LifeOS is an AI-assisted personal operating system that records a person’s goals, values, context, and current situation to help move them toward a desired future state. It supports work such as building applications, starting businesses, and creative projects. Its catalogue entries extend the system through skills, hooks, agents, and commands.
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/detectainpx skills add danielmiessler/LifeOS --skill detectaigit clone --depth 1 https://github.com/danielmiessler/LifeOSWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/danielmiessler/lifeos/detectai)<a href="https://agentmods.dev/skills/danielmiessler/lifeos/detectai"><img src="https://agentmods.dev/badge/skills/danielmiessler/lifeos/detectai.svg" alt="Measured on agentmods" height="20"></a>What 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 | $0.00208 | $0.01735 |
| Opus 5 | $0.00104 | $0.00868 |
| Sonnet 5 | $0.00042 | $0.00347 |
| Haiku 4.5 | $0.00021 | $0.00173 |
Grade A, and why
DetectAI 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 5d 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 — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DetectAI
What It Does
Answers two questions — how much does this read as machine-generated? and does it carry an embedded mark? — with four independent measures:
- Heuristic audit. Flags known AI tells (inflated vocabulary, the "not X, it's Y" tic, recycled transitions, uniform rhythm) against a severity-tiered pattern catalog. Free, instant, and it explains why each flag fired.
- Statistical signals. A deterministic pass (
LIFEOS/TOOLS/StatSignals.ts) measuring the keyless distributional tells the research literature rates real: n-gram entropy, type-token ratio, repetition structure — plus the weak-alone folklore tier (burstiness, paragraph uniformity, function-word stylometry), each labeled with its reliability. Features, never verdicts (arXiv:2310.15264: paraphrase degrades every keyless statistic). Free, no key. - Empirical score. Runs the text through the Pangram detection model and returns a real probability — AI% / AI-assisted% / human% — plus per-segment counts. Costs money, needs an API key, and doesn't care what your word list says.
- Watermark scan. A keyless, deterministic pass for character-level covert channels — invisible chars, variation-selector/Tags-block steganography, homoglyphs, bidi, odd whitespace. Catches embedded marks that live in the bytes; by design it cannot read sampling-time statistical watermarks (SynthID, Kirchenbauer, Anthropic's announced mark), which are key-gated. Free, no key.
The measures disagree often, and that disagreement is the useful part. Text can clear every pattern on the list and still score 100% AI, which tells you the tells are structural, not lexical — and a watermark hit is bytes-level proof regardless of what the other two say.
The Problem
"Does this sound like AI?" gets answered by vibes, and vibes are wrong in both directions. Heuristic word-lists flag legitimate writing and miss AI text that avoided the obvious words. Detector scores look authoritative but saturate — Pangram will confidently call a short human paragraph 100% AI. Neither measure alone is trustworthy, and a raw number with no baseline is close to meaningless.
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 5d ago First seen · 75 lines · 208 tokens per session scan A 78f565cb1310
DetectAI is a skill published in the GitHub repository danielmiessler/LifeOS (18,872 stars, last pushed 20d ago), licensed MIT. It adds 208 tokens to every session and 1,735 once invoked, about $0.0010 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.
Other skills, from other repositories
echo-plugins
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browser-spa-framework
Architecture/conventions for the Echo browser SPA: Go stdlib server :3740, Vite-built TS+JS SPA (embedded web/dist), JSON envelope, WebSocket hub, chat tool loop, shared appdata echo.json, internal/tools registry, tool-generated media transport (Phase 0/1), and the file-browser image/video/audio preview surface…
global-notifications
How Echo surfaces cross-surface/cross-tab alerts to the user: global WebSocket broadcasts (chatcompleted, planquestionsawaiting) mirrored by frontend notification modules that play sounds, show OS/browser Notifications, and deep-link on click.
writing-tests
Write unit tests, component tests, and integration tests for AiderDesk using Vitest and React Testing Library. Use when creating new tests, adding test coverage, configuring mocks, setting up test files, or debugging failing tests.
reddit-snipe
Reddit 截流。在 coding agent / Claude Code / AI dev tool 的高互动帖下，以 pikiloom builder 身份自动发现→起草→自我批判→护栏→（按 posture）发布英文 peer 评论，并记录+度量。English-first。话术统一来自 promo/pitch.md；可由 promo/orchestrate.md 无人值守驱动。.
promote
GitHub 截流。在同领域项目（local coding agent ↔ IM / mobile / remote console）的 feature-request issue 下，自动发现→起草→自我批判→护栏→（按 posture）发布 pikiloom 回复，并记录+度量。话术统一来自 promo/pitch.md；可由 promo/orchestrate.md 无人值守驱动。.