sonnet 5 | $0.00007 | $0.00198 |
| Haiku 4.5 | $0.00004 | $0.00099 |
Grade A, and why
Loop scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/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 |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 81 lines · 37 tokens per session scan A fe047b46eb63
Loop is a skill published in the GitHub repository danielmiessler/LifeOS (18,798 stars, last pushed 18d ago), licensed MIT. It adds 37 tokens to every session and 988 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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