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.