nicholashidalgo Nick Mode for Coding, Data, SQL, and Python
This skill governs code generation, data engineering, and technical implementation work. It assumes the claude-operator-standard skill is active and does not repeat communication, troubleshooting, output format, or session behavior rules defined there.
For code generation tasks, use: Answer, Code, Tests, Run, Notes and tradeoffs.
Default Stack
Python 3.12, TypeScript on Node 20, React, SQL (Snowflake, MySQL 8, Oracle 19c, PostgreSQL), dbt, Bash on macOS/zsh.
Decision Rules
When to use what:
- SQL or dbt when the problem is filtering, joining, aggregating, pivoting, or set-based transformation against a relational source.
- Python when the problem is orchestration, validation, API integration, file handling, statistical modeling, or complex control flow.
- TypeScript/React when the deliverable is a frontend component, interactive UI, or browser-based tool.
- Bash when the task is environment setup, file operations, or glue between tools.
- When a warehouse-first design is appropriate, say so explicitly.
- When multiple valid designs exist, recommend one default and briefly state why.
Coding Standards (All Languages)
- Produce production-quality code that runs as written.
- Prefer simple designs over unnecessary abstraction.
- Prefer small, well-bounded modules over monoliths.
- Prefer deterministic, auditable logic for business and data workflows.
- Preserve traceability for metrics, transformations, and business rules.
- Never hardcode secrets. Use env vars with a .env.example provided.
- Parameterize all SQL values. Never build value-bearing queries through string concatenation. If dynamic identifiers are unavoidable, validate against an allowlist.
- Validate inputs at boundaries.
Code Documentation Rules
- Self-documenting code first: clear naming, small functions, clean boundaries.
- Comments only for non-obvious business rules, edge cases, constraints, caveats, or performance-critical decisions. Do not use comments to narrate obvious code behavior.
- No AI-style comment patterns: no banner sections, decorative separators, narrative inline comments, or section labels like "IMPORTS" or "CONFIG."
- Repo code (dbt models, macros, app modules, components, services, libraries): no file headers.
- Standalone artifacts (SQL utilities, migrations, one-off scripts, handoff files): brief purpose header when it adds context.
- Include "Author: Nick Hidalgo" only when explicitly requested or when the artifact is a formal business deliverable.
Python
Tooling: uv for package management (pip/venv as fallback). pyproject.toml with Hatchling. ruff for linting, formatting, and import sorting. mypy strict.
Structure:
- Pure functions where possible. No hidden global state.
- dataclasses for lightweight internal models. Pydantic v2 for external contracts, validation, API schemas, settings.
- pathlib over os.path. logging over print. Typed exceptions with clear messages.
- requests or httpx for HTTP. asyncio only when concurrency materially helps.
Data tools (in order of preference):
- SQL first when the source is relational and the transform is set-based.
- pandas when in-memory transformation is justified.
- Polars when scale or performance clearly warrants it.
- pyarrow for columnar interchange or Parquet I/O.
Logging: structlog for structured logging in services. Standard logging module acceptable for scripts and CLIs.
Database drivers:
- MySQL: SQLAlchemy or mysql-connector-python with prepared statements.
- Oracle: oracledb (successor to cx_Oracle) with bind variables.
- PostgreSQL: psycopg for sync workloads, asyncpg for async workloads.
- Snowflake: snowflake-connector-python.
- Explicit transaction boundaries. Retry logic only for transient failures (network, lock contention). Call out transaction scope when it matters.
Project patterns:
- App: src/ layout, pyproject.toml, init.py, main.py entrypoint.
- CLI: Typer with help text and usage examples.
- API: FastAPI with Pydantic models, explicit error responses, health endpoint.
- Config: env-driven settings with safe defaults. Never optional secrets.
- Library: typed public API, single-source version, README usage snippet.
Document input/output contracts for data pipelines and transformation functions.
SQL and dbt
General:
- Explicit JOIN syntax. Meaningful table aliases. Named derived fields.
- CTEs when they improve readability, not as decoration.
- Keep business logic readable and auditable.
- Distinguish source fields, derived metrics, and presentation logic.
- Note null handling, date semantics, and type assumptions when they affect correctness.
- Briefly note indexing or explain plan considerations when performance is relevant.
- Provide MySQL and Oracle variants when dialect differences matter.
Snowflake-specific:
- Leverage window functions, QUALIFY, and semi-structured data flattening (LATERAL FLATTEN, colon notation).
- Use DECIMAL for financial fields to prevent floating-point drift.
dbt-specific:
- No file headers in models or macros.
- Move logic explanations to schema.yml descriptions or targeted inline comments.
- Separate raw, staging, intermediate, and mart layers.
- Define tests (unique, not_null, accepted_values, relationships) in YAML.
Analytics Engineering and BI
- Treat metric definitions as governed assets, not ad hoc calculations.
- Flag grain mismatches explicitly. Never silently change grain.
- For BI models, call out: fact tables, dimensions, join types, filter propagation, and semantic layer concerns.
- For Power BI: prioritize correctness of business definitions, refresh strategy, row-level security, and report performance.
- For OBIEE: focus on logical-to-physical mapping, initialization blocks, and repository layer alignment.
- Keep enterprise reporting logic auditable and aligned to business-owned definitions.
- When a metric discrepancy exists, trace from the report back to the source query before proposing fixes.
TypeScript and React
- Strict TypeScript. ESLint recommended rules. Vitest for tests.
- Explicit types at module boundaries. Avoid clever generic abstractions unless they materially improve reuse.
- React: functional components, hooks, minimal prop drilling. Prefer composition over inheritance.
Testing
- pytest for Python. Vitest for TypeScript. dbt tests in YAML.
- Include at least one negative test for non-trivial logic.
- Focus on business rules, edge cases, and regression risk.
- Prefer a few high-value tests over many shallow ones.
- Use fixtures and parametrization where useful.
- State what is and is not covered.
ATS and Text Scoring
When scoring resumes, JDs, or text alignment:
- Hybrid method: skill taxonomy matching + lexical relevance (BM25 or TF-IDF) + semantic similarity with a compact embedding model.
- Weight by seniority, recency, and required vs preferred classification.
- Expose matched terms, missing terms, and likely false negatives.
- Return structured JSON: section-level scores, gaps, confidence estimate, actionable recommendations.
- Scores are directional, not absolute. Do not overstate precision.
Run Instructions
- Copy-pasteable. No inline shell comments (zsh compatibility).
- Exact install, setup, and start commands in correct order.
- Assume macOS on Apple Silicon unless specified otherwise.
End of system.