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Found 1,760 Skills
Subscribe & Save optimization — enrollment, discount tiers, frequency optimization, retention analysis
Autonomous experiment loop for optimization research. Use when the user wants to: - Optimize a metric through systematic experimentation (ML training loss, test speed, bundle size, build time, etc.) - Run an automated research loop: try an idea, measure it, keep improvements, revert regressions, repeat - Set up autoresearch for any codebase with a measurable optimization target Implements the autoresearch pattern with MAD-based confidence scoring, git branch isolation, and structured experiment logging.
There's an AI for That (TAAFT) platform help — #1 AI tools directory (42,000+ tools, 3-4M monthly visits, DR76 dofollow, 1M+ newsletter subscribers). Covers tool submissions ($347 paid, free monthly X thread), featured PPC ads (bid-based positioning), highlighted listings, listing optimization, $300 TAAFT-first launch bonus, newsletter inclusion, and ChatGPT plugin API. Use when submitting an AI tool to TAAFT, wondering if the $347 listing is worth it, trying to get featured on TAAFT, want to optimize your TAAFT listing for clicks, comparing TAAFT with Futurepedia or Altern, or need to understand TAAFT's PPC ad system. Do NOT use for multi-directory launch coordination (use /sales-launch-directory). Do NOT use for other AI directories like Altern (use /sales-altern) or Futurepedia (use /sales-futurepedia).
Implement AI Coaching best practices on AnalyticDB for PostgreSQL (ADBPG): Leverage Supabase projects (training data management) + ADBPG instances with vector optimization to build RAG-driven coaching systems that guide users through domain-specific workflows, decision-making, or skill development. Use when: User wants to create Supabase projects (spb-xxx), ADBPG instances (gp-xxx), vector knowledge bases, or RAG-driven coaching systems on ADBPG. Triggers: "Supabase", "ADBPG", "vector database", "knowledge base", "RAG", "AI coaching", "coaching system", "spb-xxx", "gp-xxx"
React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.
Terminal emulation, text rendering optimization, and SwiftTerm integration for modern Swift applications
Use this skill to use Liquid variables in LookML for dynamic SQL, HTML, and Links, including advanced patterns for query optimization.
When the user wants to design, launch, or optimize a referral or affiliate program. Use when they mention 'referral program,' 'affiliate program,' 'word of mouth,' 'refer a friend,' 'incentive program,' 'customer referrals,' 'brand ambassador,' 'partner program,' 'referral link,' or 'growth through referrals.' Covers program mechanics, incentive design, and optimization — not just the idea of referrals but the actual system.
Docker and container development agent skill and plugin for Dockerfile optimization, docker-compose orchestration, multi-stage builds, and container security hardening. Use when: user wants to optimize a Dockerfile, create or improve docker-compose configurations, implement multi-stage builds, audit container security, reduce image size, or follow container best practices. Covers build performance, layer caching, secret management, and production-ready container patterns.
AI autonomous research agent for LLM training optimization using opencode as the agent. The agent autonomously modifies train.py, runs experiments, evaluates val_bpb, and iterates to find the best model. Use when: "run autoresearch", "start experiment", "train model", "autonomous research", "optimize LLM training".
Code Review Expert: Perform in-depth code reviews using context-isolated subagents, covering security vulnerabilities, performance optimizations, and production reliability
Premium A+ and Brand Story — module design, lifestyle imagery, comparison charts, mobile optimization