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/optimizenpx skills add danielmiessler/LifeOS --skill optimizegit 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 | $0.00052 | $0.02012 |
| Opus 5 | $0.00026 | $0.01006 |
| Sonnet 5 | $0.00010 | $0.00402 |
| Haiku 4.5 | $0.00005 | $0.00201 |
Grade A, and why
Optimize 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 yesterday.
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 — 181 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/optimize — Autonomous Optimization v2
What It Does
Runs an autonomous optimization loop against any target. The agent modifies the target, measures the result, keeps improvements, discards failures, and repeats until it stops climbing. Two modes: metric mode for code targets that produce a number (latency, bundle size), and eval mode for skills, prompts, or agents judged by LLM-as-judge binary evals.
The Problem
Tuning a thing for a measurable outcome is slow, boring, manual work. You change a file, run the measurement, eyeball whether it got better, keep or revert, then do it again — dozens of times. People give up after a few rounds and settle for "good enough" far short of the real ceiling. The targets without a clean number (a skill's quality, a prompt's effectiveness) are worse: there's no easy way to tell if a change actually helped. This skill runs that whole loop for you and only keeps changes that measurably win.
How It Works
Two modes drive the same hill-climb loop:
- Metric mode — code targets with a shell command that produces a number (the original).
- Eval mode — skills, prompts, agents, or any text target judged by LLM-as-judge binary evals.
Inspired by Karpathy's autoresearch and extended with LLM-as-judge evaluation.
Invocation
Metric Mode (code targets)
/optimize --metric "lighthouse_score" --higher-is-better \
--measure "npx lighthouse http://localhost:3000 --output=json" \
--extract "jq '.categories.performance.score * 100' lighthouse.json" \
--files "src/**/*.tsx,src/**/*.css" \
--budget 120
/optimize --resume # Resume a previous optimization loop
/optimize --status # Show results summary from last/current run
Eval Mode (skill/prompt/agent targets)
/optimize --target "~/.claude/skills/ExtractWisdom"
/optimize --target "~/.claude/skills/Research/Workflows/QuickResearch.md"
/optimize --target "prompts/my-prompt.md"
/optimize --target "~/.claude/skills/ExtractWisdom" --max-experiments 20
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.
- yesterday First seen · 181 lines · 52 tokens per session scan A 657c554ed67b
Optimize is a skill published in the GitHub repository danielmiessler/LifeOS (18,798 stars, last pushed 17d ago), licensed MIT. It adds 52 tokens to every session and 2,012 once invoked, about $0.0003 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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