rai-research

name	rai-research
description	Research with triangulated evidence to inform decisions. Use before ADRs or evaluations.
allowed-tools	["Read","Grep","Glob","Bash(ddgr:*)","WebFetch","WebSearch"]
license	MIT
metadata	{"raise.work_cycle":"utility","raise.frequency":"as-needed","raise.fase":"0","raise.prerequisites":"","raise.next":"","raise.gate":"","raise.adaptable":"true","raise.version":"2.0.0","raise.visibility":"public"}

Research

Purpose

Conduct epistemologically rigorous research to inform decisions. Standing on the shoulders of giants, not reinventing wheels.

Mastery Levels (ShuHaRi)

• Shu: Follow all steps with full evidence catalog and research prompt template
• Ha: Scale depth to decision importance; adapt prompt template
• Ri: Create domain-specific research protocols and custom prompts

Context

When to use: Before ADRs, when evaluating competing approaches, entering unfamiliar domains, or resolving parking lot items.

When to skip: Decision is low-stakes and reversible, or prior research exists in work/research/.

Inputs: Clear research question(s), decision context, depth constraint (quick/standard/deep).

Depth	Time	Sources	Use when
Quick scan	1-2h	5-10	Low-stakes, familiar domains
Standard	4-8h	15-30	Most ADRs, technology evaluation
Deep dive	2-5d	50-100+	Strategic decisions, unfamiliar domains

Steps

Step 1: Frame the Question

Define: primary question, secondary questions, decision this informs, depth constraint.

Epistemological principles: Seek disconfirming evidence (falsifiability), require 3+ sources per claim (triangulation), primary > secondary > tertiary sources.

Question is specific and falsifiable. Question too vague → decompose into sub-questions.

Step 2: Select Tool & Survey

Tool selection:

Tool	Best for
ddgr "query"	Quick scans, no API key needed
llm -m perplexity "query"	Deep research with citations
WebSearch	Reliable fallback

Gather sources: academic papers, official docs, GitHub repos (stars/activity), engineering blogs, community discussions.

10+ sources collected (scaled to depth).

Step 3: Build Evidence Catalog

Per source: type (primary/secondary/tertiary), evidence level, key finding, relevance.

Evidence level	Criteria
Very High	Peer-reviewed, production-proven at scale, >10k stars
High	Expert practitioners at established companies, >1k stars
Medium	Community-validated, emerging consensus, >100 stars
Low	Single source, unvalidated, <100 stars

Save to work/research/{topic}/sources/evidence-catalog.md.

Evidence catalog created with levels rated.

Step 4: Triangulate & Synthesize

Per major claim: find 3+ independent confirmations, note consensus vs disagreement, assign confidence (HIGH/MEDIUM/LOW), acknowledge contrary evidence explicitly.

Extract patterns: convergence points, gaps, RaiSE-specific vs general findings.

Major claims have 3+ sources. Contrary evidence documented.

Step 5: Recommend & Link

Produce: recommendation with confidence level, trade-offs, implementation implications, risks.

Connect to governance: create/reference ADR if architectural, update backlog if actionable, update parking lot if deferred.

Recommendation is actionable and traces to evidence.

Output

Item	Destination
Report	work/research/{topic}/{topic}-report.md
Evidence catalog	work/research/{topic}/sources/evidence-catalog.md
Navigation	work/research/{topic}/README.md
Next	ADR, backlog item, or parking lot update

Quality Checklist

• Research question is specific and falsifiable
• 10+ sources consulted (scaled to depth)
• Evidence catalog created with levels rated
• Major claims triangulated (3+ independent sources)
• Confidence level explicitly stated on recommendation
• Contrary evidence acknowledged (not hidden)
• Governance linkage established (ADR, backlog, or parking lot)
• NEVER present single-source findings as consensus

References

• Research prompt template: references/research-prompt-template.md
• Existing research: work/research/
• Epistemology: falsifiability, triangulation, source hierarchy