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 skills add kthorn/research-superpower --skill evaluating-paper-relevancegit 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/evaluating-paper-relevance)<a href="https://agentmods.dev/skills/kthorn/research-superpower/evaluating-paper-relevance"><img src="https://agentmods.dev/badge/skills/kthorn/research-superpower/evaluating-paper-relevance/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/kthorn/research-superpower/evaluating-paper-relevance"><img src="https://agentmods.dev/badge/skills/kthorn/research-superpower/evaluating-paper-relevance.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00020 | $0.04981 |
| Opus 5 | $0.00010 | $0.02491 |
| Sonnet 5 | $0.00004 | $0.00996 |
| Haiku 4.5 | $0.00002 | $0.00498 |
Grade A, and why
Evaluating Paper Relevance scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s "https://www.ebi.ac.uk/chembl/api/data/document.json?doi=$doi" How it starts
The opening of the file, as written. The whole thing — 585 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Evaluating Paper Relevance
Overview
Two-stage screening process: quick abstract scoring followed by deep dive into promising papers.
Core principle: Precision over breadth. Find papers that actually contain the specific data/methods user needs, not just topically related papers.
When to Use
Use this skill when:
- Have list of papers from search
- Need to determine which papers have relevant data
- User asks for specific information (measurements, protocols, datasets, etc.)
- Screening papers one-by-one
- Any research domain (medicinal chemistry, genomics, ecology, computational methods, etc.)
Choosing Your Approach
Small searches (<50 papers):
- Manual screening with progress reporting
- Use papers-reviewed.json + SUMMARY.md only
- No helper scripts needed
- Report progress to user for every paper
Large searches (50-150 papers):
- Consider helper scripts (screen_papers.py + deep_dive_papers.py)
- Use Progressive Enhancement Pattern (see Helper Scripts section)
- Create README.md with methodology
- May want TOP_PRIORITY_PAPERS.md for quick reference
- Use richer JSON structure (evaluated-papers.json categorized by relevance)
- Consider using subagent-driven-review skill for parallel screening
Very large searches (>150 papers):
- Definitely use helper scripts with Progressive Enhancement Pattern
- Create full auxiliary documentation suite (README.md, TOP_PRIORITY_PAPERS.md)
- Consider citation network analysis
- Plan for multi-week timeline
- Strongly consider subagent-driven-review skill for parallelization
- May need multiple consolidation checkpoints
Two-Stage Process
Stage 1: Abstract Screening (Fast)
Goal: Quickly identify promising papers
Score 0-10 based on:
- Keywords match (0-3 points): Does abstract mention key terms relevant to the query?
- Data type match (0-4 points): Does it mention the specific information user needs?
- Examples: measurements (IC50, expression levels, population sizes), protocols, datasets, structures, sequences, code
- Specificity (0-3 points): Is it specific to user's question or just general background/review?
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.
- 11d ago First seen · 585 lines · 20 tokens per session scan A d1f6f958af92
Evaluating Paper Relevance is a skill published in the GitHub repository kthorn/research-superpower (124 stars, last pushed 10mo ago), licensed MIT. It adds 20 tokens to every session and 4,981 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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