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concierge-testing

Conduct manual concierge tests with real users to measure commitment for gap hypotheses.

Updated Feb 17, 2026
Search Tags:#validation#provenance#hypothesis#concierge#commitment#canvases
One-click install
npx skills add https://github.com/0xHoneyJar/construct-observer --skill concierge-testing
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: concierge-testing
Source: https://github.com/0xHoneyJar/construct-observer/tree/main/skills/concierge-testing
Command: npx skills add https://github.com/0xHoneyJar/construct-observer --skill concierge-testing

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill helps product teams validate gap hypotheses by manually simulating features for a single user and measuring commitment rather than general sentiment, ensuring decisions are grounded in observed behavior.

Core Features & Use Cases

  • Manual validation for hypotheses that have reached Pattern level, using real-user canvases to gather concrete commitment signals.
  • Provenance logging, canvas-driven user selection, and structured handoffs to product teams for decision-making.
  • End-to-end workflow from hypothesis selection to canvas update with a recorded commitment outcome.

Quick Start

Run /concierge-test {hypothesis-id} to begin validating the strongest canvas hypothesis.

Frequently Asked Questions about concierge-testing

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

FAQPage Schema
How do I validate a gap hypothesis with real users before building a feature?▼

You can validate a gap hypothesis by running manual concierge-style tests that measure real user commitment rather than general sentiment, ensuring product decisions are grounded in observed behavior.

When should I use concierge testing for product hypothesis validation?▼

Use concierge testing when a gap hypothesis has reached Pattern level and requires measured commitment data from real users before you decide to file a GitHub issue or discard the idea.

How do I manually simulate a feature to test user commitment?▼

You manually simulate a feature by selecting your strongest-evidence user from canvases, constructing a concrete manual simulation for them, and recording the provenance of their commitment outcome.

Can I use canvases to select users for manual feature validation?▼

Yes, you can use real-user canvases to select the strongest-evidence user for manual validation, gathering concrete commitment signals and updating the canvases with the final commitment results.

What is the difference between measuring user commitment and gathering general sentiment?▼

Measuring user commitment grounds decisions in observed behavior through manual simulations, whereas general sentiment only captures opinions, making commitment a stronger signal for validating hypotheses.

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