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 nsfc-reviewersgit 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/nsfc-reviewers)<a href="https://agentmods.dev/skills/huangwb8/chineseresearchlatex/nsfc-reviewers"><img src="https://agentmods.dev/badge/skills/huangwb8/chineseresearchlatex/nsfc-reviewers/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/nsfc-reviewers"><img src="https://agentmods.dev/badge/skills/huangwb8/chineseresearchlatex/nsfc-reviewers.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 | $0.00117 | $0.02213 |
| Opus 5.5 | $0.00047 | $0.00885 |
| Sonnet 5.5 | $0.00023 | $0.00443 |
| Haiku 4.5 | $0.00012 | $0.00221 |
Grade A, and why
nsfc-reviewers 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 — 173 lines — stays where its author put it; the contents beside it link to each section on GitHub.
NSFC 标书专家评审模拟器
- 用于“当前版本如果今天送审，风险在哪里、先改什么”的专家式评审。
- 默认优先并行多组独立评审；若
parallel-vibe不可用、被禁用或panel_count=1，自动降级为单组模式。 - 本技能只做读取、分析和汇总，不默认编译、不修改标书源文件。
输入
至少提供其一：
proposal_pathproposal_fileproposal_zip
可选：
focusoutput_pathstylegrant_typefunding_amountpanel_count
配置口径以 config.yaml 为准，尤其是：
review_dimensionsseverity_levelsreview_gradesstage_assessmentfunding_contextparallel_reviewoutput_settings
非目标
- 不负责改正文。
- 不负责模板、排版或编译问题。
- 不负责生成新的研究设计，只负责指出现有稿件的风险、优先级和修改方向。
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
- 当用户环境中出现因本 skill 设计缺陷导致的 bug 时，优先使用
bensz-collect-bugs按规范记录到~/.bensz-skills/bugs/，严禁直接修改用户本地 Claude Code / Codex 中已安装的 skill 源码。 - 若 AI 仍可通过 workaround 继续完成用户任务，应先记录 bug，再继续完成当前任务。
- 当用户明确要求“report bensz skills bugs”等公开上报动作时，调用本地
gh与bensz-collect-bugs，仅上传新增 bug 到huangwb8/bensz-bugs；不要 pull / clone 整个 bug 仓库。
前置检查
- 校验输入路径可读。
- 若是目录，按
proposal_files.patterns/exclude找出待读.tex。 .tex数量为 0 时直接失败；目录异常大时先确认范围。- 推荐用确定性脚本列文件：
python3 <nsfc_reviewers_path>/scripts/list_proposal_files.py --proposal-path <proposal_root>
通读与结构化理解
- 提炼主题、科学问题、假说、目标、技术路线、创新点、研究基础、团队条件、预期成果。
- 生成章节级索引，作为后续证据锚点。
- 先用用户明确给出的
grant_type/funding_amount，再谨慎从正文识别资助上下文。
并行多组评审或单组退化
- 先计算
effective_panel_count，并限制在[1, parallel_review.max_panel_count]。 - 以下情况直接走单组：
parallel_review.enabled == falseeffective_panel_count == 1- 找不到
parallel-vibe
并行模式关键步骤：
- 准备中间目录。
- 基于
references/expert_*.md和references/master_prompt_template.md生成 master prompt。 - 用
scripts/build_parallel_vibe_plan.py生成plan.json。 - 调用
parallel-vibe执行 N 组独立评审。 - 收集每组
panel_output_filename，允许个别 thread 缺失但不能中断整体汇总。
单组模式仍要保留 7 位专家画像的独立判断，再做组内聚合。
聚合与排序
- 跨组聚合规则读取
references/aggregation_rules.md。 - 至少
ceil(N * consensus_threshold)组指出的问题才算跨组共识。 - 跨组共识可触发严重度升级；重复问题要合并，保留最强证据锚点。
- 最终仍按
P0 → P1 → P2输出，并给出最小修改序列。
What ships with it
17 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.
- CHANGELOG.md 11 KB
- config.yaml 10 KB
- README.md 11 KB
- references/aggregation_rules.md 2.5 KB
- references/expert_01_innovation.md 1.5 KB
- references/expert_02_methodology.md 1.3 KB
- references/expert_03_foundation.md 1.3 KB
- references/expert_04_critical.md 1.4 KB
- references/expert_05_constructive.md 1.4 KB
- references/expert_06_significance.md 1.5 KB
- references/expert_07_clarity.md 1.5 KB
- references/master_prompt_template.md 1.2 KB
- scripts/build_parallel_vibe_plan.py 6.0 KB runs code
- scripts/cleanup_intermediate.py 7.0 KB runs code
- scripts/finalize_output.py 13 KB runs code
- scripts/list_proposal_files.py 5.6 KB runs code
- scripts/validate_skill.py 19 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 · +17 lines 8908c92d22c4
- 1mo ago First seen · 156 lines · 117 tokens per session scan A 8f5ea25b007c
nsfc-reviewers is a skill published in the GitHub repository huangwb8/ChineseResearchLaTeX (2,980 stars, last pushed 10d ago), licensed MIT. It adds 117 tokens to every session and 2,213 once invoked, about $0.0005 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 2026-08-30.
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.