66건(danielmiessler의 저장소) · 인기순
Evaluate AI agent outputs with code checks and model-based grading. Run repeated trials to measure reliability, compare prompts or models, and check whether changes reduce quality, with saved transcripts for reviewing the results.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Structure problem solving around clear goals, competing explanations, experiments, and measurable results. Use the cycle to diagnose bugs, validate features, compare prompts, or evaluate an early product, then choose the next step based on evidence.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
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.
Extract web page content as Markdown from a single URL or a site crawl. When access is blocked, progressively switch from basic fetching to Chrome and Bright Data proxy services, which route requests through other IP addresses.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Improve writing, prompts, or skills through repeated review cycles that carry forward lessons and rejected approaches. Human feedback guides each pass by default, while saved history supports resuming work and tracking progress.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Explore explanations for poorly understood phenomena through repeated hypothesis generation and selection. Compare causal mechanisms, test what happens when components change, and retain distinct candidates with predictions, rival comparisons, and clearly stated unresolved questions.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Research topics across multiple sources, cross-check findings, and verify source URLs before producing a report with confidence labels. Choose from quick lookups to iterative investigations that preserve findings, with community sources prioritized for questions about public reactions.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Create editable tldraw canvases with boxes, arrows, sticky notes, frames, and text. Read an existing canvas to group related ideas and rearrange shapes, then open the resulting file in a compatible editor for further adjustments.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
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.
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.
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.
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.
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.
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.
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.
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.