/review-grant
Run a 6-agent pre-submission panel review for a grant proposal targeting a specified funder or program
$ npx -y skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill review-grant --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/review-grant
Context preview
The summary Claude sees to decide when to auto-load this skill.
Run a 6-agent pre-submission panel review for a grant proposal targeting a specified funder or program
SKILL.md
review-grant.SKILL.mdname: 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 `$ARGUMENTS` as follows:
- The recognized target programs/funders are:
- **US federal science and health**: `NSF`, `NIH`
- **International research funders**: `ERC`, `HorizonEurope`
- **General proposal standards**: `major-funder`, `foundation`
- (case-insensitive; users can add further programs or funders by editing this list in the skill file)
- If the first token of `$ARGUMENTS` matches 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 `$ARGUMENTS` as a file path and set the target program/funder to `major-funder` (meaning the review applies high general standards without a specific sponsor persona).
- If `$ARGUMENTS` is empty, set both to their defaults: no file path (auto-detect) and target program/funder `major-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:
1. 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). 2. 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`, or `application`. 3. 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. 4. 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. 5. 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`
6. 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:**
1. **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.
2. **Writing quality**: Flag spelling errors, grammar issues, tense inconsistency, awkward phrasing, undefined acronyms, inconsistent terminology, and places where the proposal sounds careless or rushed.
3. **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
4. **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?"
5. **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
6. **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 for
Read more
name: 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 `$ARGUMENTS` as follows:
- The recognized target programs/funders are:
- **US federal science and health**: `NSF`, `NIH`
- **International research funders**: `ERC`, `HorizonEurope`
- **General proposal standards**: `major-funder`, `foundation`
- (case-insensitive; users can add further programs or funders by editing this list in the skill file)
- If the first token of `$ARGUMENTS` matches 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 `$ARGUMENTS` as a file path and set the target program/funder to `major-funder` (meaning the review applies high general standards without a specific sponsor persona).
- If `$ARGUMENTS` is empty, set both to their defaults: no file path (auto-detect) and target program/funder `major-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:
1. 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). 2. 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`, or `application`. 3. 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. 4. 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. 5. 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`
6. 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:**
1. **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.
2. **Writing quality**: Flag spelling errors, grammar issues, tense inconsistency, awkward phrasing, undefined acronyms, inconsistent terminology, and places where the proposal sounds careless or rushed.
3. **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
4. **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?"
5. **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
6. **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 for
📌 文档结构(2026-07-22 起): 本文件是中文默认入口 —— banner + badges + 信任面 + 9 阶段流水线速览 + 76 行合集总表。 每个合集的完整描述、按用途分组、精确数字、验证方法在 docs/CONTENT_ZH.md(扩展正文,总表行内的 → 直接跳转到对应锚点)。 English version: README-en.md · 中文扩展正文:docs/CONTENT_ZH.md · README-zh-CN.md 已弃用(重定向占位) 🌐 语言: English |
Other skills on auto-empirical-research-skills.
- /pipeline
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest + rdrobust + econml + causalml + matplotlib/seaborn. **Defaults to economics empirical-paper style** (AER / QJE / AEJ) —
Open skill - /pipeline
Classical end-to-end empirical analysis workflow in the modern tidyverse + econometrics R ecosystem — dplyr + tidyr + haven + fixest + sandwich + lmtest + clubSandwich + AER + ivreg + did + bacondecomp + HonestDiD + eventstudyr + rdrobust + rddensity + Synth + gsynth + synthdid
Open skill - /pipeline
Classical end-to-end empirical analysis workflow in the traditional Stata ecosystem — native Stata + reghdfe + ivreg2 + csdid + did_imputation + eventstudyinteract + sdid + rdrobust + rddensity + synth + synth_runner + psmatch2 + teffects + ebalance + coefplot + esttab + asdoc +
Open skill - /00-Full-empirical-analysis-skill_StatsPAI
Use when the user asks to run a full empirical / causal analysis in Python — by default in the style of an applied economics paper (AER / QJE / JPE / ReStud / AEJ) with DID / RD / IV / SCM / DML / matching, written-out estimating equation + identifying assumption, Table 1 /
Open skill - /00.1-Full-empirical-analysis-skill_Python
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest + rdrobust + econml + causalml + matplotlib/seaborn. **Defaults to economics empirical-paper style** (AER / QJE / AEJ) —
Open skill - /00.2-Full-empirical-analysis-skill_Stata
Classical end-to-end empirical analysis workflow in the traditional Stata ecosystem — native Stata + reghdfe + ivreg2 + csdid + did_imputation + eventstudyinteract + sdid + rdrobust + rddensity + synth + synth_runner + psmatch2 + teffects + ebalance + coefplot + esttab + asdoc +
Open skill

