research

Caches codebase exploration into persistent research documents before implementation begins.

1|Updated Mar 26, 2026
One-click install
npx skills add https://github.com/arndvs/ctrlshft --skill research-arndvs
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: research
Source: https://github.com/arndvs/ctrlshft/tree/main/skills/research
Command: npx skills add https://github.com/arndvs/ctrlshft --skill research-arndvs

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Large features often require extensive codebase exploration that consumes most of an AI agent's context window, leaving no room to actually implement. This Skill caches that exploration into a persistent research document so implementation conversations start with full context instead of re-exploring from scratch. ## Core Features & Use Cases - Parallel Sub-Agent Exploration: Decomposes a research topic into 3-6 independent areas and spawns dedicated sub-agents to investigate each one concurrently. - Structured Research Documents: Synthesizes findings into working/research/<topic>.md with sections for summary, architecture, constraints, dependencies, open questions, and recommendations. - Lifecycle Management: Reuses research files under 7 days old, re-validates stale ones, and promotes lasting findings to docs/research/ before cleanup. - Use Case: Before building a new authentication system, run research to map existing auth code paths, external dependencies, and API constraints, then hand the document to the implementation conversation. ## Quick Start Ask the agent to research the authentication flow before building the new login feature.

Frequently Asked Questions about research

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

FAQPage Schema
How do I research a codebase before building a feature with an AI agent?▼

Invoke the research skill with your topic, and it decomposes the investigation into 3-6 areas explored by parallel sub-agents. Findings are synthesized into a structured markdown file at working/research/<topic>.md covering architecture, constraints, and recommendations.

When should I run research before implementing a feature?▼

Run research when architecture or API choices are unclear, when exploration would consume over 20% of context, or when multiple conversations will work on the same area. It prevents re-exploration and preserves implementation capacity.

How long are research documents valid before they need updating?▼

Research files under 7 days old can be used as-is if the topic matches. Older files require re-validation by spot-checking 2-3 key claims, and should be regenerated after major refactors or dependency changes.

What happens to research documents after the feature ships?▼

Research files are working documents and should be deleted after the feature ships. If the synthesis has lasting value, promote it to docs/research/<topic>.md before deleting the working copy.

Can research findings be handed off to a new conversation?▼

Yes, include the research file via @-references when starting a new conversation. If an active plan exists, reference both files together so the fresh conversation picks up remaining work with full context.