weco-content-integrity

Enforces reversible, verifiable bulk operations on a 31,000-item task bank.

Updated Jun 13, 2026
One-click install
npx skills add https://github.com/malinovskiy-makar/qls --skill weco-content-integrity-malinovskiy-makar
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: weco-content-integrity
Source: https://github.com/malinovskiy-makar/qls/tree/main/.claude/skills/weco-content-integrity
Command: npx skills add https://github.com/malinovskiy-makar/qls --skill weco-content-integrity-malinovskiy-makar

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Bulk imports, deduplication, and mass text edits on a large task bank can silently corrupt thousands of fields, and naive AI-driven sweeps have already damaged content. This Skill enforces a strict safety protocol so every mass operation stays reversible, measurable, and human-reviewed. ## Core Features & Use Cases - Mandatory dry-run workflow: Commands default to --dry-run, require explicit --apply flags, before/after counters, and a human-reviewed HTML diff of 15-20 sample changes. - Reversibility guarantees: Snapshots of old values (JSON id-to-text mapping) plus database backups, idempotency checks, and differential invariant validation (task counts, KaTeX $ parity, brace balance). - Hard prohibitions: Blocks model-driven mass rewrites of statement/answer/solution fields, unreviewed applies, and irreversible task deletion without owner approval. - Use Case: Before running a deduplication or import command on the production task bank, activate this Skill to generate counters, a preview diff, a backup path, and an owner sign-off gate before any data is written. ## Quick Start Review my import command for the task bank and make sure it follows the dry-run, backup, and owner-approval protocol before I run it on production.

Frequently Asked Questions about weco-content-integrity

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I safely run a bulk edit on a large task database?

Default every command to --dry-run and require an explicit --apply flag for live runs. Produce before/after counters, an HTML diff of 15-20 sample changes, and a JSON snapshot of old values plus a database backup before applying anything.

How to deduplicate imported tasks without corrupting existing data?

Normalize text before computing content_hash so deduplication actually matches, and address subitems by primary key rather than labels since labels are not unique. Wrap each risky creation in its own transaction.atomic() to avoid SQLite savepoint poisoning.

Can AI models mass-rewrite task statement or answer fields?

No, model-driven mass rewrites of statement, answer, or solution fields are prohibited because a previous sweep corrupted 233 fields across 195 tasks. Models may only write new fields, never modify existing content at scale.

Why does my embedding recalculation silently skip updated tasks?

The night_embeddings job silently skips any id already recorded in its done-file and reports zero remaining. Remove the changed ids from the done-file first, then rerun the embedding job.

When should imported tasks be published versus drafted?

Set status='draft' for any source of questionable quality; published status must be a deliberate decision about a specific source. This keeps unverified content out of the live bank while preserving reversibility.