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/promptingnpx skills add danielmiessler/LifeOS --skill promptinggit 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/prompting)<a href="https://agentmods.dev/skills/danielmiessler/lifeos/prompting"><img src="https://agentmods.dev/badge/skills/danielmiessler/lifeos/prompting.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 | $0.00090 | $0.02345 |
| Opus 5 | $0.00045 | $0.01172 |
| Sonnet 5 | $0.00018 | $0.00469 |
| Haiku 4.5 | $0.00009 | $0.00234 |
Grade A, and why
Prompting 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 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.
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 — 211 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/Prompting/
If this directory exists, load and apply any PREFERENCES.md, configurations, or resources found there. These override default behavior. If the directory does not exist, proceed with skill defaults.
🚨 MANDATORY: Voice Notification (REQUIRED BEFORE ANY ACTION)
You MUST send this notification BEFORE doing anything else when this skill is invoked.
-
Send voice notification:
curl -s -X POST http://localhost:31337/notify \ -H "Content-Type: application/json" \ -d '{"message": "Running the WORKFLOWNAME workflow in the Prompting skill to ACTION"}' \ > /dev/null 2>&1 & -
Output text notification:
Running the **WorkflowName** workflow in the **Prompting** skill to ACTION...
This is not optional. Execute this curl command immediately upon skill invocation.
Prompting - Meta-Prompting & Template System
What It Does
Generates, optimizes, and composes prompts programmatically. It's the standard library for prompt engineering — other skills call it when they need to build or improve a prompt. The output is always a prompt to be used elsewhere, never the final content itself.
Invoke when: meta-prompting, template generation, prompt optimization, programmatic prompt composition, creating dynamic agents, generating structured prompts from data.
The Problem
Prompt engineering tends to get copy-pasted and rewritten by hand across every skill that needs it, so the same patterns drift apart and best practices live in one person's head. When you want to compose a prompt from data — spin up a custom agent, build an eval judge, generate a phased workflow — there's no clean way to separate the structure from the content. This skill makes structure code and content data: one Handlebars template plus different data renders specialized agents, workflows, and eval frameworks, and the engineering standards live in one place every skill can reference.
What ships with it
28 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.
- References/WritingGreatSkills.md 7.2 KB
- Standards.md 44 KB
- Templates/Data/Agents.yaml 3.8 KB
- Templates/Data/ValidationGates.yaml 7.7 KB
- Templates/Data/VoicePresets.yaml 4.0 KB
- Templates/Evals/Comparison.hbs 3.9 KB
- Templates/Evals/Judge.hbs 3.5 KB
- Templates/Evals/Report.hbs 4.4 KB
- Templates/Evals/Rubric.hbs 2.9 KB
- Templates/Evals/TestCase.hbs 2.7 KB
- Templates/Primitives/Briefing.hbs 2.4 KB
- Templates/Primitives/Gate.hbs 1.9 KB
- Templates/Primitives/Roster.hbs 1.9 KB
- Templates/Primitives/Structure.hbs 1.4 KB
- Templates/Primitives/Voice.hbs 1.6 KB
- Templates/README.md 8.6 KB
- Templates/Tools/.gitignore 388 B
- Templates/Tools/bun.lock 2.5 KB
- Templates/Tools/CLAUDE.md 2.5 KB
- Templates/Tools/index.ts 30 B runs code
- Templates/Tools/package.json 266 B
- Templates/Tools/README.md 220 B
- Templates/Tools/RenderTemplate.ts 9.2 KB runs code
- Templates/Tools/tsconfig.json 759 B
- Templates/Tools/ValidateTemplate.ts 8.8 KB runs code
- Tools/index.ts 30 B runs code
- Tools/RenderTemplate.ts 9.2 KB runs code
- Tools/ValidateTemplate.ts 8.8 KB runs code
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 · 211 lines · 90 tokens per session scan A bf7a8cef41bf
Prompting is a skill published in the GitHub repository danielmiessler/LifeOS (18,885 stars, last pushed yesterday), licensed MIT. It adds 90 tokens to every session and 2,345 once invoked, about $0.0005 per session on Opus 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.
Other skills, from other repositories
prompt-library
Curated collection of high-quality prompts for various use cases. Includes role-based prompts, task-specific templates, and prompt refinement techniques. Use when user needs prompt templates, role-play prompts, or ready-to-use prompt examples for coding, writing, analysis, or creative tasks.
ai-prompt-generator
专业 AI 提示词生成工具,帮助用户创建高效、精准的 AI 提示词。内置多种框架和模板,让 AI 输出质量提升 10 倍。.
llm-integration
LLM integration patterns including API usage, streaming, function calling, RAG pipelines, and cost optimization.
prompt-engineering
Prompt engineering patterns including structured prompts, chain-of-thought, few-shot learning, and system prompt design.
token-saver
Minimize token consumption & maximize prompt cache hit rate. Use when user asks to save tokens, reduce cost, improve cache hit rate, or be more concise.
offline-prompt-packager
把基于 prompts/ 主源的 skill 打包成断网单机可用的离线提示词包。Use when the user wants to export/package offline prompts for disconnected hosts (WebUI、Qwen、AnythingLLM、Claude.ai), generate self-contained systemprompt / userprompt bundles, or build a per-文种 prompt.