Data & AI skills

Use scipy.optimize.curve_fit for nonlinear least squares parameter estimation from experimental data.

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Comprehensive clinical laboratory data harmonization for multi-source healthcare analytics. Convert between US conventional and SI units, standardize numeric formats, and clean data quality issues. This skill should be used when you need to harmonize lab values from different sources, convert units for clinical analysis, fix formatting inconsistencies (scientific notation, decimal separators, whitespace), or prepare lab panels for research.

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Power system network data formats and topology. Use when parsing bus, generator, and branch data for power flow analysis.

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Use this skill when reading sensor data from CSV files, writing simulation results to CSV, processing time-series data with pandas, or handling missing values in datasets.

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Analyzes 3D mesh files (STL) to calculate geometric properties (volume, components) and extract attribute data. Use this skill to process noisy 3D scan data and filter debris.

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Create, compose, migrate, and review TypeScript runtime type guards with is-kit. Use when adding reusable guards; replacing typeof, null checks, or handwritten type predicates; narrowing unknown values; validating JSON or API responses; building collection or object schemas with struct or typedStruct; or reviewing existing is-kit usage. Do not use for one-off environment checks or validation that requires data transformation or rich error reporting.

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Deep expertise in ML/CV model selection, training pipelines, and inference architecture. Use when designing machine learning systems, computer vision pipelines, or AI-powered features.

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Transform clarified user requests into structured delegation prompts optimized for specialist agents (cto-architect, strategic-cto-mentor, cv-ml-architect). Use after clarification is complete, before routing to specialist agents. Ensures agents receive complete context for effective work.

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Use when building or extending a C# service on ASP.NET Core - Minimal APIs or controllers, DI and options, EF Core with Npgsql, ProblemDetails, HttpClientFactory resilience, async and cancellation

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Use when the diff stores, caches, indexes, exports, imports, migrates, soft-deletes or revokes access-controlled or multi-tenant data - policy vs query disagreement, bypass clients, tenant-less cache and object keys, signed links, derived copies (search, cache, logs, analytics), export/restore scope, migration defaults, and stale access after delete or revoke

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Use when the diff adds or moves a credential, logs or returns data, builds a URL or argv with a token, generates a random value, encrypts, signs, hashes, configures TLS or writes a credential file

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Use when the diff builds a query, a shell command or argv, a subprocess environment, a template, an XML parse, an eval or a deserialization from data that may be attacker-controlled - SQL/NoSQL, OS command, argument, env-var, template and code injection, XXE, unsafe deserialization

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Use when writing Godot 4 GDScript - static typing everywhere, scene composition with component nodes, call down/signal up, @onready caching, Resources as data duplicated per instance, autoloads only for services, state machines, pooling, physics in _physics_process, and threaded loading.

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Use when adding or changing tuning values, items, enemies, abilities or levels - put them in the engine's data assets (ScriptableObject, Resource, DataAsset/DataTable, JSON/RON) with validation and stable IDs, keep runtime state out of shared data, inject services instead of singletons, and test data invariants.

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Use when writing or restructuring Unity C# code - assembly definitions and dependency direction, small components with cached references, ScriptableObjects for data and events, DI instead of singletons, Input System, Addressables, serialization safety and Unity 6 API changes.

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Use when you meet a new dataset, table, file or source, before building features or a model on it, or when a number looks wrong — profile grain, keys, nulls, cardinality, time coverage and target balance, and persist what you learn as code

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Use when a task asks for a model, a score, a forecast or a prediction-driven feature — pin down the decision, target, unit, prediction time, metric and baseline before writing any modelling code

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Use when dataframe code is slow or runs out of memory, when data outgrows pandas, or when writing pandas 3, Polars or DuckDB code — vectorisation, dtypes, Copy-on-Write, lazy and out-of-core execution, and measuring before and after

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Use when adding or changing a scheduled data or ML pipeline — Dagster assets, Prefect flows, Airflow DAGs or a cron'd entrypoint — idempotent partitioned stages, retries, backfills, quality gates, and testing the pipeline without the scheduler

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Use when starting a Python data or ML project, adding its first tooling, or changing pyproject.toml, the lockfile, the package layout or the lint/type/test configuration — detect the repo's tooling first, then uv, ruff, mypy, pytest and a src layout

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Use when tools from the jupyter, duckdb, dbt, mlflow, huggingface or postgres MCP servers appear in your tool list, or before reaching for one — what each is good for, what it can damage, and the CLI fallback when it is not connected

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Use when designing or analysing an A/B test, computing power or sample size, testing whether a difference is real, or writing any statistical claim — effect sizes with intervals, SRM checks, multiple comparisons and causal caveats

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Use when data crosses a boundary into your code (file, API, warehouse table, upload, model input or output), when a join or cleaning step can change row counts, or when adding pandera, Great Expectations, Pydantic or dbt checks

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Use when writing or changing analytical SQL or a dbt model — CTE structure, window functions, grain and join correctness, dbt data tests, unit tests, contracts and incremental models, and how to run them

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