loom-data-validation

loom-data-validation is a skill for Claude Code, Codex from cosmix/loom. It costs 23 tokens per session (3,954 once invoked), scanned A, original, MIT.

A guide for checking data at the points where it enters or leaves a system, such as web requests, files, queues, and data pipelines. It covers allowed formats, cleaning unsafe input, safe output, and converting values to expected types.

In plain words
What is it for?
Use it for forms, APIs, command-line input, file imports, service-to-service messages, data pipelines, and machine-learning feature data.
Why use it?
It prevents malformed or hostile data from reaching internal code, while avoiding incomplete checks that attackers can bypass through alternate encodings or formats.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/cosmix/loom/loom-data-validation
Any agent
npx skills add cosmix/loom --skill loom-data-validation
Clone the repo
git clone --depth 1 https://github.com/cosmix/loom

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for loom-data-validation

README.md
[![agentmods](https://agentmods.dev/badge/skills/cosmix/loom/loom-data-validation.svg)](https://agentmods.dev/skills/cosmix/loom/loom-data-validation)
Your own site
<a href="https://agentmods.dev/skills/cosmix/loom/loom-data-validation"><img src="https://agentmods.dev/badge/skills/cosmix/loom/loom-data-validation.svg" alt="Measured on agentmods" height="20"></a>
Per session 23 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,954 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00023 $0.03954
Opus 5 $0.00012 $0.01977
Sonnet 5 $0.00005 $0.00791
Haiku 4.5 $0.00002 $0.00395

Measured today against content hash 69bca7efcd3c, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

loom-data-validation 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 today.

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.

skills/loom-data-validation/SKILL.md · 268 lines

How it starts

The opening of the file, as written. The whole thing — 268 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Data Validation

Overview

Validate untrusted data at trust boundaries before it flows into your system. This skill covers schema libraries (Zod/Pydantic/Joi/JSON Schema), coercion pitfalls, context-dependent output encoding, injection/XSS/DoS defenses, and pipeline/ML feature validation.

Core principles (read first)

  • Parse, don't validate. A validator that returns bool throws away work — the caller re-parses or trusts blindly. Return a typed value (Result<User>, User | errors) so downstream code cannot receive unvalidated data. Schema libraries (Zod .parse, Pydantic .model_validate) do this by construction.
  • Validate at the boundary, once, then trust the typed value inward. Boundaries: HTTP handlers, queue consumers, file/CLI parsers, pipeline ingestion, cross-service calls.
  • Server-side is authoritative; client-side validation is UX only. Never rely on it for security — attackers bypass the client entirely.
  • Allowlist > denylist. Enumerate what's permitted (enum, char classes, known hosts). Denylists (blocking <script>, ../, ') are always incomplete — encodings, Unicode, and case defeat them.
  • Canonicalize before validating. Normalize Unicode (NFC), lowercase host, resolve ./.. in paths, decode percent-encoding — then check. Validating raw input lets %2e%2e%2f or (fullwidth) slip past.
  • Encoding ≠ validation. Validation decides accept/reject; encoding makes a value safe for a specific sink (HTML vs attribute vs JS vs URL vs shell vs SQL). A value can be valid and still need encoding at every sink.
  • Limits are validation. Cap length, array size, object depth, and total payload bytes to stop DoS (JSON bombs, deeply nested payloads, ReDoS amplification).

Zod (TypeScript)

safeParse returns a discriminated result (no throw); parse throws ZodError. Prefer safeParse at boundaries.

import { z } from "zod";

const CreateUser = z
  .object({
    email: z.string().trim().toLowerCase().email().max(255),
    password: z.string().min(12).max(128)
      .regex(/[a-z]/).regex(/[A-Z]/).regex(/\d/).regex(/[^A-Za-z0-9]/),
    role: z.enum(["user", "admin", "moderator"]).default("user"),
    tags: z.array(z.string().max(50)).max(10).default([]),
    age: z.number().int().min(13).max(150).optional(),
  })
  .strict();            // reject unknown keys → blocks mass-assignment/overposting

type CreateUserIn = z.input<typeof CreateUser>;   // pre-transform
type CreateUserOut = z.output<typeof CreateUser>; // post-transform (use this downstream)

const parsed = CreateUser.safeParse(req.body);
if (!parsed.success) {
  const errors = parsed.error.issues.map((e) => ({ field: e.path.join("."), message: e.message }));
  return res.status(422).json({ errors });
}
const user = parsed.data; // fully typed + validated

Read the full file on GitHub · 268 lines

Changes

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.

  1. today Changed · -33 tokens per session 69bca7efcd3c
  2. 3d ago First seen · 268 lines · 56 tokens per session scan A be250cdc2e3e

Subscribe to this mod's changes

loom-data-validation is a skill published in the GitHub repository cosmix/loom (54 stars, last pushed today), licensed MIT. It adds 23 tokens to every session and 3,954 once invoked, about $0.0001 per session on Opus 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 2026-08-30.

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