plan-track

Creates and maintains plan.md and checklist.md files to track complex task execution with evidence.

2|Updated Jun 8, 2026
One-click install
npx skills add https://github.com/HACK-WU/skills --skill plan-track-hack-wu
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: plan-track
Source: https://github.com/HACK-WU/skills/tree/main/skills/plan-track
Command: npx skills add https://github.com/HACK-WU/skills --skill plan-track-hack-wu

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve? Complex multi-step tasks lose progress when conversation context is lost, and AI self-assessment of "done" is unreliable. This Skill moves execution state into persistent files under .plans/, turning completion into checkable, evidence-backed criteria instead of guesswork. ## Core Features & Use Cases - Plan建档 (Plan Creation): Generates plan.md (goals, completion criteria, guardrails, work items with status) and checklist.md (self-review checklist) in .plans/{date}-{slug}/ before work begins. - Evidence-Bound Progress Tracking: Work items can only be marked complete with verifiable evidence (command output, file:line references, test results); unverified changes stay in an explicit "changed but unverified" state. - Self-Review Gate: After completion, every checklist item including guardrails is verified with real runs; P0 failures block delivery with up to 3 fix rounds. - Use Case: When asked to fix a timeout bug across multiple files, the Skill creates a plan with guardrails (e.g., "no retry on non-idempotent endpoints"), tracks each work item with evidence, supports cross-session resumption, and runs a final self-review before handoff to acceptance verification. ## Quick Start Ask the AI to create a plan and track progress for your multi-file task, saying something like "create a plan document and track progress for this refactoring task".

Frequently Asked Questions about plan-track

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

FAQPage Schema
How do I track progress on a complex multi-step coding task with an AI agent?

Ask the agent to create a plan document before starting. It generates plan.md with goals, completion criteria, guardrails, and work items in a .plans/ directory, then updates each item's status with evidence as work proceeds.

How does plan-track prevent AI from marking unfinished work as done?

It enforces evidence binding: a work item can only be marked complete with verifiable proof such as command output, file and line references, or test results. Changed-but-unverified work gets a distinct state and cannot skip straight to complete.

Can I resume a task in a new session after the conversation context is lost?

Yes. All state lives in .plans/ files, not conversation memory. On resumption, the agent reads plan.md, finds the in-progress or unverified work item, reviews guardrails and recent log entries, then continues from there.

When should I not create a plan document for a task?

Skip plan creation for single-file small changes, typo fixes, one-step questions, and pure consultations. The Skill explicitly avoids建档 for trivial tasks to prevent formality overhead, and users can opt out by saying no plan is needed.

What is the difference between self-review and acceptance verification?

Self-review is an internal gate where the agent checks each checklist item with real runs after task completion. Acceptance verification is a separate delivery step performed by the acceptance-verify skill or the user, and self-review never substitutes for it.