NSFC Benzi Audit skill

A skill that audits a draft NSFC grant application: it finds logic gaps and returns a prioritized list of fixes

  • 138 ★ on the repository
  • Last commit
  • MIT
Skill

Extended access

Why this level

  • The agent reads a draft that may contain names, project numbers and unpublished data
  • When checking topics the agent queries kd.nsfc.cn and OpenAlex and sends them search terms
All reasons and checks

jiankang1991/nsfc-benzi-audit

Install

In your terminal, with SkillFoxx CLI

npx skillfoxx add skills/nsfc-benzi-audit

Detects the agents on your machine, shows the access level and pins the version.

Other ways to install

Access and checks

How we review

Extended accessThe tool runs code or makes network calls and only reads external data.

  • The agent reads a draft that may contain names, project numbers and unpublished data
  • When checking topics the agent queries kd.nsfc.cn and OpenAlex and sends them search terms

This is third-party code. Review the repository files before installing.

What it does

NSFC Benzi Audit is an Agent Skill for applicants to the National Natural Science Foundation of China (NSFC). The agent reads a draft in PDF, DOCX, Markdown or plain text and checks how the title, abstract, rationale, key scientific questions, research content, innovation and research basis support each other. For every root problem it opens one numbered item with a level: must fix, verify, should fix or polish. The output is a Markdown report: overall judgment, a one-page logic map, notes by section, a table showing how questions are covered by content and validation, and rewrite skeletons for the title, abstract and innovation points. After a revision, the skill rechecks the old comments and marks each as resolved, partly resolved, still open, withdrawn or impossible to judge from the material. Medical, geospatial and information-communication drafts get their own checklists. The script `nsfc_ack_grants.py` finds numbers of active grants in paper acknowledgements through OpenAlex, and the kd.nsfc.cn lookup helps judge topic overlap risk and the choice of application code.

Who it is for. Applicants for NSFC grants (Young, General, Regional) who already have a draft and want a structural look at the argument before submitting.

Good fit when

  • The draft lists many technical metrics but never states the scientific question, and you want to see that before submitting
  • The application went through one round of edits and you need to know which comments are closed and which remain
  • You have an anonymized funded application for comparison and want reusable writing patterns from it
  • The topic is medical or remote sensing and needs a check of the chain from mechanism to validation
  • Before submitting you want to check whether the topic overlaps with projects already funded

Not a fit when

  • The application has to be written from scratch: the skill does not write for the author or invent data, papers or results
  • A formal expert review is required: the report stays advice to the applicant and does not count as one
  • You need the terms of the current call: the rules were verified only up to 12 September 2026, so use the official guide for exact requirements
  • You want to bulk-crawl kd.nsfc.cn or log in to the foundation system for the applicant: the README rules this out

Example request

Use $nsfc-benzi-audit to audit my NSFC Young Scientists application: file application.docx, 2026 form, free exploration research

Limitations

The skill advises on the draft text and does not guarantee a successful application. The baseline rules were checked on 12 September 2026, and how they apply depends on the year, project category, form version and funding mode, so terms of other years need separate checking. For PDF and DOCX the agent must be able to extract text, tables and figures; otherwise supply the extracted text. Without network access, official rules, literature and kd data are marked unchecked. The repository description is in Chinese, while SKILL.md is in English.

How to disable. Run `npx skills remove nsfc-benzi-audit -g -a claude-code` (use `-a codex` for Codex) or delete the `nsfc-benzi-audit` folder from the agent skills directory by hand.

FAQ

How do I install NSFC Benzi Audit in Claude Code?

Run `npx skills add jiankang1991/nsfc-benzi-audit -g -a claude-code -y`. Without Node.js, copy the `nsfc-benzi-audit` folder into `~/.claude/skills` and open a new session, then call the skill by name.

Does the skill write the application for me?

No, it only diagnoses an existing draft and suggests edits. It does not invent papers, data, partners or results, and the use of AI in the application stays your responsibility under the foundation rules.

What should I give it as input?

The minimum is a core fragment of the application. A full file works better, with the year, project type, funding mode, application code and your main worry. Anonymize names, project numbers and unpublished data.

How does the skill learn about competing projects?

It searches kd.nsfc.cn for funded and completed projects and records the queries and findings. No match proves nothing, because the completed-projects base lags in time.

Can I show the skill someone else's funded application?

Yes, after anonymizing it: remove names, project numbers, contacts and unpublished data, and state the project type, funding year, code and research attribute. The skill takes writing patterns from the sample and does not copy its text, data or figures.

Discussion

No questions or reviews yet.

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README in short

The Chinese README covers use cases and exclusions, install commands for Codex and Claude Code, the input format, the report contents and before-and-after edit examples. Four breakdowns from a real Young Scientists draft show how technical metrics become a scientific question and how innovation claims lose slogans in favor of comparison with existing work.

README badge

Original SKILL.md

---
name: nsfc-benzi-audit
description: Use for applicant-facing diagnosis or re-audit of a Chinese NSFC (国自然) application draft — 本子把脉、本子体检、帮我看国自然本子、申请书修改建议、标书逻辑诊断、青年/面上/地区基金申请书修改、对照已中本子、科学问题凝练、代表作支撑、选题撞题核查 or 申请代码检查. Accepts PDF/DOCX/Markdown/text and checks title, abstract, rationale, key scientific questions, research contents, innovation, feasibility, research basis and cross-section consistency, calibrated to the application year and form. Does not write applications from scratch or provide formal expert-review opinions.
license: MIT; see LICENSE
---

# NSFC Benzi Audit

Produce applicant-facing diagnosis and revision advice grounded in the supplied draft. Expose substantive logic breaks, weak evidence and high-impact fixes. Do not fabricate literature, results, project histories, budgets or official requirements.

This workflow gives applicant revision advice. If the user requests formal communication review, identify that separate scope and use a suitable review skill only if available. No companion skill is required for draft-internal diagnosis.

## Workflow

1. Select the source and establish the review scope.
   - Prioritize the source and revision explicitly identified by the user. Separate target drafts, funded examples, old reports, templates and notes. Never select a draft merely because it is the largest Markdown file or named `full.md`/`output.md`.
   - Reuse extracted text only after matching its title, revision and contents to the selected original. Record source path/version and extraction provenance. If multiple plausible drafts cannot be distinguished, ask which one to use.
   - For PDF/DOCX, use available extraction/OCR tools and preserve headings and tables. If extraction is unavailable, state the limitation and work on readable supplied material; do not invent a tool or require a named companion skill.
   - Inspect original figures when judging visual logic or evidence, including preliminary results. Converted mermaid/OCR is not authoritative. Missing extraction is a material gap, not proof that the applicant omitted content.
   - Record application year, exact project category (including 青年 A/B/C where known), form version/source, funding mode (包干制/预算制/待确认), research attribute, application code and source completeness. Keep historical-example years separate from the target year.
   - Use questions, goals and methods as analytical dimensions wherever they occur. Require separate headings only when the applicable form requires them. If year or form is unknown, continue content diagnosis and mark rule applicability unresolved.
   - For a quick pass, focus on title, abstract, scientific questions, contents, innovation and basis. For full diagnosis, cover all supplied sections. A 专项 request (one chapter, one question) stays within that scope. Do not report absent attachments or omitted excerpts as confirmed defects in the original application.
   - For long or multi-file drafts, keep section/page locators and a running list of candidate findings with their locations while reading; before closing, revisit only unresolved candidates and report any source ranges you could not read.

2. Load the relevant references.
   - Always read `references/benzi-logic.md` for logic diagnosis, writing advice and the signal → countercheck → grading table used for every finding.
   - Read `references/current-rules.md` before judging form, year/category compliance, research attributes, application codes, budgets, ethics or integrity requirements. It contains a dated 2026 baseline; verify rules for the target year and stage.
   - Read `references/audit-surfaces.md` for full diagnosis, figures, validation, annual plans, outcomes, budgets, literature or form checks.
   - Read `references/question-distillation.md` for question-source and distillation quality, or claims of 原创/独辟蹊径/瓶颈/交叉/跨域类比. Its strong forms apply when the draft actually makes that claim; they are not mandatory headings for every draft.
   - Read `references/representative-works.md` when the draft lists representative works or relies on publications. Prefer supplied PDFs, then available DOI/publisher/preprint lookup tools. A literature/citation lookup skill is an optional integration if installed. Distinguish bibliographic verification, abstract access and full-method verification; mark remaining evidence 未核实.
   - Read `references/kd-lookup.md` for topic collision, code calibration, output comparisons or applicant-requested self-overlap checks. The applicant runs portal queries; never automate login or captcha. For collision checks, first run `scripts/nsfc_ack_grants.py` to shortlist in-progress 批准号 from paper acknowledgments. Record module, query, year fields, date, error/zero-hit status and counts before making coverage claims.
   - Read `references/exemplar-learning.md` for funded/successful examples or improvement from sample applications. Use matched contexts, anonymized patterns and transferability limits; do not copy facts or distinctive wording into the target.
   - Read `references/information-communication.md` for information science, communications, networks, applied AI, security, quantum communication or related information engineering.
   - Read `references/geospatial-remote-sensing.md` for remote sensing, GIS, SAR/InSAR, optical/hyperspectral imagery, DEM/terrain, point clouds, spatial databases/graphs, trajectories, video GIS or city 3D models.
   - Read `references/medical-biomedical.md` for medicine, cohorts/specimens, disease mechanisms, cell/animal/organoid models, biomarkers, interventions, ethics or biosafety.
   - Use `assets/report-template.md` as an adaptable report shape unless the user requests another format.

3. Build the logic and evidence maps.
   - Extract object/scenario, problem/goal, method/path, innovation, validation and expected significance; track them across title, abstract, rationale, contents, objectives, questions, route and basis.
   - Include the actual research attribute and any historical scientific-question statement when relevant, without mixing their form versions.
   - Build a many-to-many question–content–objective–validation map. Report uncovered questions or tasks with no explained scientific role, not unequal item counts. Supporting methods and validation tasks can share a question.
   - Distinguish the source of a question (literature, theory, observation, own work, demand) from the applicant's capacity to study it. Assess personal contribution, available resources and preliminary evidence in field-appropriate forms; no universal first-author SCI or pre-experiment threshold.

4. Recheck prior findings when a prior report/list is supplied in files or chat.
   - First match it to the same application. Check every earlier 必改 item against both the new text and the applicable evidence/rule, then diagnose the current draft normally. Keep old IDs; give new issues unused IDs and cross-reference merged or split ones.

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