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alvarovillalbaa
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research

Convert ambiguous business research inputs into scoped decision artifacts with citations.

Updated Feb 13, 2026
Search Tags:#web research#due diligence#evidence synthesis#account research#competitor intelligence#business research#ICP research
One-click install
npx skills add https://github.com/alvarovillalbaa/plugins --skill research-alvarovillalbaa
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: research
Source: https://github.com/alvarovillalbaa/plugins/tree/main/business-ops/skills/research
Command: npx skills add https://github.com/alvarovillalbaa/plugins --skill research-alvarovillalbaa

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you transform ambiguous business research questions into decision-ready artifacts that are scoped, evidence-backed, and clearly reasoned—without producing a generic search dump.

Core Features & Use Cases

  • Evidence-backed research loops: Runs short, iterative passes to build a scoped brief, comparison, ranked queue, or recommendation using dated evidence and explicit confidence.
  • Decision-oriented synthesis: Produces artifacts with visible logic, recommendations/next actions, caveats, and uncertainty—distinguishing observed facts from inference.
  • Lane-based routing: Supports primary lanes like competitor intelligence, diligence, ICP research, account research, and customer research, with optional overlays for synthesis, prospect enrichment, web-collection, Exa category discovery, and social-signal corroboration.

Quick Start

Ask the AI to research and synthesize competitor evidence for a scoped buying decision, producing a comparison table and a recommended next action with dated inline citations.

Frequently Asked Questions about research

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

FAQPage Schema
How do I turn ambiguous business research into evidence-backed briefs?▼

Business research inputs are converted into scoped decision artifacts through iterative, evidence-backed loops that produce briefs, comparisons, or ranked queues with dated inline citations and explicit confidence levels.

What is the best way to structure competitor intelligence for a buying decision?▼

Competitor intelligence is structured by routing inputs through dedicated lanes to synthesize public-web trails into decision-oriented comparison tables, distinguishing observed facts from inference and recommending next actions.

Can I use this approach for ICP and account research?▼

Yes, ICP and account research are supported as primary lanes, transforming target lists and account questions into ranked queues or recommendations with visible logic, caveats, and escalation paths.

How do I ensure my due diligence research avoids producing a generic search dump?▼

Due diligence research avoids generic search dumps by applying evidence-first sourcing discipline, lane selection, and requiring a final output with visible logic, uncertainty, and formatted inline markdown citations.

Does evidence synthesis require distinguishing observed facts from inference?▼

Yes, evidence synthesis requires decision-oriented outputs that explicitly distinguish observed facts from inference, providing visible logic, caveats, and a recommendation or escalation path.

What are the limitations of using automated research for customer research?▼

Automated customer research is limited by its reliance on public-web trails and evidence-first sourcing discipline, meaning it produces recommendations with explicit uncertainty and caveats rather than definitive conclusions.

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