Research

Achieve comprehensive baseline (V_meta ≥0.40) in iteration 0 to enable rapid convergence. Use when planning iteration 0 time allocation, domain has established practices to reference, rich historical data exists for immediate quantification, or targeting 3-4 iteration convergence. Provides 4 quality levels (minimal/basic/comprehensive/exceptional), component-by-component V_meta calculation guide, and 3 strategies for comprehensive baseline (leverage prior art, quantify baseline, domain universality analysis). 40-50% iteration reduction when V_meta(s₀) ≥0.40 vs <0.20. Spend 3-4 extra hours in iteration 0, save 3-6 hours overall.

Master 26 documented prompt engineering principles for crafting effective LLM prompts with 400%+ quality improvement. Includes templates, anti-patterns, and quality checklists for technical, learning, creative, and research tasks. Use when writing prompts for LLMs, improving AI response quality, training on prompting, designing agent instructions, or when user mentions 'prompt engineering', 'better prompts', 'LLM quality', 'prompt templates', 'AI prompts', 'prompt principles', or 'prompt optimization'.

Expert React video scene component creator for educational content. Builds production-grade, visually distinctive components using framer-motion animations, pixel-precise positioning, and optimized performance patterns. Follows strict component format with React.memo, threshold-based state updates, and module-level definitions. Outputs self-contained TSX components with proper timing sync, 60fps performance, and comprehensive reference-based implementation.

Multi-LLM orchestration implementing the 5-stage coding workflow: Example Analysis → Hypothesis → Implementation → Debug Loop → Recursive Improvement. Based on "Towards a Science of Scaling Agent Systems" (Kim et al., 2025): - Centralized Consult architecture (Claude orchestrates, others advise) - Measured coordination (avoid MAS overhead in tool-heavy stages) - Tests-first selection (Poetiq pattern, not voting) Use when: Debugging complex issues, analyzing unfamiliar code, refactoring, or any task that benefits from diverse LLM perspectives with verification.

Journelly-format journal entries. USE WHEN user wants to create journal entries, write reflections, or work with Journelly.org file.

Personal AI Infrastructure core principles and operating system. AUTO-LOADS at session start. USE WHEN any session begins OR user asks about identity, response patterns, workflow preferences, or core principles.

Use when the user says "search internet" or for requests related to web research; it provides a structured approach to conducting comprehensive web research

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)

Jira issue management for Red Hat issues.redhat.com. USE WHEN user mentions jira, ticket, issue, epic, sprint OR references Jira issue keys (SRVKP-1234, SRVCOM-456) OR wants to manage issue workflows, assignments, tracking OR needs to integrate Jira with org-mode notes and TODOs.

Provides assistance with planning family history and genealogy research projects.

Analyze WebAssembly test (WAST) files to debug compilation issues and create regression tests. Use when the user asks to debug or analyze WAST test failures, investigate compilation bugs in wasmoon, or when encountering test failures in spec/*.wast files. Triggers include "analyze wast", "debug wast", "wast bug", or references to specific .wast test files.

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.

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.

Guidance for writing and verifying SPARQL queries against RDF datasets, particularly university/academic ontologies. This skill should be used when tasks involve querying RDF data with SPARQL, working with academic datasets (students, professors, departments, courses), or performing complex graph pattern matching with filters and aggregations.

Develop research-backed AI competitive strategies combining academic research, market trends, and social sentiment analysis

Guidance for bypassing HTML/JavaScript sanitization filters in security testing contexts. This skill should be used when tasked with finding XSS filter bypasses, testing HTML sanitizers, or exploiting parser differentials between server-side filters and browsers. Applies to CTF challenges, authorized penetration testing, and security research involving HTML injection and JavaScript execution through sanitization bypasses.

Product discovery frameworks for PMs - customer interviews, assumption mapping, JTBD, RICE prioritization, and opportunity solution trees. Transforms research into product decisions.

Guidance for writing SPARQL queries against RDF/Turtle datasets, particularly for university or academic data. This skill should be used when tasks involve querying RDF data with SPARQL, filtering entities based on multiple criteria, aggregating results, or working with Turtle (.ttl) files.

Research external open-source repositories to learn patterns and implementations. This skill should be used when agents need to study external codebases, check for existing research before cloning, manage temporary workspaces, and persist findings to the global thoughts system. Triggers include: studying external repos, learning from open source, cloning for pattern research, or checking what has been researched.

Guidance for building digital logic circuits that compute composite functions like Fibonacci of integer square root. This skill applies when implementing combinational and sequential logic in gate-level simulators, particularly when combining algorithms (like isqrt and Fibonacci) under resource constraints (gate counts, simulation steps). Use for circuit synthesis, HDL-style logic design, or gate-level algorithm implementation tasks.