research-then-implement

Runs structured research, writes a Brief, then implements code against it.

1.6k|73|Updated Mar 13, 2025
One-click install
npx skills add https://github.com/UniClipboard/UniClipboard --skill research-then-implement
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: research-then-implement
Source: https://github.com/UniClipboard/UniClipboard/tree/main/.agents/skills/research-then-implement
Command: npx skills add https://github.com/UniClipboard/UniClipboard --skill research-then-implement

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It prevents two recurring agent workflow failures: re-researching topics already studied in prior sessions, and implementation silently drifting away from research conclusions because findings were never captured in a durable artifact.

Core Features & Use Cases

  • Existing-research check: Scans .planning/research/ and memory files before starting, offering to reuse, redo, or supplement prior Briefs.
  • Structured multi-source investigation: Combines official docs via context7, web research via bb-browser, and reference code study, recording raw findings in sources.md.
  • Brief-driven implementation: Produces a BRIEF.md with recommendation, rejected alternatives, constraints, and a checklist, then implements strictly against it, updating the Brief if reality diverges.
  • Use Case: When porting an iOS UISheetPresentationController bottom sheet to Expo, the skill researches @expo/ui capabilities, writes a Transfer Spec mapping iOS APIs to Expo equivalents, then implements against that spec.

Quick Start

Ask the agent to research how to implement a native-feel bottom sheet in Expo and then implement it using the research-then-implement workflow.

Frequently Asked Questions about research-then-implement

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

FAQPage Schema
How do I avoid re-researching a topic an AI agent already studied?

Store research findings in a durable Brief file under .planning/research/<topic>/BRIEF.md. Before any new investigation, check that directory and memory files for matching topics, then reuse, redo, or supplement the existing research instead of starting over.

How to research before implementing a feature with an AI coding agent?

Define the research question, investigate official docs via context7, community practices via bb-browser, and reference implementations in code. Record raw findings in sources.md, then distill them into a Brief with a recommendation, rejected alternatives, and constraints before writing any code.

What should a research Brief contain before implementation?

A Brief should state the research question, a specific recommendation, key findings with sources, at least one rejected alternative with reasons, concrete constraints, and an actionable implementation checklist. Vague Briefs that do not constrain implementation fail the quality gate.

When should I not use a research-then-implement workflow?

Skip it when the implementation approach is already clear, when fixing bugs with a known root cause, or when doing pure research with no implementation intent. Those cases are better served by direct implementation, debugging skills, or a dedicated deep-research skill.

What happens if the Brief's recommendation fails during implementation?

Stop instead of silently working around the problem. Update the Brief with the new finding, inform the user that practice diverged from the research, and proceed with the corrected approach so the artifact stays accurate.