All Skills

Frontend development patterns for React and Next.js: component composition, state management, performance optimization with memoization and code splitting, accessibility patterns, and UI best practices.

Library-agnostic Flutter/Dart code review checklist: widget best practices, state management patterns across BLoC/Riverpod/Provider/GetX, Dart idioms, performance optimization, accessibility, security, and clean architecture.

X/Twitter API integration: posting tweets and threads, reading timelines, search endpoints, analytics retrieval, OAuth 1.0a and 2.0 auth patterns, rate limit handling, and platform-native content formatting.

Rust testing patterns: unit tests, integration tests, async testing with tokio::test, property-based testing with proptest, mocking strategies, and coverage measurement with cargo-tarpaulin.

Idiomatic Rust patterns: ownership, borrowing, lifetimes, error handling with thiserror/anyhow, trait design, concurrency with tokio, and performance optimization for safe systems programming.

Multi-platform content distribution across X, LinkedIn, Threads, and Bluesky. Adapts content per platform voice and format constraints. Never posts identical content cross-platform.

Write articles, guides, blog posts, tutorials, and newsletter issues in a distinctive voice derived from supplied examples or brand guidance. Structure for credibility with evidence, examples, and clear takeaways.

Conduct market research, competitive analysis, investor due diligence, and industry intelligence with source attribution and decision-oriented summaries. Market sizing, competitor comparisons, fund research, and technology scans.

Analyze an unfamiliar codebase and generate a structured onboarding guide: architecture map, key entry points, naming conventions, dependency graph, and a starter CLAUDE.md for AI-assisted development.

Create and update pitch decks, one-pagers, investor memos, accelerator applications, financial models, and use-of-funds tables. Maintains internal consistency across multiple fundraising assets.

Create platform-native content systems for X, LinkedIn, TikTok, YouTube, and newsletters. Adapt one source asset across platforms without cross-posting identical content. Content calendar generation and repurposing workflows.

Multi-source deep research using web search and retrieval MCPs. Searches the web, synthesizes findings, and delivers cited reports with source attribution for any research topic.

Build automated AI-powered data collection agents for public sources: job boards, prices, news, GitHub, sports. Schedule scraping via GitHub Actions, enrich with LLM, store in Notion/Sheets/Supabase, and learn from user feedback.

Draft cold emails, warm intro blurbs, follow-ups, update emails, and investor communications. Personalized, concise messaging for angels, VCs, strategic investors, and accelerators.

Extract cross-cutting principles from skill libraries and distill them into reusable rule files. Scan skills for repeated patterns, consolidate into rule sets, and output as appendable rule definitions.

Research-before-coding workflow: search for existing tools, libraries, and patterns before writing custom code. Invokes researcher agent to prevent reinventing solutions that already exist.

Capture architectural decisions as structured ADRs during development sessions: auto-detect decision moments, record context and alternatives considered, maintain a searchable ADR log for future developers.

Audit context window consumption across agents, skills, MCP servers, and rules. Identifies token bloat, redundant components, and produces prioritized savings recommendations. Target: under 10 MCPs, under 80 tools active.

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher task completion rates.

Agentic engineering operating model: eval-first execution, task decomposition, cost-aware model routing by complexity, and structured agent workflow patterns.

Operate long-lived agent workloads with observability, security boundaries, and lifecycle management patterns for enterprise deployments.

Strategic context window compaction: compact after research before implementation, after milestones before next phase, after debugging before feature work, after failed approaches before new ones. Never mid-implementation.

LLM cost optimization pipeline: automatic model routing by text length and item count thresholds, immutable cost tracking with frozen dataclasses, budget enforcement with early stopping, exponential backoff retry, and prompt caching strategies.

Instinct-based learning system: observe sessions via hooks, create atomic instincts with confidence scoring, project-scoped isolation to prevent cross-project contamination, promote universal patterns to global scope, and evolve instinct clusters into full skills.