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 wastewatercagit 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/wastewaterca)<a href="https://agentmods.dev/skills/danielmiessler/lifeos/wastewaterca"><img src="https://agentmods.dev/badge/skills/danielmiessler/lifeos/wastewaterca/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/wastewaterca"><img src="https://agentmods.dev/badge/skills/danielmiessler/lifeos/wastewaterca.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00135 | $0.01291 |
| Opus 5.5 | $0.00054 | $0.00516 |
| Sonnet 5.5 | $0.00027 | $0.00258 |
| Haiku 4.5 | $0.00014 | $0.00129 |
Grade A, and why
WastewaterCA 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 4d 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 — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
California Wastewater Data Tracking
Pulls California population-level pathogen surveillance from two public sources: CDC's National Wastewater Surveillance System (a viral activity level per treatment plant for SARS-CoV-2, Influenza A and RSV) and CDPH's sample-level data on the CHHS Open Data Portal (concentrations for every pathogen target CDPH tests). Answers cover California statewide and, when configured, one region of counties.
Clinical case counts miss asymptomatic, unreported and untested infections and trail community spread by days. Wastewater catches everything that enters the sewer regardless of testing behavior, so it leads clinical signals by 4–7 days. Both sources publish on their own schedules; this skill pulls the latest figures each run and turns them into a level and trend you can act on before booking travel or an event.
Ideal State
- Every answer names the pathogen, the geography it covers (the configured region by name, or California statewide) and the latest data date, so no figure reads as real-time. The date comes from the dataset's own week or sample date.
- A COVID answer gives CDC's own activity level per site and the week-over-week direction; a panel answer covers every pathogen target the CDPH data carries in the past year.
- Figures come from a pull made in this run through
Tools/Wastewater.ts, with the dataset named so the value can be checked.
Intent Routing
| Intent | Trigger | File |
|---|---|---|
| GetCovidData | "COVID numbers", "COVID status", "COVID data", viral load, activity level, trend before events/travel; default when ambiguous | Intents/GetCovidData.md |
| GetWastewaterData | "wastewater", "respiratory viruses", "flu", "RSV", "norovirus", multi-pathogen panel | Intents/GetWastewaterData.md |
| File | Read when |
|---|---|
Tools/Wastewater.help.md |
flags, exit codes, what each command reads |
Setup.md |
first-time setup, the region config, the proof command |
What ships with it
5 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.
- 4d ago First seen · 76 lines · 135 tokens per session scan A b0ca96cf34fe
WastewaterCA is a skill published in the GitHub repository danielmiessler/LifeOS (19,661 stars, last pushed 4d ago), licensed MIT. It adds 135 tokens to every session and 1,291 once invoked, about $0.0005 per session on Opus 5.5. 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 2026-10-28.
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long-running-work
Carrying complex coding and project work through commands that take minutes or hours: evals, benchmarks, full builds and test suites, migrations, servers, multi-round runs. Use when a command may run past a few minutes, timed out once, or when the task spans several rounds of run, read results, fix, run again.
i-have-adhd
Action-first, zero-filler replies for a reader who loses the thread in prose. Use when the user asks for terse or direct answers, says too long, get to the point, tldr, stop rambling, or when replies keep getting cut off or ignored.