Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/jxtse/scientific-research-skillsnpx agentmods add skills/jxtse/scientific-research-skills/literature-searchWrote 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/jxtse/scientific-research-skills/literature-search)<a href="https://agentmods.dev/skills/jxtse/scientific-research-skills/literature-search"><img src="https://agentmods.dev/badge/skills/jxtse/scientific-research-skills/literature-search/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/jxtse/scientific-research-skills/literature-search"><img src="https://agentmods.dev/badge/skills/jxtse/scientific-research-skills/literature-search.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 | $0.00143 | $0.01340 |
| Opus 5 | $0.00072 | $0.00670 |
| Sonnet 5 | $0.00029 | $0.00268 |
| Haiku 4.5 | $0.00014 | $0.00134 |
Grade A, and why
literature-search 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 13d 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 — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Literature Search
Systematic, multi-engine academic paper search.
When to Use
- User asks "find papers about X"
- User needs related work for a new project
- User wants to know the state of the art on a topic
- User asks for papers from a specific venue/author/year
Engine Selection
Choose engines based on the search goal:
| Goal | Primary Engine | Supplementary |
|---|---|---|
| Broad topic survey | Semantic Scholar | arXiv, Tavily |
| Latest preprints | arXiv (sort by submittedDate) | Semantic Scholar |
| Deep research / complex questions | Gemini deep research | Tavily + Exa |
| Specific paper by title | Semantic Scholar | Google Scholar (via Tavily) |
| Papers by author | Semantic Scholar (author search) | AMiner |
| Chinese research community | AMiner | Semantic Scholar |
| Industry/applied papers | Tavily (deep) | Exa semantic search |
| Social buzz / trending papers | Twitter/X (xreach) | |
| Code implementations | GitHub (gh search) | Exa (get_code_context) |
| Finding similar papers | Exa (semantic) | Semantic Scholar (citations) |
Workflow
Step 1: Understand the Query
Before searching, clarify:
- Scope: Broad survey vs. specific subtopic
- Recency: All time vs. last N years vs. latest only
- Venue preference: Top-tier only? Specific conference?
- Quantity: Top 5 vs. comprehensive survey
- Depth: Quick list vs. deep research with synthesis
Step 2: Select Search Strategy
Quick search (single engine): For simple, well-defined queries. Use Semantic Scholar or arXiv directly.
Multi-engine search (2-3 engines in parallel): For broader topics. Run engines simultaneously, deduplicate results.
Deep research (Gemini): For complex, multi-faceted research questions. Gemini deep research mode synthesizes across many sources and provides a structured analysis with citations. Use this when:
- The question spans multiple subfields
- You need synthesis, not just a list of papers
- The user explicitly asks for "deep research" or "comprehensive survey"
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.
- 13d ago First seen · 137 lines · 143 tokens per session scan A ab7cfdf23afa
literature-search is a skill published in the GitHub repository jxtse/scientific-research-skills (68 stars, last pushed 3mo ago), licensed MIT. It adds 143 tokens to every session and 1,340 once invoked, about $0.0007 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.
Other skills, from other repositories
paper-fetch
Retrieve one specified academic paper (by title, arXiv ID, DOI, URL, or a local .md/.txt/.pdf path the caller already has) and land it on disk as source.md plus a source.meta.json carrying a line-number section index. Checks context/papers/ for an existing copy first; local files and direct PDF URLs are read directly…
benchmark-archaeology
Evaluation Methodology Archaeology Campaign — 5 strategies for systematic analysis of AI/ML benchmarks, metrics, and leaderboards. Reveals construct validity issues, saturation, data contamination, and evaluation protocol inconsistencies.
bias-detection
Assess systematic biases in the evidence body — publication bias, reporting bias, and selective outcome reporting. Budget: 40 studies, 40 effect sizes, 40 web searches.
competitive-intelligence
Analyze competitor IP portfolios — comparative patent portfolio reports with strategy inference. Budget: 120 patent families, 15 claim parses, 40 web searches.
claim-analysis
Deep claim scope analysis — decompose independent/dependent claims and assess protection scope breadth. Budget: 30 patent families, 30 claim parses, 20 web searches.
adversarial-persona
Strategy: Role-play attacks from hostile personas — competing lab researcher, hostile reviewer, funding skeptic, domain outsider — each with distinct attack motivations and blind spots.