research

Evaluate candidate solution approaches and write ranked recommendations to docs/research.

5|Updated Apr 29, 2026
One-click install
npx skills add https://github.com/friedbotstudio/baseline --skill research-friedbotstudio
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: research
Source: https://github.com/friedbotstudio/baseline/tree/main/.claude/skills/research
Command: npx skills add https://github.com/friedbotstudio/baseline --skill research-friedbotstudio

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you choose the best implementation path when a task has multiple plausible solutions. It turns scattered requirements, existing code patterns, and library documentation into a clear set of options with honest tradeoffs.

Core Features & Use Cases

  • Candidate discovery: Surfaces a small number of viable approaches instead of overwhelming you with noise.
  • Doc-grounded verification: Checks current library APIs through context7 or official documentation so recommendations are based on verified behavior, not memory.
  • Decision support: Ranks options by fit, testability, reversibility, and alignment with the project’s constraints.
  • Use case: A spec author can use this Skill before implementation to compare approaches for a feature, migration, or integration and write the research memo that informs the next step.

Quick Start

Use the research skill to evaluate the current task, compare 2 to 4 viable approaches, and write the memo to docs/research/<slug>.md.

Frequently Asked Questions about research

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

FAQPage Schema
How do I compare library options and choose the best implementation path for a feature?

Comparing solution options involves evaluating 2 to 4 viable approaches against project constraints, verifying current library APIs through official docs, and ranking them by fit, testability, and reversibility. This produces a grounded recommendation memo for implementation planning.

What is tradeoff analysis for spec preparation and how does it work?

Tradeoff analysis for spec preparation evaluates candidate approaches by fit, testability, reversibility, and project constraint alignment. It surfaces viable paths and verifies current library APIs through context7 to ensure recommendations reflect actual behavior.

How do I write a research memo for solution exploration before coding?

Write a research memo for solution exploration by comparing 2 to 4 candidate approaches, verifying current library APIs through context7, and ranking options by fit and reversibility. Save the ranked recommendation to docs/research/<slug>.md to inform implementation.

Does solution exploration require verifying current library APIs through context7 or official docs?

Yes, solution exploration requires current API verification through context7 or official documentation. This doc-grounded check ensures your tradeoff analysis and ranked recommendations rely on verified, current library behavior rather than outdated memory.

What's the best way to evaluate candidate approaches for a library selection or migration?

The best way to evaluate candidate approaches for library selection is to compare 2 to 4 viable paths, verify current APIs through official docs, and rank them explicitly by fit, testability, and reversibility. This yields a constraint-aligned recommendation memo.

When should I not use tradeoff analysis for implementation planning?

You should not use tradeoff analysis for implementation planning when only one viable solution path exists or the task lacks multiple plausible approaches. The process specifically compares discrete options and writes a ranked recommendation when multiple paths are present.