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libsync-research-methodology

Define research problems, validate predictions, and control changes in libsync development.

4|Updated Aug 15, 2025
Search Tags:#software development#bug fixing#development methodology#change control#experiment validation
One-click install
npx skills add https://github.com/arthursoares/libsync --skill libsync-research-methodology
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: libsync-research-methodology
Source: https://github.com/arthursoares/libsync/tree/main/.claude/skills/libsync-research-methodology
Command: npx skills add https://github.com/arthursoares/libsync --skill libsync-research-methodology

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides a structured approach for libsync developers to turn hunches, bug theories, and improvement ideas into accepted changes.

Core Features & Use Cases

  • Evidence Bar: Ensures conclusions are well-supported and can survive adversarial refutation.
  • Predictions: Requires documented predictions for experimental results before running experiments.
  • Idea Lifecycle: Outlines the stages from hunch to validated design, executable plan, merged code, released feature, guarded rollout, and retirement.
  • Experiment Hygiene: Promotes safe experimentation without impacting live services, using in-memory DBs and mocked clients.
  • Result to Change Control: Details the steps to turn a result into a shipped change, including PR classification, gates, review, and CHANGELOG entry.

Quick Start

Define your research problem, formulate a hypothesis with predictions, run your experiment, and if successful, submit a pull request with your findings following the methodology outlined.

Frequently Asked Questions about libsync-research-methodology

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

FAQPage Schema
How do I validate a bug fix hypothesis in software development?▼

Validating a bug fix hypothesis requires formulating predictions for experimental results before running tests. This methodology enforces an evidence bar, ensuring your conclusions survive adversarial refutation and are well-supported by documented experimental data.

How do I safely run development experiments without impacting live services?▼

Safe development experimentation requires using in-memory databases and mocked clients to isolate tests. This experiment hygiene approach prevents unvalidated changes from impacting live services while you gather evidence to support or refute your hypothesis.

What is the best way to turn a bug theory into a shipped code change?▼

Turning a bug theory into a shipped change involves a structured lifecycle from hunch to validated design. You must follow controlled change implementation steps, including PR classification, passing review gates, and updating the CHANGELOG entry with your findings.

Does this development methodology require documented predictions before testing?▼

Yes, this development methodology requires documented predictions for experimental results before execution. This ensures evidence-based conclusions and enforces an evidence bar that validates your software development changes through controlled, structured experimentation.

When do I need controlled change implementation for software development?▼

Controlled change implementation is needed when applying validated experimental results to a codebase. It governs the transition from a successful experiment to a shipped feature, requiring PR classification, review gates, and guarded rollout to ensure software stability.

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