context-discipline
by Roy Yuen
Enforce explicit context discipline, artifact-gated transitions, and verification evidence for AI agent workflows.
- Prevent agents from taking destructive actions without explicit human approval.
- Ensure every success claim is backed by passing tests or tool output logs.
- Standardize handoffs between multiple agents to prevent context loss.
$6
One-time purchase · Own forever
Included in download
- Prevent agents from taking destructive actions without explicit human approval.
- Ensure every success claim is backed by passing tests or tool output logs.
- Includes example output and usage patterns
See it in action
[STATUS]: blocked [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.
Enforce explicit context discipline, artifact-gated transitions, and verification evidence for AI agent workflows.
$6
One-time purchase · Own forever
⚡ Also available via Agensi MCP — your AI agent can load this skill on demand via MCP. Learn more →
Included in download
- Prevent agents from taking destructive actions without explicit human approval.
- Ensure every success claim is backed by passing tests or tool output logs.
- Includes example output and usage patterns
- Instant install
- One-time purchase
See it in action
[STATUS]: blocked [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.
Screenshots
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.
📖 Learn more: Best DevOps & Deployment Skills for Claude Code →
Use Cases
- Prevent agents from taking destructive actions without explicit human approval.
- Ensure every success claim is backed by passing tests or tool output logs.
- Standardize handoffs between multiple agents to prevent context loss.
- Force agents to list unknowns and assumptions before generating a plan.
How to Install
unzip context-discipline.zip -d ~/.claude/skills/Reviews
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