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Found 112 Skills
Only to be triggered by explicit super-swarm-spark commands.
Writes failing tests first for test-driven development and hands off a strict implementation contract that requires agents to make those tests pass without weakening the tests. Use when users ask for test-first workflows, RED/GREEN cycles, or behavior-gating tasks with automated tests.
Spawn Codex subagents via background shell to offload context-heavy work. Use for: deep research (3+ searches), codebase exploration (8+ files), multi-step workflows, exploratory tasks, long-running operations, documentation generation, or any other task where the intermediate steps will use large numbers of tokens.
Expert in developing, installing, and managing tweaks for the Codex++ desktop app extension system
Route low-risk coding tasks to cheaper LLMs while keeping Codex for high-risk decisions, using MCP tools for cost-aware delegation
Keep documentation in sync with code changes across README, docs sites, API docs, runbooks, and configuration. Use when the user asks to update docs, ensure docs match behavior, or prepare docs for a release/PR.
Harden configuration and defaults for safer deployment. Use when a mid-level developer needs to reduce misconfig risks.
Generate nested AGENTS.md coding guidelines per module (monorepo-aware), detect languages/tooling, ask architecture preferences, and set up missing formatters/linters (Spotless for JVM). Use when the user wants module-scoped AGENTS.md coding guidelines or to set up missing formatters/linters.
Review and design SaaS/product marketing sites and frontend interfaces end-to-end: clarify value, fix hierarchy, and implement distinctive, production-grade UI that avoids generic AI aesthetics.
Assess and prioritize tech debt items. Use when a senior developer needs an investment plan for debt reduction.
Elite AI/ML Senior Engineer with 20+ years experience. Transforms Claude into a world-class AI researcher and engineer capable of building production-grade ML systems, LLMs, transformers, and computer vision solutions. Use when: (1) Building ML/DL models from scratch or fine-tuning, (2) Designing neural network architectures, (3) Implementing LLMs, transformers, attention mechanisms, (4) Computer vision tasks (object detection, segmentation, GANs), (5) NLP tasks (NER, sentiment, embeddings), (6) MLOps and production deployment, (7) Data preprocessing and feature engineering, (8) Model optimization and debugging, (9) Clean code review for ML projects, (10) Choosing optimal libraries and frameworks. Triggers: "ML", "AI", "deep learning", "neural network", "transformer", "LLM", "computer vision", "NLP", "TensorFlow", "PyTorch", "sklearn", "train model", "fine-tune", "embedding", "CNN", "RNN", "LSTM", "attention", "GPT", "BERT", "diffusion", "GAN", "object detection", "segmentation".
Use when asked to detect silent failures/weak error handling or explicitly asked to run the silent-failure-hunter subagent.