knowpatch

name	knowpatch
version	0.4.2
description	LLM knowledge cutoff compensator — knowledge corrections for breaking changes and API drift. Covers: renamed packages (shadcn-ui→shadcn), changed APIs (z.string().email()→z.email()), new config formats (.eslintrc→eslint.config.js), current model IDs, Apple platform changes. Versions are NOT cached — always verify via package manager. Useful for: install, create, scaffold, upgrade, migrate, latest, version, dependency, shadcn, tailwind, vite, zod, eslint, react, next, svelte, nuxt, django, fastapi, pydantic, ruff, uv, claude, gpt, gemini, openai, anthropic, deepseek, qwen, llama, mistral, kimi, minimax, glm, node, bun, python, typescript, macos, xcode, swift, swiftui, supabase, npm, pip, cargo, package.json, pyproject.toml, requirements.txt.

Knowpatch

Knowledge corrections for version-sensitive information that may have drifted since training data.

Table of Contents

1. Purpose
2. Core Principle
3. Activation Protocol
4. Version Lookup Commands
5. When Corrections Are Most Valuable
6. Failure Handling
7. Correction Format
8. Maintenance

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Purpose

This skill addresses the gap between LLM training data and current reality. It provides knowledge corrections — pre-loaded fixes for breaking changes, renamed packages, deprecated APIs, and removed CLI commands that training data gets wrong.

Version numbers are never stored in corrections. Always verify versions via the package manager directly.

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Core Principle

Corrections = knowledge. Versions = package manager.

• Corrections tell you what changed (API renames, breaking changes, deprecated patterns)
• Package managers tell you what version is current (npm view, pip index versions)
• Never trust version numbers from training data or from corrections — always verify

Common pitfalls to avoid:

• Using version numbers recalled from training data without verification
• Claiming something is "the latest version" without checking
• Writing dependency files (package.json, pyproject.toml) with unverified versions

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Activation Protocol

Step 1: Scan correction file tags

Scan YAML frontmatter tags in each corrections file to identify which are relevant to the current task.

Step 2: Read relevant corrections

Read matching corrections files. Check for breaking changes, renamed packages, deprecated APIs, and new patterns.

Step 3: Verify versions via package manager

If package versions are needed, query the package manager directly using the commands below. Never use version numbers from memory.

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Version Lookup Commands

Single Package

Ecosystem	Command
npm	npm view {pkg} version
Bun	bun pm info {pkg} version
Python (PyPI)	pip index versions {pkg} | head -1
Rust (crates.io)	cargo search {pkg} --limit 1
Go	go list -m -versions {module}@latest

Batch Lookup (for project scaffolding)

npm batch:

for pkg in react react-dom typescript tailwindcss vite; do
  echo "$pkg|$(npm view "$pkg" version 2>/dev/null)"
done

Python batch:

for pkg in fastapi pydantic sqlalchemy alembic uvicorn; do
  ver=$(pip index versions "$pkg" 2>/dev/null | head -1 | sed 's/.*(\(.*\))/\1/')
  echo "$pkg|$ver"
done

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When Corrections Are Most Valuable

Corrections provide knowledge that version numbers alone cannot reveal:

• Package renames: shadcn-ui → shadcn (querying old name may still return results)
• CLI command changes: npx shadcn-ui@latest → npx shadcn@latest
• API breaking changes: z.string().email() → z.email() (version number alone doesn't reveal this)
• Config format changes: .eslintrc → eslint.config.js
• AI model IDs: Cannot be looked up via package managers — corrections are the source of truth
• Open-source model recommendations: Training data recommends outdated models (Llama, Mistral) — corrections provide current frontier options with selection guide
• Platform API changes: Supabase key renames, JWT verification pattern changes
• Apple platform changes: macOS version naming (15→26), Liquid Glass design system, Swift concurrency model, Metal API generation

Check Corrections When:

• Installing or adding packages
• Creating or scaffolding a new project
• Writing code that uses library APIs known to have breaking changes
• Referencing AI model names or IDs
• Recommending open-source or self-hosted LLMs
• Using CLI tools (shadcn, tailwind, eslint, vite)
• Upgrading or migrating dependencies
• Writing Swift/SwiftUI code or targeting Apple platforms
• Referencing macOS version numbers or deployment targets

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Failure Handling

Situation	Action
Lookup command succeeds	Use the returned version
Lookup command fails (tool not installed)	Try fallback command from the table above
All commands fail	State uncertainty clearly, ask user to verify

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Correction Format

Each correction entry in corrections/ files follows this format:

### {Topic} — {YYYY-MM}
- **Wrong (training data)**: {What the model would answer}
- **Correct (current)**: {Actual current state}
- **Impact**: {What goes wrong if outdated info is used}
- **Lookup**: {Package manager verification command}

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Maintenance

Adding New Corrections

1. Identify the relevant corrections file (scan frontmatter tags)
2. Add entry using the standard format above
3. Update the last_updated date in the YAML frontmatter
4. Add new tags to the frontmatter tags array if necessary

Deletion Criteria

• Entries older than 2 years: model training data may have caught up — consider removal
• Tools no longer in use: remove entries

Discoveries During Session

When a live lookup result differs from training data:

• Suggest updating the corrections file to the user
• Ask "Should I add this to corrections?"

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Corrections Index

Each corrections file contains YAML frontmatter with tags for keyword routing. Scan frontmatter to find relevant files.