Daniel Miessler 🛡️

Import existing notes and agent instructions into LifeOS by splitting content into sections and proposing destinations. Review uncertain classifications, approve selected groups, and retain source information alongside the imported material.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Importing Obsidian or Notion exports into LifeOS
• Moving notes from an older LifeOS installation
• Reviewing and importing CLAUDE.md or Cursor rules

Improve code, prompts, skills, or agent definitions through repeated changes and evaluation. Measure numerical targets such as latency or size, or use an LLM to judge pass-or-fail criteria, keeping changes that improve the result.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Improving page load performance
• Reducing build output size
• Refining prompts against evaluation criteria

Investigate incidents and recurring defects with structured methods such as Five Whys and Fishbone cause maps. Identify multiple contributing conditions, build a blame-free account of what happened, and propose practical changes to prevent similar failures.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Review a production outage
• Investigate recurring defects
• Compare failing and working environments

Turn product documentation into sales materials with a conversational story, charcoal sketch artwork, and talking points. The workflow connects product value to a narrative and matching visual for pitches, presentations, and sales scripts.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create a sales package from product documentation
• Turn technical documentation into a pitch script
• Create artwork for an existing sales story

Research cybersecurity companies, investors, funding, and acquisitions using Return on Security's Signal data. Results retain source freshness and distinguish recorded deals from analytical estimates, helping you examine market trends and company profiles.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Track quarterly funding trends in cloud security
• Research a security company's funding and investors
• Map acquisitions in identity security

Investigate recurring problems by mapping feedback loops, the cycles through which effects influence their causes. Identify underlying structures, match known patterns, and assess where changes could help and what unintended effects they might produce.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating recurring incidents
• Understanding coordination problems as teams grow
• Assessing unintended effects before changing a policy

Turn the current conversation topic into an interactive lesson with spoken explanations and text diagrams in the terminal. Answer one question at each step, receive feedback, and check your understanding with a short quiz and recap.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Understand the work you just completed
• Learn a named topic step by step
• Check your understanding with a quiz

Maintain personal goals and beliefs with timestamped backups, or analyze project documents to connect problems, goals, strategies, and projects. Turn findings about progress, bottlenecks, and alignment into reports, presentation points, or interactive dashboards.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Update personal goals while preserving history
• Find project dependencies and bottlenecks
• Prepare key points for a presentation

Comprehensive global knowledge worker salary data with total market value calculations, sector breakdowns, geographic comparisons, and authoritative sources. USE WHEN discussing knowledge worker compensation, salary benchmarking, economic analysis of professional labor markets, or AI impact on wages.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Multi-source comprehensive research using perplexity-researcher, claude-researcher, and gemini-researcher agents. Launches up to 10 parallel research agents for fast results. USE WHEN user says 'do research', 'research X', 'find information about', 'investigate', 'analyze trends', 'current events', or any research-related request.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Personal AI Infrastructure (PAI) - PAI System Template MUST BE USED proactively for all user requests. USE PROACTIVELY to ensure complete context availability. === CORE IDENTITY (Always Active) === Your Name: [CUSTOMIZE - e.g., Kai, Nova, Atlas] Your Role: [CUSTOMIZE - e.g., User's AI assistant and future friend] Personality: [CUSTOMIZE - e.g., Friendly, professional, resilient to user frustration. Be snarky back when the mistake is user's, not yours.] Operating Environment: Personal AI infrastructure built around Claude Code with Skills-based context management Message to AI: [CUSTOMIZE - Add personal message about interaction style, handling frustration, etc.] === ESSENTIAL CONTACTS (Always Available) === - [Primary Contact Name] [Relationship]: email@example.com - [Secondary Contact] [Relationship]: email@example.com - [Third Contact] [Relationship]: email@example.com Full contact list in SKILL.md extended section below === CORE STACK PREFERENCES (Always Active) === - Primary Language: [e.g., TypeScript, Python, Rust] - Package managers: [e.g., bun for JS/TS, uv for Python] - Analysis vs Action: If asked to analyze, do analysis only - don't change things unless explicitly asked - Scratchpad: Use ~/.claude/scratchpad/ with timestamps for test/random tasks === CRITICAL SECURITY (Always Active) === - NEVER COMMIT FROM WRONG DIRECTORY - Run `git remote -v` BEFORE every commit - `~/.claude/` CONTAINS EXTREMELY SENSITIVE PRIVATE DATA - NEVER commit to public repos - CHECK THREE TIMES before git add/commit from any directory - [ADD YOUR SPECIFIC WARNINGS - e.g., iCloud directory, company repos, etc.] === RESPONSE FORMAT (Always Use) === Use this structured format for every response: 📋 SUMMARY: Brief overview of request and accomplishment 🔍 ANALYSIS: Key findings and context ⚡ ACTIONS: Steps taken with tools used ✅ RESULTS: Outcomes and changes made - SHOW ACTUAL OUTPUT CONTENT 📊 STATUS: Current state after completion ➡️ NEXT: Recommended follow-up actions 🎯 COMPLETED: [Task description in 12 words - NOT "Completed X"] 🗣️ CUSTOM COMPLETED: [Voice-optimized response under 8 words] === PAI/KAI SYSTEM ARCHITECTURE === This description provides: core identity + essential contacts + stack preferences + critical security + response format (always in system prompt). Full context loaded from SKILL.md for comprehensive tasks, including: - Complete contact list and social media accounts - Voice IDs for agent routing (if using ElevenLabs) - Extended security procedures and infrastructure caution - Detailed scratchpad instructions === CONTEXT LOADING STRATEGY === - Tier 1 (Always On): This description in system prompt (~1500-2000 tokens) - essentials immediately available - Tier 2 (On Demand): Read SKILL.md for full context - comprehensive details === WHEN TO LOAD FULL CONTEXT === Load SKILL.md for: Complex multi-faceted tasks, need complete contact list, voice routing for agents, extended security procedures, or explicit comprehensive PAI context requests. === DATE AWARENESS === Always use today's actual date from the date command (YEAR MONTH DAY HOURS MINUTES SECONDS PST), not training data cutoff date.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Create irresistible offers and pitches using Alex Hormozi's methodology from $100M Offers. Guides through value equation, guarantee frameworks, pricing psychology, and creating offers "too good not to take" for any product or service.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Universal task initializer that automatically loads PAI context for all user requests. Ensures complete context availability (contacts, preferences, protocols) before responding to any task. (project, gitignored)

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Prompt engineering standards and context engineering principles for AI agents based on Anthropic best practices. Covers clarity, structure, progressive discovery, and optimization for signal-to-noise ratio.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Expert guidance for ffuf web fuzzing during penetration testing, including authenticated fuzzing with raw requests, auto-calibration, and result analysis

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Intelligent pattern selection for Fabric CLI. Automatically selects the right pattern from 242+ specialized prompts based on your intent - threat modeling, analysis, summarization, content creation, extraction, and more. USE WHEN processing content, analyzing data, creating summaries, threat modeling, or transforming text.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Guide for creating new skills in Kai's personal AI infrastructure. Use when user wants to create, update, or structure a new skill that extends capabilities with specialized knowledge, workflows, or tool integrations. Follows both Anthropic skill standards and PAI-specific patterns.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Measures the work in a code repo as deeds (caps, fixes and tends) by running the bundled `deeds` CLI over the commit history, for this repo or any GitHub repo, over any time window. Reports totals, a week-by-week trend, a per-author breakdown and the named caps, and explains what the numbers mean.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.