Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/kthorn/research-superpower/getting-startednpx skills add kthorn/research-superpower --skill getting-startedgit clone --depth 1 https://github.com/kthorn/research-superpowerWrote 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/kthorn/research-superpower/getting-started)<a href="https://agentmods.dev/skills/kthorn/research-superpower/getting-started"><img src="https://agentmods.dev/badge/skills/kthorn/research-superpower/getting-started.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00026 | $0.01119 |
| Opus 5 | $0.00013 | $0.00560 |
| Sonnet 5 | $0.00005 | $0.00224 |
| Haiku 4.5 | $0.00003 | $0.00112 |
Grade A, and why
Getting Started with Research Superpowers 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 5d 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.
How it starts
The opening of the file, as written. The whole thing — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Getting Started with Research Superpowers
Research Superpowers gives Claude Code systematic workflows for literature searching and review.
Focus: Finding, screening, and extracting data from published papers. NOT for analyzing experimental data or designing experiments.
What You Can Do
Use these skills for systematic literature reviews:
- Search literature - PubMed and Semantic Scholar integration
- Build screening rubrics - Define and test relevance criteria collaboratively
- Screen papers - Two-stage screening (abstract → deep dive) with scoring
- Extract data - Find specific methods, results, measurements from papers
- Traverse citations - Smart backward/forward citation following
- Large-scale screening - Parallel subagent processing for 50+ papers
- Track findings - Organized research sessions with summaries, PDFs, and deduplication
Available Skills
Literature Search & Review Skills (skills/research/)
- answering-research-questions - Main orchestration workflow (search → screen → extract → synthesize)
- building-screening-rubrics - Collaborative rubric design with test-driven refinement
- searching-literature - PubMed search with keyword optimization
- evaluating-paper-relevance - Two-stage screening (abstract → deep dive)
- subagent-driven-review - Parallel screening for large searches (50+ papers)
- checking-chembl - Check if medicinal chemistry papers have curated SAR data in ChEMBL
- traversing-citations - Semantic Scholar citation network traversal
- finding-open-access-papers - Unpaywall API to find free versions of paywalled papers
- cleaning-up-research-sessions - Safe cleanup of intermediate files after research complete
Basic Workflow
When user asks a literature search question:
- Read answering-research-questions skill - Main orchestration
- Announce: "I'm using the Answering Research Questions skill"
- Parse query - Extract keywords, data types, constraints
- Create research folder - Propose name, initialize tracking
- Optional: Build rubric - For large searches (50+ papers), use building-screening-rubrics skill
- Search → Screen → Extract → Traverse - Follow the workflow
- Check in regularly - Every 10 papers, checkpoint every 50
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.
- 5d ago First seen · 106 lines · 26 tokens per session scan A 95e4ccb6bf4e
Getting Started with Research Superpowers is a skill published in the GitHub repository kthorn/research-superpower (123 stars, last pushed 10mo ago), licensed MIT. It adds 26 tokens to every session and 1,119 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 2026-08-30.
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