forge-research

Convert broad research requests into sourced, confidence-scored claims.

Updated Aug 27, 2026
One-click install
npx skills add https://github.com/f4rkh4d/forge-skill --skill forge-research
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: forge-research
Source: https://github.com/f4rkh4d/forge-skill/tree/main/skills/research/forge-research
Command: npx skills add https://github.com/f4rkh4d/forge-skill --skill forge-research

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes assets (resource) components.

What problem does it solve?

forge-research prevents confident-but-shaky outputs by forcing AI or humans to perform real research: decomposing questions, prioritizing source quality, cross-checking claims, and explicitly stating confidence and unknowns.

Core Features & Use Cases

  • Question decomposition: turns a broad topic into searchable sub-questions so each step reduces uncertainty.
  • Source hierarchy with recency: prioritizes primary sources over secondary and tertiary, and favors newer material in fast-moving domains.
  • Search-vs-ask decisioning: searches for public answers, asks for private/contextual answers, and reads code when the question is codebase-specific.
  • Anti-hallucination verification: requires cited links and verified quotes, plus counter-searches for consequential conclusions.
  • Confidence-based synthesis template: outputs claims labeled as established, likely, disputed, and includes open questions plus a method section.

Quick Start

Use forge-research to answer this question by decomposing it into sub-questions, performing searches with a primary>secondary>tertiary source hierarchy, verifying cited URLs and quotes, and returning the full template with confidence levels and open questions.

Frequently Asked Questions about forge-research

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

FAQPage Schema
How do I prevent AI hallucinated citations in research outputs?▼

This Skill prevents hallucinated citations by requiring verified links and quotes, prioritizing primary sources, and counter-searching consequential conclusions. It produces confidence-scored claims labeled as established, likely, or disputed.

What's the best way to structure decision-support research with confidence scoring?▼

The best way to structure decision-support research is decomposing broad questions into searchable sub-questions, applying a primary to tertiary source hierarchy, and synthesizing findings into a template with confidence levels, sources, and open questions.

How does question decomposition work for evidence verification?▼

Question decomposition for evidence verification works by breaking broad investigation requests into smaller searchable sub-questions. Each sub-question is researched independently to reduce uncertainty and ensure fact verification across public web sources.

Does this research approach work for analyzing codebases or only public web sources?▼

This research approach works for both public web sources and codebases. It features search-versus-ask decisioning that searches for public answers, asks for private contextual answers, and reads code when the question is codebase-specific.

When should I not use automated fact verification for comparing trade-offs?▼

You should avoid using automated fact verification when you lack access to primary sources for validation, or when a domain requires specialized private data that cannot be counter-searched across public web sources to ensure citation discipline.