nsfc-benzi-audit

REVIEW · 63
Registry indexed

Use when a user wants applicant-facing diagnosis and revision advice on a Chinese NSFC (国自然) application draft — asks for 本子把脉, 本子体检, 国自然申请书修改建议, NSFC benzi audit, 帮我看国自然本子, 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, 从中标样本提炼写法规律, 选题撞题核查, or 申请代码选得对不对 — or wants critique of the title, abstr

Verified installs0
Stars46
Version1.0.0
Quality63/100 · Promising
Trust63/100 · Sandbox only
Audit77/100 · Needs review

Supply asset profile

Research and knowledge work

Deep research, source comparison, literature review, RAG, knowledge search, and reports.

Browse track

Scenario

Document processing

I need my agent to read PDFs, extract tables, and turn documents into structured data.

Agent fit

Claude Code + CLI + Codex

Codex, Claude Code, Cursor, CLI, or custom agents.

Install

Ready

npx skills add jiankang1991/nsfc-benzi-audit --skill nsfc-benzi-audit

Maintenance

fresh

1d since push

Risk

Needs review

The SKILL.md excerpt is truncated, but the provided content is sufficient to assess the skill's purpose and workflow.

GitHub quality

46

63/100 Quality · 71/100 Trust

Coverage tags

ResearchDocument processingsecurityagent-skill

Review notes

The SKILL.md excerpt is truncated, but the provided content is sufficient to assess the skill's purpose and workflow. · The skill references external references (e.g., references/benzi-logic.md) that are not fully included in the excerpt, but this is expected for a multi-file skill.

Agent adoption scorecard

Trust, audit, and install readiness at a glance

These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.

Quality

Promising
63

Useful candidate, but compare it with alternatives before adopting.

Trust

Sandbox only
63

Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.

Audit

Needs review
77

A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.

OpenAgentSkill Trust Score v5

Human review before install

Run only in a sandbox and compare close alternatives before using it for real work.

CodexClaude CodeCursorOpenAgentSkill CLI

Stars

46 GitHub stars

Repo activity

46 stars, 5 forks

Maintenance

1d since push

License

MIT

Install

npx skills add jiankang1991/nsfc-benzi-audit --skill nsfc-benzi-audit

Install safety

standard package or runtime install path

Permission surface

filesystem or document access, network or browser access

Agent outcomes

No agent outcome data yet

Docs

Strong README/SKILL.md context

Risk summary

Review before production

  • The SKILL.md excerpt is truncated, but the provided content is sufficient to assess the skill's purpose and workflow.
  • Low GitHub adoption signal
  • Quality score needs review
  • GitHub adoption: 46 GitHub stars

Install readiness

Install path available

  • Install path is available
  • Repository evidence is available
  • License is declared
  • No Agent Proven outcome evidence yet

Agent-readable metadata

Machine-readable decision data for this skill.

Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.

Open JSON

Suited tasks

  • Document processing workflows
  • Claude Code teams
  • builders willing to evaluate younger projects
  • Read uploaded files

Suited agents

CodexClaude CodeCursorOpenAgentSkill CLICLI

Install decision

Command
npx skills add jiankang1991/nsfc-benzi-audit --skill nsfc-benzi-audit
Policy
review
Human review
yes

Trust and risk

Trust
63/100
Audit
77/100
Risk level
Needs review

Outcome loop

Endpoint
/api/agent/outcome
Event ID
resolve
Outcomes
5

Install command

npx skills add jiankang1991/nsfc-benzi-audit --skill nsfc-benzi-audit

Do not use when

  • teams that need a vendor-supported SLA
  • production agents without a repository review
  • Low GitHub adoption signal
  • The SKILL.md excerpt is truncated, but the provided content is sufficient to assess the skill's purpose and workflow.
  • The skill references external references (e.g., references/benzi-logic.md) that are not fully included in the excerpt, but this is expected for a multi-file skill.

Agent safety v2

53/100 · Avoid automatic install

Experimentalreview

Sparse or mixed signals. Useful for discovery, but not for autonomous installation.

Test manually in an isolated workspace and compare against safer alternatives.

Resolve via API

medium

Browser automation

Skill may drive a browser or interact with web pages.

medium

Network access

Skill likely fetches remote pages, APIs, repositories, or external services.

medium

Filesystem access

Skill may read or write project files, documents, generated artifacts, or local workspace state.

medium

Database access

Skill may inspect schemas, query databases, or work with persistent stores.

  • The SKILL.md excerpt is truncated, but the provided content is sufficient to assess the skill's purpose and workflow.

Install targets

Install this skill in your agent workflow

Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.

skill install

OpenAgentSkill CLI

Resolve policy, run the source installer safely, and report a verified install receipt.

$ npx --yes https://github.com/Leon-Drq/openagentskill/releases/download/cli-v0.2.1/openagentskill-0.2.1.tgz install jiankang1991-nsfc-benzi-audit

Agent resolve plan

Let an agent verify fit before installing.

The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.

Open text plan

Agent should check

  • Task fit and alternatives from Resolve API.
  • Audit score, trust score, and safety policy warnings.
  • Install target compatibility for Codex, Claude Code, Cursor, or CLI.

Copy prompt

Task: Use nsfc-benzi-audit in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20nsfc-benzi-audit%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/jiankang1991-nsfc-benzi-audit/install
Install command: npx skills add jiankang1991/nsfc-benzi-audit --skill nsfc-benzi-audit
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.

Agent handoff

Give an agent the install path, not another directory page.

Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.

Open install API

Agent prompt

Use nsfc-benzi-audit for this task. Review https://www.openagentskill.com/api/skills/jiankang1991-nsfc-benzi-audit/install, then install with: npx skills add jiankang1991/nsfc-benzi-audit --skill nsfc-benzi-audit

Registry metadata

Agent-readable profile for automatic skill selection.

This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.

Open manifest

Agent fit

63/100

Document processing

Platforms

Claude Code

Audit report

Needs review · 77/100

A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.

View audit reportView eval report

Agent decision cockpit

Fallback candidate for Document processing

Prototype with this skill first; keep a fallback candidate ready.

63
Readiness
Prototype
Stage

Role in stack

Fallback candidate

Primary fit

Document processing

Trust label

Prototype first

Install path

Command ready

Use when

  • Document processing workflows
  • Claude Code teams
  • builders willing to evaluate younger projects

Evidence

  • recent repository activity
  • install command or GitHub repo available
  • 63/100 quality profile
  • 3 OpenAgentSkill engagement events

review first

  • Low GitHub adoption signal
  • The SKILL.md excerpt is truncated, but the provided content is sufficient to assess the skill's purpose and workflow.

Implementation path

  1. 1Install it in a sandbox agent and run one Document processing task end to end.
  2. 2Compare output quality, latency, and failure behavior against at least one alternative.
  3. 3Promote it into production only after reviewing repository permissions, license, and maintenance signals.

Trust profile

Sandbox only

Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.

63
OpenAgentSkill Trust Score

GitHub adoption

CHECK

46 GitHub stars

Stars/forks activity

CHECK

46 stars, 5 forks; issue activity unavailable in current metadata

Recent maintenance

PASS

1d since push

License clarity

PASS

MIT

Good signals

  • AI review approved
  • Install path is available
  • Repository evidence is available
  • Recently maintained repository
  • Install command has no obvious high-risk pattern
  • Outcome loop is ready but needs first real agent run

Review before install

  • The SKILL.md excerpt is truncated, but the provided content is sufficient to assess the skill's purpose and workflow.
  • Low GitHub adoption signal
  • Quality score needs review
  • GitHub adoption: 46 GitHub stars
  • Stars/forks activity: 46 stars, 5 forks; issue activity unavailable in current metadata
  • No real agent outcome reports yet
  • Human review required before unattended installation

Recommended action

Run only in a sandbox and compare close alternatives before using it for real work.

Quality profile

Promising candidate for agent workflows

Useful candidate, but compare it with alternatives before adopting.

63
GitHub stars
46
Freshness
1d ago
Install ready
Yes
License
MIT
Review before install: Low GitHub adoption signal · The SKILL.md excerpt is truncated, but the provided content is sufficient to assess the skill's purpose and workflow.

Workflow fit

Use this skill in these scenarios

Workflow fit

Add it to a complete workflow

Alternative shortlist

Compare before you install

Similar skills that may fit this task.

Compare all

Overview

--- name: nsfc-benzi-audit description: Use when a user wants applicant-facing diagnosis and revision advice on a Chinese NSFC (国自然) application draft — asks for 本子把脉, 本子体检, 国自然申请书修改建议, NSFC benzi audit, 帮我看国自然本子, 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, 从中标样本提炼写法规律, 选题撞题核查, or 申请代码选得对不对 — or wants critique of the title, abstract, key scientific questions, rationale, research contents, innovation, feasibility, research basis, 代表作/代表性论著 quality and support, or cross-section consistency. Accepts PDF/DOCX/Markdown/text or extracted draft text. This is applicant revision advice, not formal communication-review opinions; for expert review forms use nsfc-review. ---

# NSFC Benzi Audit

Use this skill to produce applicant-facing diagnosis and revision advice for NSFC application drafts. The goal is to expose logic breaks, weak scientific-question framing, mismatched sections, and high-impact fixes before submission.

Do not write a formal peer-review opinion unless the user explicitly asks for communication review; route that to `nsfc-review`. Do not fabricate facts, papers, project histories, budgets, or official rule details. Keep final advice grounded in the draft text and clearly mark uncertain extraction/OCR issues.

## Workflow

1. Locate and extract the draft. - For PDF input, use PDF extraction/OCR as needed. Prefer existing extracted Markdown such as `full.md` or `output.md` when available. - For DOCX input, extract text while preserving headings and tables where possible. - For extracted Markdown/text folders, prefer `output.md`, `full.md`, or the largest readable Markdown/text file; inspect images only when visual logic diagrams or tables matter. - If the file is scanned or extraction is noisy, state the limitation in the report and avoid treating OCR artifacts as applicant mistakes.

2. Identify the review scope before judging. - Extract project category, research attribute, application code, title, abstract, keywords, applicant/team context, and section boundaries. - If the user asks for a quick pass, inspect title, abstract, scientific questions, research contents, innovations, and research basis first. - If the user asks for full diagnosis, inspect the whole application by section.

3. Load the right references. - Always read `references/benzi-logic.md` before diagnosing logic or writing suggestions. - Read `references/audit-surfaces.md` for full diagnosis, structure/form checks, figure/readability checks, literature-current-status checks, or policy-risk triage. - Read `references/question-distillation.md` when judging how well the scientific question is distilled: the 关键科学问题 importance argument, the rationale's convergence chain, the 科学问题属性 justification, drafts claiming 原创/独辟蹊径/卡脖子/瓶颈/学科交叉, or when the user asks whether the 科学问题凝练得好. - Read `references/representative-works.md` when the draft lists 代表性论著/代表作/主要论文, or when 研究基础 leans on the applicant's publications. Do not judge those works from titles alone — get the abstract and method from the applicant's PDFs first, else via the `paper-lookup` skill, else mark them 未核实. - Read `references/kd-lookup.md` when the audit needs external evidence rather than draft-internal logic: 撞题/选题重复风险, 申请代码是否送对学部, 预期成果是否超额承诺, 申请人自己已资助/已结题项目的重复度与绩效, or a draft claiming 国内尚无人开展. The applicant runs the queries on kd.nsfc.cn; never automate its login or captcha, and never report a lookup result without the query string, hit count, and date. - Read `references/exemplar-learning.md` when the user provides already-funded/successful examples, asks to compare with "中的本子"/"中标本子", or asks to improve this skill from sample applications. - Read `references/information-communication.md` when the draft or provided examples involve information science, communication networks, optical networks, computer networks, data centers, remote sensing information processing, applied AI, network security, quantum communication, or related information-engineering directions. - Read `references/geospatial-remote-sensing.md` when the draft or provided examples involve remote sensing, GIS, geospatial intelligence, DEM/terrain/geomorphology, spatial databases, point clouds, SAR/optical/hyperspectral imagery, video GIS, camera networks, POI/trajectory/location data, city 3D modeling, or geospatial knowledge graphs. - Read `references/medical-biomedical.md` when the draft or provided examples involve medicine, clinical research, biomedicine, disease mechanisms, patient cohorts, specimens, animal/cell/organoid models, biomarkers, diagnostics, therapy/intervention, immunology, ethics, or biosafety. - Read `references/current-rules.md` when checking current-year compliance, research attributes, application-code risk, budget/ethics/scientific-integrity issues, or anything tied to official NSFC rules. - Use `assets/report-template.md` as the output shape unless the user requests another format.

4. If successful examples are provided, separate exemplar learning from target diagnosis. - Treat funded examples as pattern evidence, not as text to copy or proof of causality. - Anonymize names, project numbers, institutions, unpublished data, and sensitive achievements before extracting patterns. - Prefer patterns repeated across matched examples: same project type, similar discipline/application code, similar research attribute, or comparable career stage. - Apply exemplar patterns as contrastive questions: what does the target draft fail to make visible that successful examples make visible?

5. Build the one-page logic map. - Extract the draft's core logic elements: object/scenario, focused problem or goal, method/path, distinctive feature or innovation, and data/validation loop. - Extract the abstract logic chain: object/problem, method/goal, contents/innovation, achievement/significance. - Map these terms across title, abstract, rationale, research contents, scientific questions, innovation, feasibility, and research basis. - If a 科学问题属性 statement exists, map it too and check it cross-validates the key scientific questions. - Mark missing, vague, inconsistent, or duplicated elements.

6. Diagnose by priority, not by page order. - Lead with the three decisive funding factors (创新性、技术路线、前期研究基础) and the three scored axes (课题、申请人、研究条件); see `references/benzi-logic.md`. Because 会评 leans on 通讯意见 + title + abstract (see `references/current-rules.md`), weight title/abstract/通讯-facing clarity accordingly. - High priority: flaws likely to affect funding judgment, such as no real scientific question, engineering/technical task posing as basic research, object too broad, problem not focused, innovation unsupported, methods not tied to scientific questions, or research basis unrelated. - Medium priority: section-level weaknesses, such as literature review not pointing to proposed contents, research objectives not scientific enough, feasibility too generic, innovation phrased as slogans, required columns that may be missing, figures that do not match the text, or literature gaps that weaken the rationale. - Low priority: expression, structure, title length, repeated wording, formatting, and local polishing.

7. Give concrete revision actions. - For every major finding, cite the draft section or quoted phrase briefly, explain why it is a problem, and give an actionable fix. - Prefer rewrite recipes and replacement skeletons over generic advice. - When suggesting rewritten text, label it as a draft example that the applicant must verify against facts and literature.

8. Output a Markdown report. - Default filename: `本子诊断报告.md` next to the source draft when working in files; otherwise answer in chat. - Include: overall judgment, one-page logic map, prioritized fixes, section-by-section findings, structure/form checks, figure/readability checks, data/validation evidence mapping, representative-works support (when listed), funding-landscape/topic-collision checks (when kd lookups were run), literature checks, consistency matrix, candidate rewrites, official-rule status, and limits. - If successful examples were used, include a short exemplar-derived pattern section with transferability limits. - Do not paste long extracted source text. Quote only short phrases needed to support findings.

## Style

- Write in simplified Chinese unless the user asks otherwise. - Be direct, specific, and applicant-facing: "建议将..." rather than "评审认为..." unless simulating review. - Separate "must fix before submission" from "can polish later". - Avoid empty comments such as "加强创新性"; say which scientific question, mechanism, model, method, experiment, or evidence must be changed. - Preserve academic integrity: do not invent literature, data, publications, patents, collaborations, preliminary results, or official policy requirements. - Do not copy distinctive wording, structure, data, or undisclosed ideas from successful examples into another applicant's draft.

Technical details

Version
1.0.0
License
MIT
Last updated
Aug 21, 2026
Published
Aug 21, 2026

Decision snapshot

Fallback candidate

63
Ready
Prototype
Stage

recent repository activity

Audit

Install review

Install and adoption review

77
Needs review
Security
79/100
Maintenance
100/100
Install
92/100
Open full auditView eval report

Agent-proven evidence

Agent-proven evidence

Outcome reports after resolve, review, install, and one narrow run.

0
Proven
Needs first agent runAuto-install: review firstLast: Unknown
Success rate
Recent failure
Outcomes
0
Output quality
Failed
0
Not relevant
0
Installs
0
Risk blocked
0
Setup needed
0
Production
0

No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.

Install

Add to agent workflow

Free and open source. Review the report before installing into production agents.

Growth loop

Share kit

X

Scenario-led draft for nsfc-benzi-audit, ready for a manual X post.

Curator note
nsfc-benzi-audit: Use when a user wants applicant-facing diagnosis and revision advice on a Chinese NSFC (国自然)...

46 stars

https://www.openagentskill.com/skills/jiankang1991-nsfc-benzi-audit?ref=x
Open X draft
Optional reply with install command
Listing + install path for nsfc-benzi-audit:
https://www.openagentskill.com/skills/jiankang1991-nsfc-benzi-audit?ref=x

Install: npx skills add jiankang1991/nsfc-benzi-audit --skill nsfc-benzi-audit

Listing source

Registry indexed

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This listing was indexed from public sources and is not marked official until a maintainer claim is approved.

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This Registry indexed listing is attributed to jiankang1991 but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.

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Author

J

jiankang1991

@jiankang1991

Platform fit

Health signals

GitHub stars
46
Quality score
35/100
Last GitHub push
Aug 21, 2026
Framework hints
Unknown
OpenAgentSkill views
3
Install copies
0
Outbound clicks
0

Community signal

Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.

Trust & safety

Sandbox only

63
  • GitHub adoption46 GitHub starsCHECK
  • Stars/forks activity46 stars, 5 forks; issue activity unavailable in current metadataCHECK
  • Recent maintenance1d since pushPASS
  • License clarityMITPASS
  • README/SKILL.md completenessMetadata includes enough usage and workflow contextPASS
  • Dependency/runtime riskno major dependency risk hints in public metadataPASS