Loading...
Loading...
Found 1,666 Skills
Specialized business logic evaluator for the Evaluate-Loop. Use this for evaluating tracks that implement core product logic — pipelines, dependency resolution, state machines, pricing/tier enforcement, packaging. Checks feature correctness against product rules, edge cases, state transitions, data flow, and user journey completeness. Dispatched by loop-execution-evaluator when track type is 'business-logic', 'generator', or 'core-feature'. Triggered by: 'evaluate logic', 'test business rules', 'verify business rules', 'check feature'.
Create diverse synthetic test inputs for LLM pipeline evaluation using dimension-based tuple generation. Use when bootstrapping an eval dataset, when real user data is sparse, or when stress-testing specific failure hypotheses. Do NOT use when you already have 100+ representative real traces (use stratified sampling instead), or when the task is collecting production logs.
Fastlane, GitHub Actions for mobile, code signing (iOS provisioning, Android keystores), beta distribution (TestFlight, Firebase App Distribution), and app store submissions. Use when setting up mobile build pipelines, automating releases, or managing signing.
Universal Assistant — Automatically analyzes scenarios, takes inventory of ECC resources, intelligently routes to the optimal agent pipeline, and completes complex workflows with one click.
Develop Python applications using modern patterns, uv, functional-first design, and production-first practices. Use this whenever working with .py files, pyproject.toml, uv commands, pip/pip3, poetry, virtualenv/venv, inline script metadata, or Python tooling like pytest, mypy, ruff, asyncio, itertools, functools, or dataclasses. If the task involves running Python, managing Python dependencies, creating environments, or building Python packages, load this skill and prefer uv-oriented workflows.
Integrate and optimize Core ML models in iOS apps for on-device machine learning inference. Covers model loading (.mlmodelc, .mlpackage), predictions with auto-generated classes and MLFeatureProvider, compute unit configuration (CPU, GPU, Neural Engine), MLTensor, VNCoreMLRequest, MLComputePlan, multi-model pipelines, and deployment strategies. Use when loading Core ML models, making predictions, configuring compute units, or profiling model performance.
Generate a handoff document after implementation work is complete — summarizes changes, risks, and review focus areas for the review pipeline. Use when done coding and ready to hand off for review.
Frontend debugging team using Chrome DevTools MCP. Dual-mode -- feature-list testing or bug-report debugging. Covers reproduction, root cause analysis, code fixes, and verification. CSV wave pipeline with conditional skip and iteration loops.
4 production-ready business and growth skills: customer success manager with health scoring and churn prediction, sales engineer with RFP analysis, revenue operations with pipeline and GTM metrics, and contract & proposal writer. Python tools included (all stdlib-only). Works with Claude Code, Codex CLI, and OpenClaw.
Advanced GitHub Actions workflow automation with AI swarm coordination, intelligent CI/CD pipelines, and comprehensive repository management
Rapidly scaffold and implement a playable game — no assets, design, audio, deploy, or monetize. Get something on screen fast. Use when the user says "quick game", "fast prototype", "just get something playable", or wants a game without the full pipeline. For the complete pipeline, use make-game instead. Do NOT use for production games (use make-game for the full pipeline).
Compile LaTeX papers to PDF with automatic error detection, chktex style checking, and citation/reference validation. Runs the full pdflatex + bibtex pipeline. Use when the user wants to compile a paper, fix compilation errors, or debug LaTeX.