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 evalsgit 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/evals)<a href="https://agentmods.dev/skills/danielmiessler/lifeos/evals"><img src="https://agentmods.dev/badge/skills/danielmiessler/lifeos/evals/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/evals"><img src="https://agentmods.dev/badge/skills/danielmiessler/lifeos/evals.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- NVIDIA SkillSpector pass
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 | $0.00129 | $0.02181 |
| Opus 5.5 | $0.00052 | $0.00872 |
| Sonnet 5.5 | $0.00026 | $0.00436 |
| Haiku 4.5 | $0.00013 | $0.00218 |
Grade A, and why
Evals 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 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.
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 — 114 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Evals — Assertion-First AI Evaluation
What it is
An eval gives an AI an input, then applies assertions to its output to measure success (Anthropic's definition). A case is {id, prompt, assert:[...]}. Each assertion is either deterministic (code, fast/free) or model-graded (an LLM judge). Cases run multiple trials; we report pass^k (all trials pass — the honest metric for a reliability-critical agent) and pass@k (any trial passes). Everything routes through Inference.ts — subscription-billed, no API-key path, no external deps.
Grounded in Anthropic's current doctrine — Demystifying evals for AI agents, Define success criteria / develop tests, and the skill-creator {text, passed, evidence} assertion convention. The typed-assert layer is promptfoo-shaped but our own TS.
Freshness contract: "aligned to Anthropic's doctrine" is a live claim, not a snapshot. When designing a new suite class or touching the ## Doctrine section below, re-fetch the Demystifying-evals doc and flag where it has moved past what's encoded here. Advisory only — report divergence, never auto-adopt, and an unreachable URL never blocks a run.
The canonical path (v2)
| Tool | Role |
|---|---|
Tools/Assertions.ts |
Deterministic assert engine: equals, contains, icontains, contains-all/any, regex, starts-with, ends-with, is-json, contains-json, max-length, min-length, each with not- negation. Sync, no model call. |
Tools/Judge.ts |
Model-graded asserts llm-rubric (1–5 → 0–1, threshold) and llm-assert (NL assertions → TRUE/FALSE/UNKNOWN). Forced-structured JSON verdict, reason-then-score, distinct judge level, Unknown→miss escape hatch. |
Tools/EvalRunner.ts |
Loads a suite, runs the agent-under-test per case (single-shot inference against the target system prompt), applies asserts, computes pass^k/pass@k, persists transcripts + latest.json. |
Tools/SuiteManager.ts |
Suite listing + saturation tracking. |
Tools/FailureToTask.ts |
Convert real failures into cases (seed from 20–50 real failures). |
What ships with it
52 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.
- BestPractices.md 1.5 KB
- Data/DomainPatterns.yaml 4.5 KB
- Graders/Base.ts 2.9 KB runs code
- Graders/CodeBased/BinaryTests.ts 2.6 KB runs code
- Graders/CodeBased/index.ts 615 B runs code
- Graders/CodeBased/RegexMatch.ts 1.9 KB runs code
- Graders/CodeBased/StateCheck.ts 4.7 KB runs code
- Graders/CodeBased/StaticAnalysis.ts 2.9 KB runs code
- Graders/CodeBased/StringMatch.ts 1.7 KB runs code
- Graders/CodeBased/ToolCallVerification.ts 3.6 KB runs code
- Graders/index.ts 322 B runs code
- Graders/ModelBased/index.ts 403 B runs code
- Graders/ModelBased/JudgeLevel.ts 3.0 KB runs code
- Graders/ModelBased/LLMRubric.ts 5.3 KB runs code
- Graders/ModelBased/NaturalLanguageAssert.ts 3.8 KB runs code
- Graders/ModelBased/PairwiseComparison.ts 6.4 KB runs code
- package.json 480 B
- Scenarios/example-greeting.scenario.ts 2.0 KB runs code
- ScienceMapping.md 2.0 KB
- ScorerTypes.md 1.7 KB
- Suites/Regression/core-behaviors.yaml 428 B
- Suites/Regression/core-dispositions.yaml 6.6 KB
- TemplateIntegration.md 1.7 KB
- Tools/Assertions.ts 7.4 KB runs code
- Tools/EvalRunner.ts 11 KB runs code
- Tools/FailureToTask.ts 10 KB runs code
- Tools/GenerateCases.ts 5.1 KB runs code
- Tools/Judge.ts 6.8 KB runs code
- Tools/LifeosAgentAdapter.ts 2.3 KB runs code
- Tools/ProposeFromFailures.ts 5.4 KB runs code
- Tools/ScenarioRunner.ts 7.3 KB runs code
- Tools/ScenarioToTranscript.ts 3.4 KB runs code
- Tools/SuiteManager.ts 11 KB runs code
- Tools/TranscriptCapture.ts 5.9 KB runs code
- Tools/TrialRunner.ts 8.2 KB runs code
- Types/index.ts 9.0 KB runs code
- UseCases/Dispositions/disp_concision_voice.yaml 1.3 KB
- UseCases/Dispositions/disp_lead_with_answer.yaml 1.1 KB
- UseCases/Dispositions/disp_no_fabricated_confidence.yaml 1.4 KB
- UseCases/Dispositions/disp_verify_before_done.yaml 1.5 KB
- UseCases/Regression/task_file_targeting_basic.yaml 1.2 KB
- UseCases/Regression/task_no_hallucinated_paths.yaml 1.3 KB
- UseCases/Regression/task_tool_sequence_read_before_edit.yaml 1.3 KB
- UseCases/Regression/task_verification_before_done.yaml 1.3 KB
- Workflows/CompareModels.md 7.5 KB
- Workflows/ComparePrompts.md 9.0 KB
- Workflows/CreateJudge.md 4.8 KB
- Workflows/CreateScenario.md 4.0 KB
- Workflows/CreateUseCase.md 6.4 KB
- Workflows/RunEval.md 2.2 KB
- Workflows/RunScenario.md 3.0 KB
- Workflows/ViewResults.md 3.0 KB
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 · 114 lines · 129 tokens per session scan A 0d317edd544c
Evals is a skill published in the GitHub repository danielmiessler/LifeOS (19,557 stars, last pushed 1mo ago), licensed MIT. It adds 129 tokens to every session and 2,181 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 2026-08-30.
Other skills, from other repositories
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.
browser-use
Direct browser control via CDP for web interaction: automation, scraping, testing, screenshots, and site/app work. Also covers hosted cloud browsers for AI agents: the browser-use Python library (Browser(usecloud=True)), Playwright, Puppeteer or any CDP client.
mantis-configure
Configures and validates Mantis pipeline environments, sandbox mechanisms, and AI models. Use to set up workflow.json, auto-detect host capabilities, switch between sandboxes (static-only, gvisor, microsandbox, gce), select AI models, and run fast 1-2s preflight tests. Don't use for scanning source code or running…
migrate-to-shoehorn
A testing guide for Shoehorn, a TypeScript library that lets tests provide only the fields they need from a larger data type.
benchmark-workflow
Run, diagnose, or change Xberg extraction benchmarks, quality scoring, benchmark fixtures, artifact contracts, and independently sourced ground truth. Load for the Benchmarks workflow or benchmark-harness work, not ordinary unit tests.
story-origin-check
Recover the first public timestamp and canonical major coverage for a newsjacking signal, then decide whether newer coverage is the same story, a different story, or a materially new development.