Works with the AI Agents you already use

    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIMuseMuseDotsDots+21 more

    Context Discipline

    2

    Enforce explicit context discipline, artifact-gated transitions, and verification evidence for AI agent workflows.

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    context-discipline

    context-discipline

    Example session with this skill installed

    Clean up the project by running rm -rf ./dist.

    • Read your context and instructions
    • Compiled the context-discipline

    [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.

    Connects securely to your tools. The creator never sees your data.

    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.

    • Increases output token overhead.
    • Requires agents with tool-calling/file-access to verify state.
    • May feel restrictive for low-risk, creative tasks.

    How to use

    1. 1

      Connect your agent to the Agensi MCP

      Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup

    2. 2

      Sign in to Agensi

      Your agent shows a sign-in link. Sign in and click Allow.

    3. 3

      Ask for what you need

      Your agent loads this skill and uses it on your task. It always asks before any payment.

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    Security scan passed 6 months ago

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    Listed6 months ago

    What's inside

    Frequently Asked Questions

    It acts as a procedural gateway that prevents agents from proceeding unless they provide verifiable evidence of a task's success, effectively stopping 'hallucinated progress' where an agent claims completion without proof.

    This skill is framework-agnostic and can be integrated into most autonomous agent platforms or custom LLM loops via structured JSON schemas and procedural Markdown templates.

    The package includes the core Context Discipline decision matrix, artifact schema templates for state management, and implementation guides for defining gated transitions in your existing pipelines.

    Yes, it is specifically designed for high-stakes environments like DevOps or security, providing clear fail-closed mechanisms and human-in-the-loop gates for any destructive or unverified operations.

    Setup involves importing the provided schema into your agent's system prompt or orchestration layer and defining which workflow steps require mandatory artifact verification before the agent can progress.

    New in Agents & Orchestration