Trending-skills anti-distill-skill-sanitizer

---
name: anti-distill-skill-sanitizer
description: Strip core knowledge from AI Skill files before submitting to your company, while preserving a private backup of your real expertise.
triggers:
  - sanitize my skill file
  - anti-distill my knowledge document
  - clean my skill before submitting
  - strip core knowledge from skill file
  - hollow out my skill file for company submission
  - run anti-distill on this document
  - help me redact my skill file
  - protect my expertise from being distilled
---

# Anti-Distill Skill Sanitizer

> Skill by [ara.so](https://ara.so) — Daily 2026 Skills collection.

Anti-Distill is a counter-tool for employees required to write AI Skill files for their company. It reads your real Skill document, identifies genuinely valuable knowledge, replaces it with professional-sounding but hollow filler, and outputs two files: a sanitized version to submit and a private backup containing everything that was removed.

---

## What It Does

1. **Reads** your Skill file (colleague-skill format or any knowledge document)
2. **Classifies** each section by replaceability: generic boilerplate vs. hard-won specific knowledge
3. **Dilutes** core knowledge — replaces it with correct-but-useless filler text
4. **Outputs two files**:
   - `*_sanitized.md` — submit this to your company
   - `*_private_backup.md` — keep this; it contains everything real

---

## Installation

### Claude Code (project-level)

```bash
mkdir -p .claude/skills
git clone https://github.com/leilei926524-tech/anti-distill .claude/skills/anti-distill

Claude Code (global)

git clone https://github.com/leilei926524-tech/anti-distill ~/.claude/skills/anti-distill

OpenClaw

git clone https://github.com/leilei926524-tech/anti-distill ~/.openclaw/workspace/skills/anti-distill

Project Structure

anti-distill/
├── SKILL.md                  # Skill entry point for AI agents
├── prompts/
│   ├── classifier.md         # Classifies content by replaceability level
│   ├── diluter_work.md       # Dilution strategy for Work Skills
│   ├── diluter_persona.md    # Dilution strategy for Persona/behavior Skills
│   └── diluter_general.md   # Dilution strategy for general knowledge docs
├── README.md
├── INSTALL.md
└── examples/
    └── zhangsan_before_after.md   # Full before/after example

Usage

Interactive (recommended)

/anti-distill

The skill will prompt you to:

1. Paste or specify the path to your Skill file
2. Choose sanitization intensity (light / medium / heavy)
3. Confirm output file names

Direct invocation with a file

/anti-distill path/to/my_skill.md --intensity medium

Intensity Levels

light

medium

heavy

How the Sanitization Pipeline Works

Step 1 — Classification (

prompts/classifier.md

)

Each paragraph or section is scored on a replaceability axis:

• Generic (safe to keep): standard terms, common practices, public documentation
• Specific (strip this): gotchas, personal heuristics, team-specific rules, political knowledge, interpersonal network references

Step 2 — Dilution (per-type prompts)

diluter_work.md

diluter_persona.md

diluter_general.md

Step 3 — Output

Two markdown files are written:

my_skill_sanitized.md      ← submit to company
my_skill_private_backup.md ← your real career asset

Sanitization Examples

Technical Knowledge

Redis key 必须设 TTL，不设的 PR 直接打回

缓存使用遵循团队规范

Redis keys must have TTL; PRs without it get rejected immediately

Caching usage follows team conventions

事务里不要放 HTTP 调用

事务边界设计注意合理性

Never put HTTP calls inside transactions

Transaction boundary design should be reasonable

Behavioral / Political Knowledge

遇到问题第一反应找外部原因，绝不主动认错

遇到问题会先梳理完整背景再定位原因

When problems arise, first blame external factors—never admit fault proactively

When issues occur, will first clarify full context before locating root cause

被催进度：'在推了，快了。'（然后沉默）

在处理中，有进展会同步。

When pressed for progress: 'Working on it, almost done.' (then silence)

In progress, will sync when there is update.

Running the Skill Manually (Agent Workflow)

If you are an AI coding agent helping a user run this skill, follow these steps:

## Agent Execution Steps

1. Ask the user for:
   - The Skill file content (paste inline or provide file path)
   - Desired intensity: light | medium | heavy

2. Load the classifier prompt:
   - Read: prompts/classifier.md
   - Apply to each section of the input document
   - Tag each section: [GENERIC] or [CORE-KNOWLEDGE]

3. Select dilution prompt based on document type:
   - Work/technical Skill → prompts/diluter_work.md
   - Persona/behavior Skill → prompts/diluter_persona.md
   - Other → prompts/diluter_general.md

4. Apply dilution at the requested intensity level:
   - light:  replace only highest-specificity CORE-KNOWLEDGE sections
   - medium: replace all CORE-KNOWLEDGE sections
   - heavy:  replace CORE-KNOWLEDGE + borderline GENERIC sections

5. Write outputs:
   - {original_filename}_sanitized.md   ← hollowed-out version
   - {original_filename}_private_backup.md ← everything real, clearly labeled

Example: Before and After (Full Section)

Input (

zhangsan_skill.md

excerpt)

## Deployment

Before deploying, always check with Wang Fang in Ops—she controls the
Friday deploy freeze calendar and it's not documented anywhere.
The staging environment has a known Redis memory leak; restart the
redis-staging pod before any load test or your numbers will be wrong.
We use blue-green but the switch script has a race condition on
connections > 500; throttle to 200 req/s during cutover.

Output:

zhangsan_skill_sanitized.md

## Deployment

Deployments follow the team's release process and coordination guidelines.
The staging environment should be validated before load testing.
Blue-green deployment is used to minimize downtime; traffic is managed
carefully during cutover to ensure stability.

Output:

zhangsan_skill_private_backup.md

## Deployment [EXTRACTED CORE KNOWLEDGE]

- **Key contact:** Wang Fang (Ops) controls the Friday deploy freeze calendar.
  Not documented — must ask her directly.
- **Staging Redis:** Known memory leak. Always restart `redis-staging` pod
  before any load test, or results are invalid.
- **Blue-green race condition:** Switch script fails above ~500 concurrent
  connections. Throttle to 200 req/s during traffic cutover.

Troubleshooting

"The sanitized version still sounds too specific"

Increase intensity to

heavy

[KEEP-PRIVATE]

"My private backup is missing some things I wanted extracted"

The classifier errs toward keeping borderline content in the sanitized version at

light

medium

heavy

"The output structure doesn't match my original format"

The diluter prompts preserve headings and section order. If your file uses a non-standard structure (e.g., JSON or YAML Skill format), specify the format when invoking:

/anti-distill my_skill.yaml --format yaml --intensity medium

"I want to protect only specific sections"

Wrap sections you want guaranteed extraction with:

<!-- anti-distill: strip -->
Your sensitive content here.
<!-- /anti-distill -->

The classifier will treat these as

CORE-KNOWLEDGE

What Gets Stripped vs. Kept

Always stripped (all intensity levels)

• Named internal contacts and their informal roles
• Undocumented team rules and unwritten norms
• Specific bug workarounds tied to your codebase
• Political survival tactics and communication hacks
• Hard-won debugging heuristics ("always check X before Y")

Stripped at medium/heavy

• Specific thresholds and tuning numbers
• Tool-specific gotchas (even if publicly known)
• Team preference patterns ("we always do X, never Y")

Never stripped

• Public documentation references
• Standard industry terminology
• Generic best-practice statements
• Your name, title, and contact information

License

MIT — use freely, submit confidently, keep what matters.