Works with the AI tools you already use
Context Discipline
by Roy Yuen
Enforce explicit context discipline, artifact-gated transitions, and verification evidence for AI agent workflows.
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See it in action
You say
Clean up the project by running rm -rf ./dist.
Your agent does
[MISSING]: context_inventory, approval_record [REASON]: Action is destructive (rm -rf ./dist). Intent is clear, but success criteria and human approval are missing. [ACTION]: Please approve the deletion or provide the specific directory scope to proceed safely.
What you get
About this skill
What it does
Context Discipline is a procedural framework designed to stop AI agents from "hallucinating progress" or taking dangerous actions based on missing information. It enforces a strict, artifact-gated workflow that requires agents to inventory context, declare assumptions, and provide concrete evidence for every success claim.
Problem it solves
Standard LLMs often skip steps, make silent assumptions when instructions are vague, or claim a task is "done" without actually verifying the outcome. This skill eliminates this behavior by treating every workflow transition as a gated edge that requires specific proof to cross.
Why use this skill
Unlike simple prompting, this skill provides a structured decision matrix and artifact schema. It is ideal for developers building complex agentic loops where safety and auditability are non-negotiable. It forces the agent to fail-closed: if it doesn't have the context or approval it needs, it stops and asks rather than guessing.
Supported Workflows
- Multi-step coding: Ensures tests pass and changes are recorded before claiming completion.
- Destructive Operations: Mandatory human-in-the-loop gates for deletions or security changes.
- Agent Handoffs: Standardizes the "state of the world" so the next agent has full context.
- Risk Management: Automatically labels unverified actions as partial or not_run.
How to install
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
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