rugged-gemini: Skill for Gemini CLI

Research Methodology Skill

Ensure code is informed by accurate, up-to-date knowledge — not stale training data.

When to Invoke

• Before implementing with unfamiliar tech
• Evaluating library/framework options
• Architect informing design decisions
• Builder encountering unknown API/pattern

Process

1. Topic Decomposition

Break into 2-5 keyword searchable topics:

"Task CRUD API with Supabase":
  1. Supabase client library JS/TS
  2. Supabase RLS policies
  3. Supabase real-time subscriptions
  4. PostgreSQL UUID PK patterns
  5. TS type generation from Supabase

2. Multi-Tool Search

Priority	Tool	Best For
1st	Qurio MCP	Deep doc search, official docs
2nd	Context7/similar MCP	Library-specific API refs
3rd	Supabase MCP	Supabase-specific docs
4th	search_web	General, blogs, SO
5th	read_url_content	Deep reading specific pages

Strategy: broad search → find doc page → deep read → search gotchas + edge cases.

3. Document Findings

Path: docs/research_logs/{feature_name}.md

# Research: {Topic}
Date: {date}
Researcher: {agent}

## Topics Investigated
1. {topic} — {tool} — {finding}

## Key Patterns
- {Pattern}: {description + code}

## API Signatures
```{lang}
// Exact API from docs

Gotchas

• {Gotcha}: {avoidance}

Code Examples

// Working examples from docs

Sources

• {title} — {learned}

Training Data Reliance

• {topic}: Relying on training data. No external verification.

### 4. Training Data Fallback

If no search yields results:
1. Document queries attempted + tools used
2. Explicitly state: "Relying on training data for {topic}. External verification unavailable."
3. Flag for human review
4. If critical, ask user for docs

**Never silently use training data when verification is available.**

### 5. ADRs

If research reveals choice between 2+ approaches, new dependency, or arch change → create ADR via `adr` skill at `docs/decisions/NNNN-short-title.md`.