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 InternLM/WildClawBench --skill academic-literature-searchgit clone --depth 1 https://github.com/InternLM/WildClawBenchWrote 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/internlm/wildclawbench/academic-literature-search)<a href="https://agentmods.dev/skills/internlm/wildclawbench/academic-literature-search"><img src="https://agentmods.dev/badge/skills/internlm/wildclawbench/academic-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/internlm/wildclawbench/academic-literature-search"><img src="https://agentmods.dev/badge/skills/internlm/wildclawbench/academic-literature-search.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 | $0.00000 | $0.01525 |
| Opus 5 | $0.00000 | $0.00763 |
| Sonnet 5 | $0.00000 | $0.00305 |
| Haiku 4.5 | $0.00000 | $0.00153 |
Grade A, and why
academic-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 12d 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 — 182 lines — stays where its author put it; the contents beside it link to each section on GitHub.
学术文献检索技能
概述
这是一个专注于学术文献检索的专业工具，集成了多个权威学术数据库，提供全面、快速、准确的文献检索服务。支持多数据库并发检索、高级过滤、智能排序和多种输出格式。
核心功能
🔍 强大的检索能力
- 多数据库集成：Semantic Scholar、Crossref、arXiv、PubMed
- 智能查询解析：自然语言、布尔运算、字段限定、短语搜索
- 并发检索：同时查询多个数据库，毫秒级响应
- 高级过滤：年份、引用数、期刊类型、开放获取、语言等
📖 高级检索特性
- 自然语言查询
- 布尔运算符 (AND, OR, NOT)
- 字段限定搜索 (title:, author:, year:)
- 范围搜索 (year:2020-2024, citations:>100)
- 通配符搜索
🎯 精准的结果处理
- 智能去重：基于DOI、标题、作者等多维度去重
- 多维度排序：引用数、年份、相关性、影响力、趋势
- 高级过滤：（期刊、开放获取、文献类型）
- 结果丰富：自动补充元数据、计算影响力指标
- 质量评分：综合评分系统，提供最佳结果
💬 丰富的输出格式
- Markdown：适合阅读和笔记
- JSON：适合程序处理
- CSV/Excel：适合数据分析和导入
- BibTeX/RIS：适合参考文献管理
- HTML/XML：适合网页展示和数据交换
⚡ 性能优化
- 多级缓存：内存、磁盘、分布式缓存
- 智能重试：自动处理速率限制和网络错误
- 渐进式加载：快速返回第一批结果
- 请求合并：减少API调用次数
支持的数据库
| 数据库 | 数据量 | 优势领域 | 速率限制 |
|---|---|---|---|
| Semantic Scholar | 2.33亿+ | AI、计算机科学、多学科 | 100请求/5分钟（无认证） |
| Crossref | 1.4亿+ | 期刊文章、官方DOI | 无限制（礼貌使用） |
| arXiv | 220万+ | 预印本、计算机、物理、数学 | 无限制 |
| PubMed | 3500万+ | 生物医学、生命科学 | 10请求/秒 |
使用示例
基本检索
from agent import AcademicLiteratureSearchSkill
import asyncio
async def main():
skill = AcademicLiteratureSearchSkill()
params = {
"query": "deep learning in medical imaging",
"databases": ["semantic_scholar"],
"max_results": 10
}
result = await skill.execute(params)
print(result["results"])
asyncio.run(main())
高级检索
params = {
"query": "attention mechanism AND transformer",
"databases": ["semantic_scholar", "crossref"],
"year_range": "2020-2024",
"max_results": 100,
"sort_by": "citations",
"min_citations": 50,
"open_access_only": True,
"output_format": "markdown"
}
作为库使用
from agent import LiteratureSearchEngine
async def search():
async with LiteratureSearchEngine() as engine:
papers = await engine.search(
query="reinforcement learning",
databases=["semantic_scholar", "arxiv"],
max_results=20
)
for paper in papers:
print(f"{paper.title} - {paper.citation_count} citations")
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago First seen · 182 lines · 0 tokens per session scan A 6c0fbb8ef3af
academic-literature-search is a skill published in the GitHub repository InternLM/WildClawBench (519 stars, last pushed 25d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,525 tokens. 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
papyrus-writing
Base skill for the Papyrus writing assistant — the conduct, paper-quality principles, and LaTeX house style that every Papyrus editing task builds on. Load this first when working on a .tex paper in Papyrus. For the mechanics of a specific task, also load papyrus-latex-comments, papyrus-latex-suggestions…
arithmetic-evaluator
Evaluate arithmetic expressions and return numeric results. Use when a user asks an arithmetic question, requests calculation, or provides a math expression involving numbers, parentheses, and arithmetic operators.
swmm-end-to-end
Top-level orchestration skill for agentic SWMM modelling. Use when an agent needs one entrypoint that decides which module tools to run, in what order, and when to stop, for example to build, run, QA, and optionally calibrate a SWMM case from prepared or partially prepared inputs.
swmm-calibration
Calibration and validation scaffold for EPA SWMM. Use when an agent needs to (1) compare simulated vs observed flow, (2) evaluate candidate parameter sets, (3) rank explicit candidates by an objective, (4) run a bounded random / LHS / adaptive search for the best-fitting parameters, (5) run a publication-grade SCE-UA…
swmm-gis
GIS/DEM preprocessing for SWMM experiments using the user's own QGIS/GRASS layers. Use when the user asks to (1) delineate subcatchments through QGIS/GRASS (standard or entropy-guided), (2) preprocess QGIS-derived subcatchment polygons into builder-ready CSV, (3) identify high-entropy hotspot subcatchments, or (4)…
swmm-uncertainty
Parameter and forcing uncertainty for EPA SWMM. Without observed flow, call propagateparameterranges (global ranges, one SWMM run per sample, peak spread); the Morris/OAT/Sobol tools need an observed series. Use when an agent needs to (1) propagate parameter uncertainty through SWMM (fuzzy alpha-cut or Monte Carlo)…