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Review whether a repository's coding-agent harness can reliably carry work from intent through controlled execution, verification, delivery, and learning. Use when asked to assess agent readiness, repeated agent failures, Rules/Skills/Hooks/Memory effectiveness, missing validation or recovery loops, or whether a harness repair improved later outcomes. Do not use for code-only audits, AGENTS-only audits, individual skill reliability reviews, or executing the task itself.

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Debug and fix infinite widget rebuild loops in Flutter apps using Riverpod state management. Use when: (1) Logs show "RAPID REBUILD" warnings or 50+ rebuilds in seconds, (2) UI becomes unresponsive or shows same content repeatedly, (3) Bug only affects slower devices or poor network conditions, (4) Provider watching creates circular dependencies with router/URL state. Covers ref.watch() overuse, watch+listener redundancy, and transitive dependency chains.

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Generate Isabelle or Coq proofs establishing partial or total correctness of imperative programs from code and formal specifications. Use when users need to: (1) Prove program correctness using Hoare logic, (2) Generate verification conditions from pre/postconditions, (3) Construct loop invariants and termination arguments, (4) Verify imperative programs with assignments, conditionals, and loops. Supports both partial correctness (if terminates, postcondition holds) and total correctness (terminates and postcondition holds) for both Isabelle/HOL and Coq.

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Automatically infer loop invariants for code verification and correctness proofs. Use when analyzing loops to identify properties that hold throughout execution, generating assertions for verification, proving loop correctness, or documenting loop behavior. Supports Python, Java, C/C++, and language-agnostic analysis. Generates invariants as code assertions (assert statements). Triggers when users ask to infer invariants, find loop properties, generate loop assertions, prove loop correctness, or verify loop behavior.

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Generate formal specifications (definitions, predicates, invariants, pre/post-conditions) in Isabelle/HOL or Coq from informal requirements, source code, pseudocode, or mathematical descriptions. Use when users need to: (1) Formalize algorithms or data structures, (2) Create function specifications with contracts, (3) Generate predicates and properties for verification, (4) Translate informal requirements into formal logic, (5) Specify invariants for loops or data structures, or (6) Create formal definitions for mathematical concepts. Supports both Isabelle/HOL and Coq equally.

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Generate abstract Control Flow Graph (CFG) representations of programs showing loops, branches, and function calls for static analysis or verification. Use when users need to: (1) Visualize program control flow structure, (2) Generate CFGs for static analysis tools, (3) Create control flow abstractions for formal verification, (4) Analyze program paths and reachability, (5) Document program structure. Supports both function-level (intraprocedural) and program-level (interprocedural) analysis with multiple output formats (textual, DOT/Graphviz, JSON).

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Zustand state management for React with TypeScript. Use for global state, Redux/Context API migration, localStorage persistence, slices pattern, devtools, Next.js SSR, or encountering hydration errors, TypeScript inference issues, persist middleware problems, infinite render loops.

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TanStack AI (alpha) provider-agnostic type-safe chat with streaming for OpenAI, Anthropic, Gemini, Ollama. Use for chat APIs, React/Solid frontends with useChat/ChatClient, isomorphic tools, tool approval flows, agent loops, multimodal inputs, or troubleshooting streaming and tool definitions.

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Use when auditing a product, business, or project ecosystem — analyzing data sources, decision loops, bottlenecks, and implementation contours. Triggers on "аудит продукта", "product audit", "data audit", "аудит данных", "аудит бизнеса", "проанализируй экосистему", "аудит систем".

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Orchestrate iterative visual style searches with branch prompts, decision graphs, feedback loops, and final direction selection.

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Design and operate bounded multi-agent workflows with task decomposition, dependency graphs, ownership, handoff contracts, shared-state controls, approvals, recovery, and synthesis. Use when a task contains genuinely independent workstreams, specialized roles, parallel research or implementation, reviewer-worker loops, or coordination problems that one agent should not execute sequentially.

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Train machine learning models end-to-end, covering data loading, preprocessing, architecture selection, training loops, validation, and checkpointing. Use when the user requests model training or provides relevant inputs for this workflow.

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Think and work like an expert Machine Learning Engineer. Use when a task calls for Machine Learning Engineer judgment. Reasons from decision-policy framing, Google's Rules of ML, point-in-time data, and prefill/decode inference physics through GBDT/PyTorch baselines, vLLM/SGLang/KServe serving with FP8/AWQ quantization, hybrid-retrieval RAG, RAGAS and human-validated LLM judges, OpenTelemetry GenAI tracing, and post-Omnibus EU AI Act obligations while treating train-serve skew, temporal leakage, prompt injection, LLM nondeterminism, and degenerate feedback loops as first-class failure modes.

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Think and work like an expert Electrical Engineer. Use when a task calls for Electrical Engineer judgment. Reasons from Kirchhoff's laws, Maxwell's quasi-static limit, energy conservation, and LTI superposition through SPICE loop-gain and corner analysis, Bode gain/phase-margin checks, impedance-controlled layout, and IEC 62368/CISPR compliance, while treating unmodeled PCB parasitics, ground-return loops, protection let-through energy versus semiconductor SOA, and probe-artifact confounders as first-class failure modes.

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Design production-ready music prompts and optionally generate music through SandBase. Use for songs, instrumentals, soundtracks, jingles, loops, or multi-part music; use a speech or sound-effect workflow when music is not the main deliverable.

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Plan and direct Midjourney Video V1 image-to-video work through the official website or Discord, with human operator handoff, motion prompts, start/end frames, loops, extensions, resolution and batch budgeting, privacy, rights, safety, and delivery QA. Use when creating Midjourney videos or when determining whether a requested API, automation, plan, or production workflow is supported.

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Produce, direct, generate, QA, and integrate non-speech audio with ElevenLabs Text to Sound Effects / Sound Effects API. Use when an agent needs custom SFX, foley, ambience, loops, stingers, impacts, UI sounds, musical one-shots, trailer braams, or sound-design layers for video, ads, social edits, games, apps, podcasts, audiobooks, or interactive media using ElevenLabs.

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Use for Stability AI Stable Audio production work: selecting Stable Audio hosted API or open-weight models, generating or editing music, loops, sound effects, foley, beds, stingers, and sonic-branding audio from text or source audio, planning rights-safe uploads, setting model parameters, polling asynchronous jobs, and reviewing generated audio for media projects.

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Bulk-query Ensembl BioMart (and other BioMart instances) for cross-database ID mapping, gene/transcript/exon coordinates, and ortholog tables. Use when batch-converting Ensembl IDs to other namespaces (HGNC, RefSeq, UniProt, Entrez), pulling gene coordinate tables for thousands of genes, building ortholog wide-tables across species, or replacing slow Ensembl REST loops with one-shot bulk export. Encodes BioMart's XML query format, R biomaRt vs Python pybiomart trade-off, mart-vs-dataset hierarchy, and the URL endpoint that's BioMart-specific (separate from rest.ensembl.org).

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Workflow for Hi-C and related 3D genomics analyses including compartments, loops, TADs, differential contacts, and visualization.

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Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when: - Implementing self-critique and reflection loops - Building evaluator-optimizer pipelines for quality-critical generation - Creating test-driven code refinement workflows - Designing rubric-based or LLM-as-judge evaluation systems - Adding iterative improvement to agent outputs (code, reports, analysis) - Measuring and improving agent response quality

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Use when the user wants a reusable rolling execution pattern (README + TODO-INDEX) like plans/web-reference-react-refactor, and wants to run delivery in small validated loops.

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Apply when designing commands, lifecycle steps, or processing loops that run amid crashes, restarts, and retries. Converge to the same end state regardless of partial prior runs.

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The fallback workflow for authoring custom HyperFrames video compositions at any length or format — longer or multi-scene pieces, brand / sizzle reels, montages, title cards, static loops, and freeform compositions. Input- and length-agnostic. If a specialized workflow clearly fits the input — a marketed product, a website, a topic explainer, a GitHub PR, existing footage, a short motion graphic, or a Remotion port — prefer it (see /hyperframes); use this only as the general fallback when none fit.

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