Borrowing it
Nothing to install: this file belongs to irahardianto/awesome-agv. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/irahardianto/awesome-agv/main/.agents/skills/research-methodology/SKILL.mdgit clone --depth 1 https://github.com/irahardianto/awesome-agvWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/irahardianto/awesome-agv/research-methodology)<a href="https://agentmods.dev/skills/irahardianto/awesome-agv/research-methodology"><img src="https://agentmods.dev/badge/skills/irahardianto/awesome-agv/research-methodology/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/irahardianto/awesome-agv/research-methodology"><img src="https://agentmods.dev/badge/skills/irahardianto/awesome-agv/research-methodology.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00029 | $0.00687 |
| Opus 5 | $0.00015 | $0.00344 |
| Sonnet 5 | $0.00006 | $0.00137 |
| Haiku 4.5 | $0.00003 | $0.00069 |
Grade A, and why
research-methodology scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- research-methodology — 92% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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`. The `adr` skill template produces structured-spec-compatible ADRs with YAML frontmatter and `<!-- decision -->` annotations.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 104 lines · 29 tokens per session scan A e656cd53d1d2
research-methodology is a skill published in the GitHub repository irahardianto/awesome-agv (156 stars, last pushed 21d ago), licensed MIT. It adds 29 tokens to every session and 687 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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