review-grant
Run a 6-agent pre-submission panel review for a grant proposal targeting a specified funder or program
Install / Use
npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill review-grantInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
Education & ResearchSupported Platforms
Our assessment of review-grant
review-grant scores 92/100 on our quality scale, 47th of 212 Education & Research skills we index (top 23%).
Its SKILL.md is 20 KB long, well organised into 50 sections with 7 code examples: a thorough specification that gives an agent plenty to work with.
With 4,360 GitHub stars, it is one of the more widely adopted skills in the catalogue.
Maintenance, license and trust
- The repository was last updated 3 days ago, so review-grant is actively maintained.
- No license is declared. By default that means all rights are reserved: you can read it, but reusing or redistributing it is not clearly permitted. Ask the author before building on it commercially.
- Its trust signals score 88/100, with 1 caution from licensing, adoption, age or documentation. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
Safety scan
No issues foundOur scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands.
Automated pattern scan on 2026-09-27. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.
review-grant compared with similar skills
All 4 of these similar skills score higher than review-grant; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| review-grant (this skill)by brycewang-stanford | 92 | 4.4k | 3d ago | SKILL.md |
| last30days-skillby mvanhorn | 100 | 62.9k | 4d ago | CLAUDE.md |
| algorithmic-artby anthropics | 100 | 177.9k | 4d ago | SKILL.md |
| pptxby anthropics | 100 | 177.9k | 4d ago | SKILL.md |
| designby nextlevelbuilder | 100 | 130.2k | 5d ago | SKILL.md |
Frequently asked questions
- How do I install review-grant?
- Run
npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill review-grant. The install tabs above show the steps for each supported agent. - Which AI agents does review-grant work with?
- It is written for Zed, as a SKILL.md file. Other agents that read the same format can often use it too.
- Is review-grant safe to use?
- Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. It declares no license and scores 88/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is review-grant still maintained?
- The repository was last updated 3 days ago, so review-grant is actively maintained.
Skill content
View source on GitHubname: review-grant description: Run a 6-agent pre-submission panel review for a grant proposal targeting a specified funder or program
You are coordinating a rigorous pre-submission review of a grant proposal. You will run 6 specialized review agents in parallel and consolidate their findings into a structured report.
Phase 1: Parse Arguments and Discover the Proposal
Parse $ARGUMENTS as follows:
- The recognized target programs/funders are:
- US federal science and health:
NSF,NIH - International research funders:
ERC,HorizonEurope - General proposal standards:
major-funder,foundation - (case-insensitive; users can add further programs or funders by editing this list in the skill file)
- US federal science and health:
- If the first token of
$ARGUMENTSmatches one of these names, treat it as the target program/funder and treat any remaining text as the main proposal file path. - If no token matches one of these names, treat the entire
$ARGUMENTSas a file path and set the target program/funder tomajor-funder(meaning the review applies high general standards without a specific sponsor persona). - If
$ARGUMENTSis empty, set both to their defaults: no file path (auto-detect) and target program/fundermajor-funder.
Store the resolved target program/funder as TARGET_PROGRAM for use in Agent 6 and the report header.
If a file path was provided, use it as the main proposal file. Otherwise, auto-detect:
- Search the current directory recursively for likely proposal files with common extensions:
*.md,*.txt,*.tex,*.docx,*.pdf(exclude hidden folders,.git, build output, and dependency directories). - Prioritize files whose names suggest they are the main narrative, such as those containing
proposal,project-description,research-plan,specific-aims,narrative,case-for-support, orapplication. - Identify the main proposal document: the file that appears to contain the core project narrative rather than only a budget, CV, biosketch, appendix, or letter. If more than one file looks plausible, prefer the one with the clearest summary/abstract and the most complete proposal sections.
- Read the main proposal file and identify references to supporting documents, appendices, attachments, supplementary materials, budget files, timeline files, biosketches/CVs, facilities/resources statements, data-management plans, mentoring plans, or letters of support.
- Search recursively for common supporting files and record them if present:
- Budget and justification: files containing
budget,justification - Timeline and workplan: files containing
timeline,gant,gantt,milestone,workplan - Personnel documents: files containing
biosketch,cv,resume,personnel,team - Compliance/supporting plans: files containing
data-management,data sharing,management plan,mentoring,facilities,resources,support letter,letter - Appendices and supplements: files containing
appendix,supplement,supplementary
- Budget and justification: files containing
- Record:
- Full path of the main proposal file
- Full path of each supporting file and its likely role
- Proposal title, PI(s)/team, abstract/summary if available
- Any explicit funding call, solicitation, or sponsor named in the materials
If the proposal is in a binary format such as .pdf or .docx and the environment cannot read it directly, review what is accessible and explicitly note the limitation in the final report.
Phase 2: Launch 6 Review Agents in Parallel
In a single message, launch all 6 agents using the Agent tool with subagent_type: "general-purpose". Each agent reads the proposal materials independently. Pass the complete list of proposal and supporting file paths to each agent in its prompt. When constructing Agent 6's prompt, substitute the actual resolved value of TARGET_PROGRAM for every occurrence of TARGET_PROGRAM in that agent's prompt text.
AGENT 1 — Clarity, Writing Quality & Compliance Signals
You are a grant editor reviewing the proposal for clarity, professionalism, and compliance with common proposal-writing expectations. Read all accessible proposal files and focus on the actual prose rather than markup or formatting commands.
What to check:
-
Clarity and readability: Identify sentences and paragraphs that are hard to follow, overloaded with jargon, too abstract, or too dense for a panel reviewer reading quickly.
-
Writing quality: Flag spelling errors, grammar issues, tense inconsistency, awkward phrasing, undefined acronyms, inconsistent terminology, and places where the proposal sounds careless or rushed.
-
Structure and signposting: Check whether the proposal clearly states:
- the problem
- why it matters
- the core aims or objectives
- the approach
- expected outputs or outcomes
- why this team can do it
-
Reviewer-orientation problems: Flag any place where a busy reviewer would ask:
- "What exactly is the project trying to do?"
- "Why is this important?"
- "What is new here?"
- "What will be delivered, and when?"
-
Compliance signals: Check for common proposal-writing failures that create noncompliance risk even when rules are not fully provided:
- missing project summary or abstract-like overview
- unclear aims/objectives
- no explicit deliverables
- no timeline cues
- no evaluation or success criteria
- vague dissemination or broader-impact language when expected
-
Tone and style: Flag hype, overstatement, empty buzzwords, and generic claims such as "transformative," "groundbreaking," or "highly innovative" when unsupported by specifics.
Output format:
## Agent 1: Clarity, Writing Quality & Compliance Signals
### Critical Writing or Clarity Issues
[numbered list: Location | Problematic text or section | Why it hurts the proposal | Suggested correction]
### Minor Writing Issues
[numbered list: same format]
### Structural or Compliance Signals to Fix
[numbered list: Missing or weak element | Where it should appear | Recommended remedy]
The proposal files to review are: [LIST ALL FILE PATHS HERE]
AGENT 2 — Internal Consistency, Scope & Deliverables
You are a technical reviewer checking whether the proposal is internally coherent and operationally consistent. Read all accessible proposal files and verify that the project description, aims, methods, timeline, personnel plan, and budget story align.
What to check:
-
Aims vs. methods consistency: For each stated aim or objective, verify that the methods section actually explains how that aim will be achieved.
-
Aims vs. deliverables consistency: Check whether every major promised output, deliverable, dataset, prototype, publication, or policy product is traceable to a concrete work package or task.
-
Timeline consistency: If phases, milestones, or years are named in different places, verify that they match. Flag contradictions across narrative, timeline, budget justification, and appendices.
-
Personnel consistency: Do the named investigators, collaborators, staff roles, and external partners match across the narrative, biosketches/CVs, management plan, and budget story?
-
Budget-story consistency: If the proposal requests resources for a task, is that task actually described in the narrative? Conversely, are there major activities in the narrative that appear under-resourced or unsupported?
-
Terminology consistency: Identify every key project term, work package, intervention, dataset, target population, or evaluation metric and flag drift in naming or meaning.
-
Claim consistency across sections: Check whether the abstract/summary, significance section, research plan, management plan, and conclusion describe the same project at the same level of ambition.
-
External references and attachments: Flag cases where the proposal says "see attached", "as shown in the budget/timeline/letter", or similar, but the referenced material is missing or does not appear to support the claim.
Output format:
## Agent 2: Internal Consistency, Scope & Deliverables
### Critical Inconsistencies
[numbered list: [Location 1] ↔ [Location 2] | What conflicts | Why it matters]
### Deliverable or Scope Gaps
[numbered list: Aim/deliverable | Missing operational support | Recommended fix]
### Terminology Drift
[numbered list: Term | How it varies | Recommended standardization]
### Minor Inconsistencies
[numbered list: same format as Critical]
The proposal files to review are: [LIST ALL FILE PATHS HERE]
AGENT 3 — Significance, Innovation & Fit to the Call
You are a skeptical panel reviewer evaluating whether the proposal addresses an important problem, offers a credible level of novelty, and fits the likely sponsor or solicitation.
What to check:
-
Problem significance: Does the proposal explain why the problem matters now, to whom it matters, and what is at stake if the problem is not addressed?
-
Innovation claims: Flag every place where the proposal claims novelty, first-mover status, uniqueness, or transformative potential without making clear what is actually new.
-
Fit to sponsor or call: Based on the accessible materials and the named
TARGET_PROGRAM, assess whether the project seems aligned with likely review criteria, scope, mission, and audience. Flag mission drift or weak fit. -
Value proposition: Does the proposal clearly explain why this project deserves funding rather than simply being interesting or worthwhile in the abstract?
-
Broader impacts / translational / public value claims: Check whether claims about impact, policy relevance, clinical relevance, social benefit, or broader impacts are concrete and plausible rather than generic.
-
Competitive positioning: Would a reviewer understand why this proposal stands out from other plausible applications in the same space? If not, identify what is missing.
-
Overclaiming and underclaiming:
- Overclaiming: Claims of importance or novelty that exceed the evidence presented
- Underclaiming: Strong aspects of the proposal that are not framed sharply enough to help in review
Output format:
## Agent 3: Significance, Innovation & Fit to the Call
### Major Fit or Significance Problems
[numbered list: Location | Issue | Why it weakens competitiveness | Fix]
### Innovation Overclaiming
[numbered list: Quoted or paraphrased claim | Why it overreaches | Better framing]
### Underused Strengths
[numbered list: Strength | Where it should be emphasized | Suggested framing]
### Minor Positioning Issues
[numbered list: same format]
The proposal files to review are: [LIST ALL FILE PATHS HERE]
AGENT 4 — Research Design, Methods & Feasibility
You are a demanding methodological reviewer assessing whether the proposed work is technically sound and realistically executable within the proposed project period.
What to check:
-
Methodological adequacy: For each aim or work package, does the proposal specify a method that is adequate to answer the stated question or achieve the stated objective?
-
Feasibility: Are the timeline, staffing, data access, recruitment plan, partnerships, computation, infrastructure, and regulatory assumptions realistic?
-
Risk identification and mitigation: Does the proposal identify the main technical, logistical, data, recruitment, regulatory, or dependency risks? Are fallback plans credible?
-
Evaluation plan: If the proposal promises outputs, interventions, tools, pilots, or impact, does it specify how success will be measured?
-
Sampling, data, and evidence plan: Where relevant, assess whether the proposal adequately explains:
- data sources or materials
- sample or participant selection
- power or scale logic
- analytic strategy
- validation or quality-control proced
Truncated for display — read the full file on GitHub.
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Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
