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Found 808 Skills
Helps users discover and apply shared coding solutions when they ask "has anyone solved this", "search for a fix", "find a workaround", or want proven patterns before debugging from scratch. Uses `npx shareful-ai search` to find relevant shares, compare options, and recommend the best match.
Generates a case study aligned with Digital Speed brand voice. Use when asked to write a case study, success story, or client spotlight.
Grand Slam Offer creation framework based on Alex Hormozi's "$100M Offers". Use when you need to: (1) create irresistible offers using the Value Equation, (2) design Grand Slam Offers with bonuses, guarantees, and scarcity, (3) find your starving crowd and ideal market, (4) implement value-based pricing with 10:1 value-to-price ratio, (5) stack bonuses that increase perceived value, (6) design risk-reversing guarantees, (7) apply ethical scarcity and urgency, (8) name offers using the MAGIC formula.
Create and contribute skills to the communal knowledge base. Use when creating new skills, updating existing skills, or contributing learnings back to the repository.
Guidance for creating standalone CLI tools that perform neural network inference by extracting PyTorch model weights and reimplementing inference in C/C++. This skill applies when tasks involve converting PyTorch models to standalone executables, extracting model weights to portable formats (JSON), implementing neural network forward passes in C/C++, or creating CLI tools that load images and run inference without Python dependencies.
Creates, updates, and manages Agent Skills following the Claude Code style. Use this skill when the user wants to add a new capability, create a new skill, or modify an existing skill.
General coding best practices and software engineering principles to build robust, maintainable, and scalable software.
Run parallel quality reviews (React, SOLID, Security, Simplification, Slop) on branch changes and auto-fix issues
Run Schemathesis for property-based API security testing. Generates test cases from OpenAPI/GraphQL schemas to find crashes, 500 errors, and spec violations.
C++ Reinforcement Learning best practices using libtorch (PyTorch C++ frontend) and modern C++17/20. Use when: - Implementing RL algorithms in C++ for performance-critical applications - Building production RL systems with libtorch - Creating replay buffers and experience storage - Optimizing RL training with GPU acceleration - Deploying RL models with ONNX Runtime
Apply lean thinking to UX: hypothesis-driven design, collaborative sketching, and rapid experiments instead of heavy deliverables. Use when the user mentions "Lean UX", "design hypothesis", "UX experiment", "collaborative design", or "outcome over output". Covers hypothesis statements, MVPs for UX, and cross-functional collaboration. For Build-Measure-Learn, see lean-startup. For usability audits, see ux-heuristics.
Integrates Flowlines observability SDK into Python LLM applications. Use when adding Flowlines telemetry, instrumenting LLM providers, or setting up OpenTelemetry-based LLM monitoring.