Research Methodology Skill
SkillSearchStructured research protocol: investigate technologies, patterns, APIs before implementation. Multi-tool search, research logs, training data fallback honesty.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Research Methodology Skill skill
What this skill tells your AI
The instructions your AI receives, as published by irahardianto/awesome-agv in .agents/skills/research-methodology/SKILL.md and read by ahel’s review.
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`. The `adr` skill template produces structured-spec-compatible ADRs with YAML frontmatter and `<!-- decision -->` annotations.
## Agent Integration
| Agent | Usage |
|---|---|
| Architect | Research before design, inform ADRs |
| Backend | Unfamiliar APIs, library patterns |
| Frontend | Component libs, CSS patterns |
| Mobile | Platform APIs, Flutter packages |
| Database | Query patterns, extensions |
Research logs persist in `docs/research_logs/` for cross-session knowledge. ADRs follow `adr` skill format.
Signals
- GitHub stars
- 156
- Forks
- 53
- Last commit
- Aug 2026
Advanced
- Catalog kind
- skill
- Gateway key
research-methodology- Source
- github.com/irahardianto/awesome-agv