nsfc-benzi-audit
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
Supply asset profile
Research and knowledge work
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
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
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
PromisingUseful candidate, but compare it with alternatives before adopting.
Trust
Sandbox onlyUseful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
Audit
Needs reviewA 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.
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.
Suited tasks
- Document processing workflows
- Claude Code teams
- builders willing to evaluate younger projects
- Read uploaded files
Suited agents
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-auditDo 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
Sparse or mixed signals. Useful for discovery, but not for autonomous installation.
Test manually in an isolated workspace and compare against safer alternatives.
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.
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-auditAgent 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 JSON
/api/agent/resolve?task=Use%20nsfc-benzi-audit%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20nsfc-benzi-audit%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/jiankang1991-nsfc-benzi-audit/install
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.
Install handoff
/api/skills/jiankang1991-nsfc-benzi-audit/install
LLM text format
/api/skills/jiankang1991-nsfc-benzi-audit/install?format=text
Find alternatives
/api/skills/search?q=nsfc-benzi-audit&limit=3
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-auditRegistry 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.
Manifest
/api/registry/manifest/jiankang1991-nsfc-benzi-audit
LLM text
/api/registry/manifest/jiankang1991-nsfc-benzi-audit?format=text
Install alias
/api/registry/install/jiankang1991-nsfc-benzi-audit
Recommend
/api/registry/recommend?task=Use%20nsfc-benzi-audit%20in%20an%20agent%20workflow&limit=3
Agent fit
Document processing
Use-case tags
Platforms
Claude Code
Audit report
Needs review · 77/100
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Fallback candidate for Document processing
Prototype with this skill first; keep a fallback candidate ready.
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
- 1Install it in a sandbox agent and run one Document processing task end to end.
- 2Compare output quality, latency, and failure behavior against at least one alternative.
- 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.
GitHub adoption
CHECK46 GitHub stars
Stars/forks activity
CHECK46 stars, 5 forks; issue activity unavailable in current metadata
Recent maintenance
PASS1d since push
License clarity
PASSMIT
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.
Workflow fit
Use this skill in these scenarios
Parse messy files
Document processing
I need my agent to read PDFs, extract tables, and turn documents into structured data.
Investigate faster
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Manage repositories
GitHub automation
I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.
Workflow fit
Add it to a complete workflow
Find, compare, and synthesize
Research report agent
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Ingest, retrieve, and cite
RAG knowledge base
A workflow for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.
Turn skills into distribution
Content growth agent
A workflow for turning newly indexed skills into SEO briefs, social drafts, comparison pages, and reusable publishing workflows.
Alternative shortlist
Compare before you install
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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
recent repository activity
Audit
Install review
Install and adoption review
- Security
- 79/100
- Maintenance
- 100/100
- Install
- 92/100
Agent-proven evidence
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
- 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
Scenario-led draft for nsfc-benzi-audit, ready for a manual X post.
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
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
This listing was indexed from public sources and is not marked official until a maintainer claim is approved.
- Creator
- jiankang1991
- Indexed by
- OpenAgentSkill community index
Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
Claim this skillOwner claim
Claim this skill listing
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.
Creator backlink kit
Add the evidence badges to your README
Show the canonical listing, current trust and audit signals, and real Agent-Proven evidence where developers evaluate the repository.
[](https://www.openagentskill.com/skills/jiankang1991-nsfc-benzi-audit)
[](https://www.openagentskill.com/skills/jiankang1991-nsfc-benzi-audit)
[](https://www.openagentskill.com/skills/jiankang1991-nsfc-benzi-audit/audit)
[](https://www.openagentskill.com/skills/jiankang1991-nsfc-benzi-audit)Author
jiankang1991
@jiankang1991
Tags
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
- 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
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