research_skill

This skill conducts focused technology evaluations and architecture analyses, delivering actionable recommendations that balance YAGNI, KISS, and DRY
  • Python

12

GitHub Stars

1

Bundled Files

2 months ago

Catalog Refreshed

3 months ago

First Indexed

Readme & install

Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.

Installation

Preview and clipboard use veilstrat where the catalogue uses aiagentskills.

npx veilstrat add skill samhvw8/dotfiles --skill research

  • SKILL.md6.6 KB

Overview

This skill performs focused technical research using YAGNI, KISS, and DRY principles to deliver concise, actionable recommendations. It guides projects through defined phases: scope definition, information gathering, analysis, synthesis, and final recommendations. The output is pragmatic, trade-off aware, and prioritizes maintainability, security, and scalability.

How this skill works

I start by defining a tight research scope: key terms, recency, and evaluation criteria. I perform systematic multi-source searches (official docs, repos, expert content), cross-validate findings, and limit external queries to a small, deliberate number. Analysis extracts patterns, risks, and maturity assessments; synthesis produces clear recommendations, quick-start steps, and next actions. Reports focus on implementable guidance with explicit trade-offs and security/performance notes.

When to use it

  • Selecting or evaluating new technologies or libraries
  • Comparing architecture approaches or migration paths
  • Assessing trade-offs for scalability, security, or maintainability
  • Creating a feasibility study or competitive analysis
  • Defining implementation priorities that follow YAGNI/KISS/DRY

Best practices

  • Define strict scope and recency requirements before researching
  • Prioritize official docs, API references, and changelogs for accuracy
  • Limit external searches to a few high-quality queries and validate across sources
  • Highlight concrete trade-offs and avoid hypothetical solutions without cost estimates
  • Produce step-by-step quick starts and code examples to make recommendations actionable
  • Flag unresolved questions and migration or deprecation risks

Example use cases

  • Evaluate whether to adopt a new web framework for a production service, with migration plan and risks
  • Compare managed database options for cost, scalability, and operational burden
  • Analyze microservices vs. modular monolith for maintainability and deployment complexity
  • Produce security and performance implications for adding a third-party auth provider
  • Create a concise feasibility study for implementing event sourcing in an existing app

FAQ

I prioritize materials from the past 12 months by default and will include older sources only when historical context or stability data is required.

How many external searches will you run?

I limit research queries to a small, deliberate set to avoid noise — typically no more than five targeted searches — unless you request broader exploration.

Built by
VeilStrat
AI signals for GTM teams
© 2026 VeilStrat. All rights reserved.All systems operational