util-research-library
Systematic library evaluation with emphasis on readability, actionable insights, and informed decision-making. Use when asked "should we use X", "is there a better library", during security audits, or making migration decisions. Produces concise, scannable reports that drive adoption decisions - not walls of text.
npx skills add majiayu000/claude-skill-registry --skill util-research-library --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
This skill performs systematic library evaluation to support library adoption, migration, and upgrade decisions. It conducts research across six dimensions (official docs, recent developments, alternatives, security, community health, and current usage) and produces a scannable decision brief with a clear recommendation and action items.
How it works
- Phase 1: Gather evidence across 6 dimensions using steps such as reading pyproject.toml for Context7 IDs, resolving library IDs, obtaining official docs, performing WebSearch for updates, alternatives, security, and community signals, and analyzing current codebase usage.
- Phase 2: Synthesize evidence into a clear decision by scoring each dimension (1-10), identifying the primary decision driver, determining a Recommendation (ADOPT/MIGRATE/UPGRADE/STAY/AVOID), assessing confidence, and listing action items.
- Phase 3: Write a skimmable decision brief including a TL;DR, a Decision Matrix with scores and evidence, the final Recommendation and rationale, concrete action items, supporting evidence, and references.
The workflow emphasizes readability, decision-focused content, confidence over completeness, and time-to-value targets (total ~40 minutes).
When to use it
Use this skill for adoption decisions (Should we use library X?), migration decisions (Should we switch from X to Y?), upgrade decisions (Should we upgrade to version Z?), security audits of dependencies, performance consideration, or general technology evaluation when a concise, evidence-based decision brief is required.
What it can touch
- Tools and inputs referenced by the workflow include: reading pyproject.toml, resolving library IDs, WebSearch results, and current codebase usage data to assess integration and migration effort.
Caveats
- The skill aims to deliver decision-focused outputs with assigned scores and actionable items; it should not be treated as a guarantee of outcomes. It requires up-to-date sources for accurate scoring and may depend on the quality of available evidence across the six dimensions.
# Library Research & Evaluation ## Purpose Systematic library evaluation framework that produces readable, actionable decision briefs for library adoption, migration, and upgrade decisions using a 6-dimension research methodology. ## Quick Start **Create readable, actionable research that drives informed decisions.** This skill is about **research methodology** - knowing what to look for, where to find it, how to synthesize it, and how to present it. The goal is NOT to dump data, but to **accelerate time to adoption** through clear, confident recommendations. **Most common use case:** ``` User: "Should we use Pydantic v2?" → Research 6 dimensions (docs, updates, alternatives, security, community, codebase) → Score each with evidence → Deliver decision brief with clear ADOPT/MIGRATE/STAY/AVOID recommendation Result: 40 minutes (vs 2-4 hours ad-hoc research) ``` ## Table of Contents 1. [When to Use This Skill](#when-to-use-this-skill) 2. [What This Skill Does](#what-this-skill-does) 3. [Instructions](#instructions) 4. [Research Framework (6 Dimensions)](#research-framework-6-dimensions) 5. [Output Format: Decision Brief](#output-format-decision-brief) 6. [Usage Examples](#usage-examp
- Purpose
- Quick Start
- Table of Contents
- When to Use This Skill
- What This Skill Does
- Instructions
- Phase 1: Gather (15 minutes)
- Phase 2: Synthesize (10 minutes)
- Phase 3: Write (15 minutes)
- Core Principles
- 1. Readability First
- 2. Decision-Focused
- 3. Confidence Over Completeness
- 4. Time to Value
Verify all 6 dimensions covered Each dimension scored 1-10 with evidence Overall score calculated Confidence level assigned (High/Medium/Low) Clear ADOPT/MIGRATE/UPGRADE/STAY/AVOID decision Rationale documented Action items specific and assignable Can user make decision from TL;DR alone? (30 seconds) Is recommendation clear and confident? Are scores backed by specific evidence?
What does the util-research-library skill do?
Systematic library evaluation with emphasis on readability, actionable insights, and informed decision-making. Use when asked "should we use X", "is there a better library", during security audits, or making migration decisions. Produces concise, scannable reports that drive adoption decisions - not walls of text.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill util-research-library --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.