convention-extraction

Extract tacit team conventions into versioned HARNESS.md and CLAUDE.md artefacts.

44|6|Updated Mar 30, 2026
One-click install
npx skills add https://github.com/Habitat-Thinking/ai-literacy-superpowers --skill convention-extraction
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: convention-extraction
Source: https://github.com/Habitat-Thinking/ai-literacy-superpowers/tree/main/ai-literacy-superpowers/skills/convention-extraction
Command: npx skills add https://github.com/Habitat-Thinking/ai-literacy-superpowers --skill convention-extraction

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Teams and AI produce inconsistent code and reviews because important conventions live implicitly in people's heads; this skill captures that tacit knowledge and converts it into explicit, versioned artefacts so AI and new team members follow the same expectations.

Core Features & Use Cases

  • Structured interview protocol: A five-question extraction flow that surfaces architectural non-negotiables, frequent AI corrections, security instincts, review rejections, and refactoring philosophy.
  • Artefact mapping: Converts interview answers into concrete outputs such as HARNESS.md constraints, CLAUDE.md conventions, and reviewer agent notes with clear role, context, priority, and output format.
  • Practical guidance and heuristics: Includes sizing recommendations, anti-patterns, and re-extraction cadence to keep conventions current.
  • Use Case: Run during new project setup, onboarding, after a production incident, or when AI output quality varies by prompter to produce enforceable rules and reduce review churn.

Quick Start

Run an extraction interview with senior engineers and map answers to HARNESS.md constraints and CLAUDE.md conventions using the guided five-question protocol.

Frequently Asked Questions about convention-extraction

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

FAQPage Schema
How do I extract tacit code conventions into enforceable rules for AI agents?

Capture implicit team conventions by running a structured five-question interview with senior engineers, then mapping answers into versioned artefacts like HARNESS.md constraints and CLAUDE.md conventions for CI or AI agent enforcement.

What is the best way to document architectural non-negotiables for onboarding new developers?

Document architectural non-negotiables by applying an extraction interview protocol during onboarding to surface rules, then converting them into explicit reviewer agent notes with defined priorities, roles, and output formats.

How do I set up CLAUDE.md conventions after a production incident?

Generate CLAUDE.md conventions post-incident by interviewing the team to capture security instincts and refactoring philosophy, then mapping extracted constraints into versioned files for agent or CI enforcement.

Can I use a structured interview to capture code review rejections and turn them into CI constraints?

Yes, you can use the extraction interview protocol to capture frequent review rejections and map them directly into HARNESS.md constraints and reviewer agent notes, ensuring automated checks enforce historically tacit expectations.

When do I need to re-extract team conventions to keep AI output consistent?

Re-extract team conventions after team composition changes, new project setups, or when AI output quality varies by prompter, using the provided re-extraction cadence heuristics to keep constraints current and reduce review churn.

What limitations exist when encoding tacit knowledge into versioned artefacts?

Limitations include relying on accurate interview responses from senior engineers and avoiding anti-patterns during sizing; constraints may become stale without adhering to recommended re-extraction cadences after team changes.