skill-hypothesis-discovery

Name: skill-hypothesis-discovery
Availability: InStock
Author: ginmp

Prioritize testable improvement hypotheses from skill evidence into a backlog.

Updated Apr 27, 2026

Search Tags:#planning #validation #discovery #evidence #backlog #hypothesis #risk

Ready to run

npx skills add https://github.com/ginmp8/rhapsodia --skill skill-hypothesis-discovery

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: skill-hypothesis-discovery
Source: https://github.com/ginmp8/rhapsodia/tree/main/skills/skill-hypothesis-discovery
Command: npx skills add https://github.com/ginmp8/rhapsodia --skill skill-hypothesis-discovery

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Discover and prioritize evidence-backed improvement hypotheses for an existing skill package, enabling safe, no-mutation planning before any edits.

Core Features & Use Cases

Generates a prioritized backlog of testable hypotheses from available evidence (benchmarks, harness results, activation/non-activation signals, and reviews).
Guides skill-improver or related orchestration workflows by selecting high-impact, low-risk hypotheses and clear validation plans.
Supports governance by ensuring no patches are applied and that evidence dictates next steps.

Quick Start

Provide a backlog of hypotheses from the target skill's evidence without editing files.

Frequently Asked Questions about skill-hypothesis-discovery

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

FAQPage Schema

How do I prioritize testable improvement hypotheses for an existing skill package?▼

To prioritize testable improvement hypotheses, analyze evidence from benchmarks and validation gaps to generate a backlog. This process selects high-impact, low-risk items by detailing observed signals, mechanisms, and expected effects without applying any file edits.

What is no-mutation planning in product backlog validation?▼

No-mutation planning is the process of dictating next steps strictly from evidence without applying patches or edits. It ensures safe governance by generating a backlog of testable hypotheses detailing validation methods and rollback notes before any code changes occur.

How do I create a backlog of evidence-backed improvement ideas from harness findings?▼

You can create a backlog from harness findings by extracting observed signals and validation gaps to formulate testable hypotheses. This structures your evidence into prioritized items with expected effects, validation methods, and risk notes to guide safe improvements.

Can I use hypothesis discovery to plan skill improvements without editing files?▼

Yes, you can use hypothesis discovery to plan improvements without editing files because it enforces a no-mutation governance model. It outputs a structured backlog of testable hypotheses with risk and rollback notes, ensuring evidence dictates planning before any mutation.

What is the best way to document validation methods and rollback risks for backlog items?▼

The best way to document validation methods and rollback risks is by structuring them into a hypothesis backlog. This approach captures observed signals, expected effects, and specific validation methods, ensuring every evidence-backed item includes comprehensive risk notes.