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Build OCR pipelines in MATLAB using the ocr() function. Use this skill when the user wants to read text from images, documents, signs, meters, displays, license plates, gauges, receipts, or seven-segment displays. Covers image preprocessing, text detection (CRAFT, MSER), ROI-based recognition, multi-language OCR, and custom model training. Use when: OCR, text recognition, extract text from image, character recognition, document scanning, meter reading, gauge reading, receipt scanning, digitize text from photo.

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Extract features from signals collected on rotating machinery components, including motors, pumps, fans, gears, bearings, and shafts. Signals can include vibration, electrical, or environmental sensor measurements. Use when developing and deploying condition monitoring and fault detection applications for rotating machinery, including industrial machines, electrical vehicles, internal combustion engines, turbines, and drive trains.

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Set up and manage NVIDIA GPU servers for AI workloads — driver installation, CUDA toolkit, container toolkit, MIG partitioning, GPU health monitoring, and multi-GPU configuration for LLM inference and training.

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Operate GPU-backed Kubernetes clusters for AI inference and training with scheduling, autoscaling, node health, MIG partitioning, and cost controls.

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Orchestrate AI/ML pipelines for data ingestion, model training, batch inference, and RAG indexing using Prefect, Airflow, or Dagster. Build reliable, observable, and retriable workflows for production AI systems.

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Use ctx as working memory for prior agent work. Before starting or continuing work in an unfamiliar area, resuming earlier work, or revisiting an investigation, search prior sessions to ground yourself in earlier context. Use ctx blame to trace a line, file, commit, or PR to the agent session that produced it. Use when prior decisions, constraints, attempts, tool calls, transcript evidence, or code provenance may matter. Use ctx graph queries on indexed projects to follow calls and dependencies and understand what a change might affect.

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Calibrated interrogation of a fuzzy idea before it becomes a spec. Asks one question at a time, recommends an answer, and lands each answer in §G (goal) or §C (constraints) — unknowns parked as `?` items, never guessed. The cheapest place to kill a bad idea is before §T exists. Triggers when the user has a vague idea, says "grill me", "stress-test this", "challenge my plan", "interview me before I spec", or invokes /ck:grill. Defers the actual write to the spec skill.

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Apply systematic problem-solving techniques when stuck. Use for complexity spirals, innovation blocks, recurring patterns, assumption constraints, simplification cascades, scale uncertainty.

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Transforms vague prompts into optimized Claude Code prompts. Adds verification, specific context, constraints, and proper phasing. Invoke with /best-practices.

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Writing efficient code that handles large data and tight constraints

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Create comprehensive business strategy with premise deconstruction, business model stress test, pricing, go-to-market plan, and competitive positioning. Runs a 4-layer premise audit before strategy, then generates a full market research report with SWOT, 4P, 10-point business model validation, and constraint analysis. Use when the user has an idea and needs a strategic plan, competitive analysis, pricing strategy, GTM plan, or says 'business plan', 'strategy', 'pricing', 'go-to-market', or 'competitive analysis'.

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Deep business diagnosis for when things aren't working. Uses Socratic questioning, first-principles reasoning, and constraint analysis to find WHY a business is stuck — not just prescribe solutions. Use when the user says 'my business isn't working', 'I'm stuck', 'what's wrong', 'diagnose my business', 'why am I not growing', 'help me figure out the problem', or describes a specific business challenge.

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Generate database seed scripts with realistic sample data. Reads Drizzle schemas or SQL migrations, respects foreign key ordering, produces idempotent TypeScript or SQL seed files. Handles D1 batch limits, unique constraints, and domain-appropriate data. Use when populating dev/demo/test databases. Triggers: 'seed database', 'seed data', 'sample data', 'populate database', 'db seed', 'test data', 'demo data', 'generate fixtures'.

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LP, MILP, QP — concepts, problem-text parsing, and formulation patterns (parameters, constraints, decisions, objective). Concepts only; no API.

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Parse a mathematical-modeling problem into goals, objects, data, constraints, outputs, subquestions, dependencies, variables, relationships, and human-confirmed success criteria before any method selection.

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Review, run, debug, and verify approved MATLAB or Beita Tianyuan modeling code against its plan, data contract, method decision, compatibility constraints, and experiment outputs, saving one compact JSON review.

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Use when one look must hold across Scenario generations: one character across scenes, a turnaround, or a video animated from its references, one product across angles, one style across icon sets, a character from an uploaded drawing, a variant off an approved baseline, or deciding when references stop scaling and a trained model is due. Triggers: make this match, same character, on-model, reference to video, style reference. Keywords: consistency, identity, control map, seed, LoRA.

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Compile a PAM control's Android Kotlin module into the runtime-loadable DEX for a `.ppmplugin`. Creates a staged Gradle build with the pinned wrapper and `react-android` compile dependency, verifies manifest/module/package alignment and runtime-loading constraints, builds the release AAR, then runs `d8 --min-api 24`. Writes `ppmplugin/staging/android/<PascalName>Plugin.dex` and surfaces actionable Gradle or d8 failures. Requires JDK 17+ and Android SDK Build-Tools 35.0.0; `d8` is located from the SDK and system Gradle is optional. Run after /generate-ppmplugin-manifest and before /assemble-ppmplugin.

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Walk the user through four directional axes (tone register, aesthetic philosophy, audience relationship, sensory ambition) and produce a structured aesthetic brief that downstream content, copy, design, and art-direction skills consume as required input. The aesthetic depth layer, distinct from `creative-brief` (operational kickoff: scope, audience, deliverables, constraints). Use when a project needs aesthetic coherence across many small decisions and the user has only a vague feeling, or when multiple downstream aesthetic-producing skills need a shared brief. Triggers on creative direction, aesthetic direction, set the aesthetic, define the visual direction, what's the vibe, what's the tone, the four axes. Does NOT fire for a general kickoff brief (use `creative-brief`), tactical single-piece work, already-documented direction, purely functional output, or locked production-stage work.

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Plan how to earn customer trust and appropriate access. Use when credibility, permissions or AI policy constrain the engagement.

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Compare feasible approaches to a customer problem and recommend a path with costs, constraints and evidence. Use for an architecture or delivery decision, not implementation.

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Clarify a new customer brief, desired outcome, constraints and evidence gaps. Use for kickoff or a first meeting; do not repeat discovery already supplied.

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Constraint-based metabolic modeling and flux balance analysis

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Designs production-grade ML pipeline infrastructure with experiment tracking, Kubeflow/Airflow DAGs, feature store schemas, model registries, and automated retraining workflows.

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