ChineseResearchLaTeX is a collection of LaTeX templates and an AI-assisted workflow for preparing Chinese research documents such as grant proposals, papers, theses, and academic CVs. Researchers use it to plan, format, review, compile, and revise these documents with human oversight. The catalogue skills and instructions support its agent-based research-writing workflow.
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 huangwb8/ChineseResearchLaTeX --skill research-literature-radargit clone --depth 1 https://github.com/huangwb8/ChineseResearchLaTeXWrote 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/huangwb8/chineseresearchlatex/research-literature-radar)<a href="https://agentmods.dev/skills/huangwb8/chineseresearchlatex/research-literature-radar"><img src="https://agentmods.dev/badge/skills/huangwb8/chineseresearchlatex/research-literature-radar/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/huangwb8/chineseresearchlatex/research-literature-radar"><img src="https://agentmods.dev/badge/skills/huangwb8/chineseresearchlatex/research-literature-radar.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 | $0.00096 | $0.03038 |
| Opus 5.5 | $0.00038 | $0.01215 |
| Sonnet 5.5 | $0.00019 | $0.00608 |
| Haiku 4.5 | $0.00010 | $0.00304 |
Grade A, and why
research-literature-radar 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 1mo 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 — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Literature Radar
目标
把来自不同发现渠道的候选论文转成可持续维护的研究雷达。research-literature-search 只是本 skill 的一个检索子步骤，负责基于显式关键词的数据库召回、字段规范化、canonical 去重和 provenance；本 skill 仍负责分层发现、跨渠道汇总、价值判断、分类、跟踪与归档。
本 skill 负责：
- 将用户目标转成筛选标准和论文类型配额；
- 对全部 canonical 候选分别判断相关性、证据深度、发表状态和身份可信度；
- 分类、排序、记录不确定性，并保留核心、辅助、待跟踪和范围外角色；
- 将入选论文映射到稳定 ID，写入论文库并维护跨轮次跟踪。
触发与输入概览
用户提出“找值得读的论文”“建立某主题论文雷达”“按经典/热点/顶会等类型推荐论文”或类似发现与学习需求时触发。
必填：用户选题和目标数量。可选：领域/子主题、五类论文配额（classic、rising-star、community、hot、top-venue）、时间窗、作者/venue 白名单、排除项、是否下载公开 PDF、笔记深度。
运行前读取 .bensz-api/research-literature-radar/catalog.jsonl（不存在则创建），并保留用户原始请求和运行日期。
流程
输入
按用户请求和配置文件提供必要输入；缺失信息应明确列出并停止依赖该输入的步骤。
执行步骤
- 仅将本 Skill 的设计缺陷（流程漏判、输入契约不完整或环境假设错误）视为可上报 bug；用户数据错误、第三方服务抖动、用户主动改源码和模型偶发波动不属于此范围。
- 发现设计缺陷时先脱敏记录到
~/.bensz-skills/bugs/，当前任务继续；只有用户明确要求公开上报时，才使用本机gh api直传，不 clone 仓库。 - 不收集用户名、主机名、工作目录、密钥、令牌、Cookie 或其它无关隐私；不得直接修改用户本地已安装 Skill 的源代码来“顺手修 bug”。
版本唯一来源为同目录 config.yaml:skill_info.version；本文件只描述稳定工作契约，不重复易变配置。
不要把论文发现简化成单一关键词检索。根据用户选题和目标数量，组合以下五类渠道；每类都可以使用联网检索、研究者脉络或社区信号，但必须记录其来源和用途：
- 经典论文：定位领域奠基者、明星研究者、综述、奖项和公认里程碑，再查找其代表性工作。
- rising-star 论文：寻找近 3–5 年持续突破的作者、实验室或研究线，关注尚未广为人知但影响快速上升的研究者。
- 社区精选：检查 Hugging Face Daily/Weekly/Monthly Papers、Import AI、研究者通讯、公众号/X 账号和可信整理页。
- 偶然的热点论文：关注近期发表后因讨论度、代码传播、新闻或社交平台而受到关注的论文，即使作者并不知名。
- 顶会/顶刊近期论文：仅在用户选题足够窄时重点使用，结合明确的 venue 和时间窗，避免宽主题造成无穷候选。
research-literature-search 负责其中“基于关键词检索学术数据库”的小步骤；经典作者脉络、社区精选和热点信号不能假设会自动出现在该检索 bundle 中。
首选：调用 search 后端
当分层策略需要关键词/数据库召回时，调用 research-literature-search 的 run，为其提供合法的 topic 和 5–25 条显式查询；随后调用其 validate。只接受 manifest.json 的 status 为 success 或 partial_success 的 bundle。没有需要数据库检索的渠道时，不得为了形式强行调用；但必须说明使用了哪些其它发现渠道。
将以下文件作为只读输入保存到本轮 radar run：
manifest.json
candidates_deduped.jsonl
provenance.jsonl
dedupe_map.json
候选必须符合 rls.paper.v1。保留 manifest、contract version、artifact hash、canonical 候选数量和 search source path，便于追溯。若实际调用了 search 但 bundle 校验失败、没有合法查询或 search skill 不可发现，应停止该检索子步骤并报告原因；不能静默改用另一套内嵌 provider。其它分层渠道仍可独立记录为补充发现，但不得伪装成 search 结果。
What ships with it
10 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.
- agents/openai.yaml 257 B
- CHANGELOG.md 1.2 KB
- config.yaml 1.1 KB
- README.md 1.1 KB
- scripts/build_landscape.py 7.4 KB runs code
- scripts/catalog.py 5.1 KB runs code
- scripts/finalize_corpus.py 191 B runs code
- scripts/generate_initial_corpus.py 13 KB runs code
- scripts/reconcile_integrity.py 7.7 KB runs code
- scripts/validate_layout.py 1.3 KB runs code
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.
- 1mo ago Changed · +4 lines df6c8ec5eb5f
- 1mo ago Changed · +20 lines 0e7a9aa30240
- 1mo ago First seen · 118 lines · 96 tokens per session scan A 466dcfd2ac99
research-literature-radar is a skill published in the GitHub repository huangwb8/ChineseResearchLaTeX (2,980 stars, last pushed 10d ago), licensed MIT. It adds 96 tokens to every session and 3,038 once invoked, about $0.0004 per session on Opus 5.5. 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 2026-09-05.
Other skills, from other repositories
semantic-scholar-deep
Deep research over the Semantic Scholar Graph API. Covers endpoints missing from allenai's lookup skill — paper references (backward citations), recommendations, batch paper lookup (up to 500 IDs), snippet search, and multi-hop citation graph traversal (BFS forward/backward). Use when the user asks to build a citation…
adaptyv
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API…
openevidence
Query OpenEvidence (clinical evidence assistant) without an MCP server. Closely mirrors the openevidence-mcp tool surface - check auth, list question history, fetch an article, or ask a new question (with optional follow-up thread). Use when the user asks medical / evidence-based clinical questions, references…
thesis-control
Use when AI-assisted thesis or manuscript edits risk claim drift, scope creep, loss of intended use, experiment-role promotion, or repeated revisions that fail to converge; provides author-intent control, lightweight or strict contracts, drift audits, revision escalation, and human gates.
manuscript-reframe
Reframe report-like academic drafts into paper-form scientific arguments while preserving or explicitly renegotiating author intent; requires an approved old-versus-proposed spine, evidence and argument baselines, analysis-role control, and post-edit drift review.
onescience-primitives
A resource-search tool for finding reusable OneScience building blocks. It searches a catalogue for the most relevant scientific workflow components and returns their supporting details and usage contracts.