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Found 1,660 Skills
Build and deploy parallel execution via subagent waves, agent teams, and multi-wave pipelines. Use when the Decomposition Gate identifies 2+ independent actions or when spawning teams. NOT for single-action tasks or non-parallel work.
Universal Assistant — Automatically analyzes scenarios, takes inventory of ECC resources, intelligently routes to the optimal agent pipeline, and completes complex workflows with one click.
Run a structured, adversarial multi-agent bug review pipeline on a codebase. Use this skill whenever the user wants to find bugs, audit code quality, review a codebase for issues, or run any kind of bug-finding or code analysis workflow. Also trigger when the user asks to 'review my code for bugs', 'find all issues in this repo', 'audit this codebase', or any similar request. The pipeline uses three sequential phases: a Bug Finder that maximizes issue discovery, a Bug Adversary that challenges false positives, and an Arbiter that issues final verdicts — producing a clean, high-confidence bug report.
Optimize a prompt through a critique-compress pipeline with semantic equivalence verification at each stage. Applies think-critically to improve the prompt, then compress-prompt to reduce it, validating that behavior is preserved after each transformation.
Claude CLI sub-agent system for persona-based analysis. Use when piping large contexts to Anthropic models for security audits, architecture reviews, QA analysis, or any specialized analysis requiring a fresh model context.
Run configurable BMAD pipeline for story delivery using subagent
Explore-first wave pipeline. Decomposes requirement into exploration angles, runs wave exploration via spawn_agents_on_csv, synthesizes findings into execution tasks with cross-phase context linking (E*→T*), then wave-executes via spawn_agents_on_csv.
End-to-end deck pipeline — extract, build, audit, critique, and polish in one pass. Use when the user wants a complete deck from scratch with a template, says "create a full deck", "build me a presentation end to end", "make a polished deck from this template", or provides both a brief and a template and expects a finished, presentation-ready result. Prefer this over chaining individual skills manually.
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.
Manage Alibaba Cloud ApsaraVideo for Media Processing (MPS/MTS) resources and workflows via OpenAPI/SDK. Use for media ingest and metadata tasks, transcoding/snapshot jobs, pipeline/template/workflow operations, and MPS job troubleshooting.
Expert guide for participating in the SOMA network — a decentralized system that trains a foundation model through competition. Provides data submission workflows, model training pipelines, reward claiming, SDK code generation, CLI command guidance, and competitive strategy optimization. Use when user mentions "SOMA", "soma-sdk", "soma-models", "submit data to SOMA", "train a SOMA model", "SOMA targets", "SOMA rewards", "next-byte prediction network", "decentralized model training", or asks about earning SOMA tokens through data or model contributions. Do NOT use for general machine learning, PyTorch, or JAX questions unrelated to the SOMA network.
Orchestrate quality engineering across CI/CD pipeline phases. Use when designing test strategies, planning quality gates, or implementing shift-left/shift-right testing.