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 skills add danielmiessler/LifeOS --skill biascheckgit 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/biascheck)<a href="https://agentmods.dev/skills/danielmiessler/lifeos/biascheck"><img src="https://agentmods.dev/badge/skills/danielmiessler/lifeos/biascheck/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/danielmiessler/lifeos/biascheck"><img src="https://agentmods.dev/badge/skills/danielmiessler/lifeos/biascheck.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk warn
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Data Exfiltration · line 23 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 5.1 | $0.00109 | $0.01785 |
| Opus 5.5 | $0.00044 | $0.00714 |
| Sonnet 5.5 | $0.00022 | $0.00357 |
| Haiku 4.5 | $0.00011 | $0.00178 |
Grade A, and why
BiasCheck scanned grade A with 1 finding 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 1mo 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s -X POST http://localhost:31337/notify \ How it starts
The opening of the file, as written. The whole thing — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Customization
Before executing, check for user customizations at:
~/.claude/LIFEOS/USER/CUSTOMIZATIONS/SKILLS/BiasCheck/
If this directory exists, load and apply any PREFERENCES.md or additional reference files found there. These override default behavior. If the directory does not exist, proceed with skill defaults.
BiasCheck
Voice Notification
When executing a workflow, do BOTH:
-
Send voice notification:
curl -s -X POST http://localhost:31337/notify \ -H "Content-Type: application/json" \ -d '{"message": "Running the Check workflow in the BiasCheck skill to audit the source"}' \ > /dev/null 2>&1 & -
Output text notification:
Running the **Check** workflow in the **BiasCheck** skill to audit the source...
What It Does
Runs a three-layer bias audit on any source — a URL, a file path, or raw text. It fetches the content plus any study it cites, then checks (1) biases inside the data, (2) conflicts of interest in the source organization, and (3) distortions the journalism added on top. The output separates what the data actually supports from what got editorialized.
The Problem
Most "this is biased" arguments are vibes — a feeling about a source, with nothing concrete underneath. They're not repeatable and they don't tell you where the distortion lives. The other failure is analyzing an article without ever reaching the study it cites, so you critique the headline and never see that the underlying data was fine (or that it was junk). A fixed taxonomy and a fixed output shape fix both: the analysis is repeatable, the gaps are visible, and every claim ties to a specific tell.
How It Works
The skill operates on three layers:
- The data itself — biases inside the underlying study, paper, or dataset (funding, sampling, instrument design, demand characteristics, self-report distance from behavior, causal inflation, missing benchmark, publication availability)
- The source organization — who paid for or produced the work, what they sell, what conclusion would be inconvenient
- The journalism on top — what the reporter/commentator added: headline-to-source distortion, frame escalation, echo-chain amplification, causal claims layered over correlational data
What ships with it
2 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.
- 1mo ago First seen · 120 lines · 109 tokens per session scan A 8d3d4a2f4224
BiasCheck is a skill published in the GitHub repository danielmiessler/LifeOS (19,573 stars, last pushed 1mo ago), licensed MIT. It adds 109 tokens to every session and 1,785 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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