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Found 523 Skills
Generate a time-windowed pulse report on what users experienced and how the product performed - usage, quality, errors, signals worth investigating. Use when the user says 'run a pulse', 'show me the pulse', 'how are we doing', 'weekly recap', 'launch-day check', or passes a time window like '24h' or '7d'. Configures via .compound-engineering/config.local.yaml and saves reports to docs/pulse-reports/.
Use when adding, modifying, optimizing, or debugging CuTile autotuning code. Trigger signals: `exhaustive_search` / `replace_hints` / `hints_fn` / `cuda.tile.tune` in code, `autotune` in filenames, or correctness/performance issues in autotuned CuTile kernels. Covers: tune-once/cache/launch pattern, per-architecture configs (sm80–sm120), parameter space design (tile sizes, occupancy, num_ctas), and 7 common pitfalls with solutions.
Backtest crypto and traditional trading strategies against historical data. Calculates performance metrics (Sharpe, Sortino, max drawdown), generates equity curves, and optimizes strategy parameters. Use when user wants to test a trading strategy, validate signals, or compare approaches. Trigger with phrases like "backtest strategy", "test trading strategy", "historical performance", "simulate trades", "optimize parameters", or "validate signals".
Spawn a single autonomous AI agent with a specific task, personality, and CLI backend (Claude, Gemini, OpenCode, Copilot). Agent accepts task from docs/todo/pending/, selects personality based on task type, and works autonomously with CLI tools. Integrates with docs-first workflow via task signals and progress tracking.
Detects market top probability using O'Neil Distribution Days, Minervini Leading Stock Deterioration, and Monty Defensive Sector Rotation. Generates a 0-100 composite score with risk zone classification. Use when user asks about market top risk, distribution days, defensive rotation, leadership breakdown, or whether to reduce equity exposure. Focuses on 2-8 week tactical timing signals for 10-20% corrections.
Quick backtest a strategy on a symbol. Creates a complete .py script with data fetch, signals, backtest, stats, and plots.
Detect buying signals across TAM companies and watchlist personas. Three-phase architecture: (1) free diff-based signals from existing data (headcount growth, tech stack changes, funding rounds), (2) Apify-powered signals (job postings, LinkedIn content analysis, profile changes), and (3) post-processing with dedup, scoring, and lead status updates. Writes signals to Supabase signals table for downstream activation.
Enrich contacts and companies with verified emails, phones, and firmographic data. Also covers CRM data hygiene, deduplication, and bulk enrichment. Use when enriching leads, finding email addresses, cleaning CRM data, doing bulk enrichment, optimizing enrichment credits, setting up auto-enrichment, or fixing stale contact data. Do NOT use for building new prospect lists from scratch (use /sales-prospect-list), interpreting buying signals (use /sales-intent), or general Apollo platform help (use /sales-apollo).
Expert-level SolidJS and SolidStart development skill with 20+ years senior/lead engineer mindset. Comprehensive guidance for building production-ready, scalable web applications with fine-grained reactivity. Use when Claude needs to: (1) Create new SolidJS/SolidStart projects, (2) Implement TanStack Query/Router/Table/Form integration, (3) Build reactive components with signals/stores/resources, (4) Handle SSR/SSG/streaming with SolidStart, (5) Implement authentication and API routes, (6) Optimize bundle size and performance, (7) Debug reactivity issues and memory leaks, (8) Structure large-scale applications, (9) Implement type-safe patterns with TypeScript, (10) Handle error boundaries and suspense, (11) Build accessible UI components, (12) Deploy to Vercel/Netlify/Cloudflare. Triggers: "solid", "solidjs", "solidstart", "createSignal", "createStore", "createResource", "tanstack solid", "vinxi", "fine-grained reactivity".
Framework for developing, testing, and deploying trading strategies for prediction markets. Use when creating new strategies, implementing signals, or building backtesting logic.
Healthcare Enterprise Funding Monitoring System. Real-time monitoring of industrial and commercial changes of healthcare enterprises, identification of funding signals, and automatic alert pushing. Supports data collection from Tianyancha/Qichacha, AI funding judgment, and multi-channel pushing.
Design a product-led sales motion from usage signals to sales handoff and conversion.