# Rseng Defensive Coding > Covers defenses against silently wrong research results: validating data at boundaries (schemas, assertions, sanity checks), explicit physical units and quantities in code (pint/astropy-style), disciplined randomness (explicit seeded generators, parallel streams), and fail-loud handling of NaN and missing data. Use PROACTIVELY when code ingests external or instrument data, when values carry physical units, when randomness enters simulations or sampling, or when NaN or missing-data handling is implicit; also when the user mentions data validation, unit errors, seeds or silent bugs, or reviews analysis code whose failure would be invisible. For floating-point behavior and tolerances see rseng-numerical-accuracy; for diagnosing an existing bug see rseng-debugging. - Skill: `fdiblen/rseng-defensive-coding` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add fdiblen/rseng-defensive-coding` - Raw SKILL.md: https://api.skillmd.com/api/skills/fdiblen/rseng-defensive-coding/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Research & Search - License: CC-BY-4.0 - Author: fdiblen (https://skillmd.com/u/fdiblen) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/fdiblen/rseng-defensive-coding --- # Defensive coding for research software Research code's worst bug is the one that runs to completion: the unit mismatch, the silently dropped rows, the column shifted by one, the reused random stream - producing plausible, wrong numbers that reach a paper. Defensive coding is the discipline of making wrongness LOUD: validate at boundaries, make implicit physics and randomness explicit, and prefer a crash today to a correction notice next year. It complements rseng-numerical-accuracy (float behavior) and feeds rseng-testing (the checks become tests). ## Validate at the boundaries Data enters code at boundaries - files, instruments, APIs, user parameters - and every boundary is a corruption opportunity: - Schema-check tabular data on ingest: expected columns, dtypes, units, ranges, allowed categories, uniqueness of keys (pandera-style declarative schemas for DataFrames; JSON Schema for configs and records). The schema is executable documentation of the data contract (rseng-data-management's data dictionary, enforced). - Sanity-check the science, not just the types: physical ranges (no negative concentrations, latitudes within +/-90), plausible magnitudes, conservation totals, expected row counts within tolerance of yesterday's. Domain assertions catch what dtype checks cannot. - Missing data is a decision, not a default: silent NaN propagation and silent row-dropping (the pandas default in many operations) are the classic silent killers - count and REPORT what was excluded and why, and make exclusion rules explicit code (rseng-research-integrity checks this at the manuscript end; this skill prevents it at the source). - Fail loud, fail early: raise on contract violations at ingest rather than letting bad data flow downstream; in batch settings quarantine-and-log per item (rseng-big-data-processing) - but never silently skip. ## Units and quantities Unit errors are the canonical silent research bug - flagship missions have died of them: - Attach units in code, not in comments: a quantity library (pint; astropy.units in astronomy) makes units part of the value, checked at every operation - adding meters to seconds raises instead of publishing. - Enforce at boundaries even when the core stays plain-numeric for performance: convert-and-strip on ingest (asserting the expected unit), reattach on output, and document the internal convention in ONE place (rseng-scientific-file-formats' metadata discipline carries units in the files themselves). - Degrees vs radians, per-second vs per-minute, and log-vs-linear are unit bugs in spirit: name them in variable names or types when a full quantity system is overkill (angle_rad, rate_per_s). ## Disciplined randomness - Explicit generators, never global state: create a seeded RNG object (numpy's Generator API) and pass it - seeding the global makes order-dependent, library-colliding randomness (the Scientific Python RNG guidance is the reference). - Parallel work gets derived streams: spawn per-worker generators from a master seed (seed sequences), never the same seed in every worker - identical streams across "independent" replicates is a silent statistics-destroyer (rseng-hpc-computing job arrays included). - Seeds are provenance: record them with outputs and in configs (rseng-reproducibility owns the bookkeeping); stochastic tests assert distributions or use fixed seeds knowingly (rseng-testing). ## The habit, proportioned Not every script needs schemas: exploration code needs only the cheap habits (explicit NaN policy, named units, seeded RNGs); shared pipelines add ingest validation; published analyses add the full contract checks in CI (rseng-ci-cd) so drift in upstream data sources is caught at the pull request, not in the plot. When a defensive check fires in production, keep it as a regression test - each catch is a documented failure mode (rseng-trainer's errors-are-curriculum applies: explain what the check just prevented). ## Working with this skill This skill is source-independent: its authority is the tool documentation and Scientific Python guidance linked below. It is the prevention layer for the failure modes rseng-research-integrity hunts post-hoc. Learn more (verified): - https://pandera.readthedocs.io - pandera DataFrame validation - https://pint.readthedocs.io - pint physical quantities - https://json-schema.org - JSON Schema for configs and records - https://docs.astropy.org/en/stable/ - astropy (units module) - https://blog.scientific-python.org/numpy/numpy-rng/ - Scientific Python guidance on NumPy random number generators ## Related skills Check whether any of these applies before moving on: - rseng-big-data-processing - quarantine-and-log at batch scale - rseng-data-management - data dictionaries the schemas enforce - rseng-hpc-computing - per-worker RNG streams in parallel jobs - rseng-numerical-accuracy - float behavior behind silent errors - rseng-research-integrity - post-hoc hunt for same failures - rseng-testing - fired checks become regression tests