Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add mickeyyaya/refactoring-skills --skill data-validation-schema-patternsgit clone --depth 1 https://github.com/mickeyyaya/refactoring-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/mickeyyaya/refactoring-skills/data-validation-schema-patterns)<a href="https://agentmods.dev/skills/mickeyyaya/refactoring-skills/data-validation-schema-patterns"><img src="https://agentmods.dev/badge/skills/mickeyyaya/refactoring-skills/data-validation-schema-patterns/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/mickeyyaya/refactoring-skills/data-validation-schema-patterns"><img src="https://agentmods.dev/badge/skills/mickeyyaya/refactoring-skills/data-validation-schema-patterns.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00090 | $0.05239 |
| Opus 5.5 | $0.00036 | $0.02096 |
| Sonnet 5.5 | $0.00018 | $0.01048 |
| Haiku 4.5 | $0.00009 | $0.00524 |
Grade A, and why
data-validation-schema-patterns scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 641 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Validation and Schema Patterns for Code Review
Overview
Unvalidated input is the root cause of injection attacks, data corruption, and unexpected crashes. Validation is not a one-time gate at the UI layer — every system boundary must enforce its own schema contract. Use this guide during code review to catch validation hazards before they ship.
When to use: Reviewing code that accepts external data (HTTP requests, queue messages, file uploads, CLI args, environment variables); designing API contracts; evaluating schema evolution for backward compatibility; auditing trust boundaries in internal services.
Quick Reference
| Pattern | Core Idea | Primary Red Flag |
|---|---|---|
| Validation Boundary | Validate at every trust boundary, not just at the UI | Passing raw unknown / any deep into business logic |
| TypeScript / Zod | Runtime schema tied to compile-time type | z.any() escapes, skipping .parse() on external data |
| TypeScript / Joi | Rich rule DSL with detailed error messages | .unknown(true) without explicit allow-list |
| TypeScript / io-ts | Codec = decoder + encoder, composable | Ignoring left branch of Either decode result |
| Python / Pydantic v2 | Model-first validation, high performance | model_config = {'arbitrary_types_allowed': True} masking issues |
| Python / marshmallow | Schema-centric, explicit serialization control | load() result used without checking validation errors |
| Python / dataclasses | Structural typing only, no runtime enforcement | Trusting @dataclass fields have the declared type at runtime |
| Go / validator | Struct tag-based declarative rules | Missing binding:"required" tags on mandatory fields |
| Java / Bean Validation | Annotation-driven, integrates with frameworks | @Valid missing on nested objects or method parameters |
| Schema Evolution | Additive changes are safe; removals and renames break | Removing required fields without a deprecation cycle |
| Strict vs Coercive | Coercion silently accepts wrong types; strict fails fast | z.coerce.number() accepting "abc" → NaN |
| Custom Validators | Encode business rules as first-class schema constraints | Business rule checks scattered outside schema definition |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 641 lines · 90 tokens per session scan A e2bcaea048da
data-validation-schema-patterns is a skill published in the GitHub repository mickeyyaya/refactoring-skills (6 stars, last pushed 7mo ago), licensed MIT. It adds 90 tokens to every session and 5,239 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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