数据与 AI技能

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 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 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 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 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 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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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 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 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 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 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 you write the `context` and `design` sections of an analiz report - components, exact interfaces, data flow, errors, decision record, out of scope

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Use when writing or changing a query against PostgreSQL from Go — avoiding N+1 loops, reading EXPLAIN output, indexing, and keyset pagination

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Use when a Flutter screen needs app state or data - separate presentation from state, keep business logic out of widgets, and follow the app's existing state solution (Riverpod 3 Notifier/AsyncNotifier, or the official ChangeNotifier+ListenableBuilder MVVM pattern)

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Build an RL environment / eval that trains or measures a skill in any domain (finance, medicine, law, math, science, a niche industry) from real cases that Browser Use agents mine from the web, then baseline and hillclimb a model on it. Use when the user knows a domain and what a model should be able to do in it, but not how to get the data or build the environment.

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Behavioral guardrails to reduce common LLM coding mistakes with a caution-first approach.

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Author and validate Neo4j Ontology Graph Specification (ontology graph spec) documents. Use when writing or editing an ontology, defining nodes and relationships with properties, Neo4jType data types, constraints (key/unique/must_exist), labels, and endpoint cardinality, in YAML or JSON, or validating an ontology document against the live-resolved spec schema. Does NOT handle Cypher or loading an ontology into a database — use neo4j-cypher-skill. Does NOT cover generic graph data modeling advice — use neo4j-modeling-skill.

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