execution-discipline

Enforce planning, tracking, verification, and drift handling in agent workflows.

Updated Aug 23, 2026
One-click install
npx skills add https://github.com/j-mckerracher/agent-research --skill execution-discipline-j-mckerracher
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: execution-discipline
Source: https://github.com/j-mckerracher/agent-research/tree/main/dated-agents/4-6-2026/.claude/skills/execution-discipline
Command: npx skills add https://github.com/j-mckerracher/agent-research --skill execution-discipline-j-mckerracher

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Mandatory execution discipline protocol for AI agent workflows. Use this skill whenever an agent needs to plan work, track progress, verify completion, or handle execution drift. Provides the standard operating procedures for: (1) Plan Mode Default — creating explicit plans/checklists before non-trivial tasks (2+ steps or architectural decisions), (2) Replan on Drift — stopping and reassessing when execution diverges from plan, (3) Verification Before Done — never marking work complete without evidence (tests, logs, diffs, rubric checks), (4) Track Progress — marking checklist items complete as work progresses, (5) Demand Elegance — preferring the simplest robust design and avoiding over-engineering. Keywords: plan mode, replan, drift, verification, evidence, checklist, track progress, elegance, non-trivial task, planning discipline.

Core Features & Use Cases

  • Explicit planning and checklists before complex work to ensure clarity and accountability.
  • Drift detection and re-planning mechanisms to recover from deviations.
  • Verification-before-done with evidence requirements (tests, logs, diffs, rubrics) to prevent premature completion.
  • Progress tracking with structured checkpoints and decision logs.
  • Encourages elegant, minimal-impact designs that balance rigor with practicality.

Quick Start

Always create an explicit plan or checklist before starting any non-trivial task.

Frequently Asked Questions about execution-discipline

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

FAQPage Schema
How do I enforce planning and verification in multi-stage AI agent workflows?

Apply a formal execution discipline protocol to enforce planning, progress tracking, drift handling, and final verification across multi-stage agent pipelines. This ensures agents create explicit checklists, verify completion with evidence, and prevent untracked deviations.

What is execution drift in AI agent pipelines and how do I handle it?

Execution drift occurs when agent actions diverge from the established plan. Handle drift using a replan mechanism that stops task execution, reassesses the situation, and generates an updated plan before resuming non-trivial work.

How do I stop AI agents from marking tasks complete without verification?

Implement a verification-before-done requirement that prevents agents from marking work complete without explicit evidence. Require tests, logs, diffs, or rubric checks as proof of completion before closing checklist items.

When should I require an explicit checklist before running AI agent tasks?

Require an explicit checklist before running non-trivial AI agent tasks involving two or more steps or architectural decisions. This plan mode default ensures clarity, accountability, and structured progress tracking for complex pipelines.

Can I use this execution discipline protocol for simple single-step agent tasks?

This execution discipline protocol targets non-trivial tasks in multi-stage agent pipelines. Simple single-step tasks likely do not require the full plan mode, drift handling, and verification checkpoint overhead.