principle-prove-it-works

Verifies completed work by directly inspecting real artifacts instead of proxies or self-reports.

Updated Mar 27, 2026
One-click install
npx skills add https://github.com/gmackie/agent-skills --skill principle-prove-it-works-gmackie
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: principle-prove-it-works
Source: https://github.com/gmackie/agent-skills/tree/main/skills/principle-prove-it-works
Command: npx skills add https://github.com/gmackie/agent-skills --skill principle-prove-it-works-gmackie

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Work declared done without direct verification has unknown correctness. Indirect signals like file timestamps, cached output, or an agent's own summary can be wrong, and acting on a wrong inference costs more than checking the source. ## Core Features & Use Cases - Direct artifact verification: Run the actual feature, read the real value, and inspect the git diff rather than trusting proxies or self-reports. - End-to-end checks: Exercise the full chain from input to output, including complete communication paths for integrations. - Scripted proof: Write a deterministic re-runnable script that compares expected and actual output, keeping its result as a reviewable artifact. - Use Case: After delegating a code change to an agent, inspect the actual diff and run the feature path yourself instead of accepting the agent's summary that the task is complete. ## Quick Start Ask the agent to apply the prove-it-works principle to verify the task just completed by running the feature and inspecting the real output.

Frequently Asked Questions about principle-prove-it-works

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

FAQPage Schema
How do I verify that a coding task is actually complete?▼

Verify task completion by checking the real artifact directly: build the code, run the actual feature path, and confirm data flows from input to output. Building successfully is necessary but not sufficient proof that the feature works.

How to verify work delegated to an AI agent?▼

Verify delegated work by inspecting the actual output artifact, such as the git diff, file contents, or runtime behavior, rather than the agent's summary. Agents report what they intended to do, not always what actually happened.

Why is indirect verification unreliable for software tasks?▼

Indirect verification through file mtimes, output freshness, cached screenshots, or self-reports can reflect stale or derived state rather than reality. When verification fails, suspect the observation method before suspecting the system itself.

What is the strongest way to prove a change works?▼

The strongest proof is a deterministic script that re-runs the same comparison, such as diffing old and new compiled output. Keep the script's output visible as an artifact a reviewer can re-run instead of trusting a one-time visual check.

When should verification artifacts be committed to the repository?▼

Commit verification artifacts only for large or complex work where the trail must be auditable later, such as a big port or migration. Most routine work only needs the artifact visible to the human reviewer, not committed.