Agent skill

Grant Mock Reviewer

by aipoch in aipoch/medical-research-skills

Simulate structured grant peer review for biomedical proposals; use when stress-testing significance, innovation, approach, feasibility, and reviewer-facing weaknesses before submission.

MITAuto-check passedResearch & Science

Install Grant Mock Reviewer

skills CLI
$ npx skills add aipoch/medical-research-skills --skill grant-mock-reviewer -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install aipoch/medical-research-skills grant-mock-reviewer --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/aipoch/medical-research-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/'scientific-skills/Academic Writing/grant-mock-reviewer' .claude/skills/grant-mock-reviewer && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
grant-mock-reviewer
GitHub stars
1.9k
Token cost
~3.5k tokens
SKILL.md length
1,545 words
Files
9 (incl. scripts, references)
Skills in repo
578
Repo updated
First seen
Licence
MIT

At a glance

Simulate structured grant peer review for biomedical proposals; use when stress-testing significance, innovation, approach, feasibility, and reviewer-facing weaknesses before submission.

  • Works in 6 steps: Score Summary → Strengths → Weaknesses → …
  • Stress-testing significance
  • SKILL.md covers Quick Check, Audit-Ready Commands, When to Use and Workflow, plus 9 more sections
  • Runs Python scripts from its folder; calls python

What it does

Grant Mock Reviewer is an agent skill from aipoch/medical-research-skills. Simulate structured grant peer review for biomedical proposals; use when stress-testing significance, innovation, approach, feasibility, and reviewer-facing weaknesses before submission.

Its SKILL.md is about 3.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 10 other files, including scripts and reference files (for example `POLISH_CHANGELOG.md`, `eval_report_grant-mock-reviewer_result.json` and `references/common_weaknesses_catalog.md`).

It sits in Research & Science, covering Proposals and quotes, Load testing and Peer review. The repository describes itself as: Hundreds of agent skills for medical research, including protocol design, data analysis, evidence insights, and academic writing. The licence is MIT.

When your agent uses it

  • Stress-testing significance
  • Reviewer-facing weaknesses before submission

Example prompts

  • “/grant-mock-reviewer”

Requirements

  • Python 3

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Score Summary
  2. Strengths
  3. Weaknesses
  4. Detailed Critique
  5. Summary Statement
  6. Revision Recommendations

What it can do on your machine

Read from SKILL.md and the folder at commit 686e09d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Grant Mock Reviewer loads about 3.5k tokens when it runs, and up to ~16k if it reads all its reference files. Until then it costs about 52 tokens; SKILL.md has 1,545 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~52
When it runs · the whole SKILL.md, loaded when a task matches
~3.5k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~16k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from aipoch/medical-research-skills at commit 686e09d, republished under its MIT licence (© aipoch). 1,545 words, ~3,547 tokens.

Download SKILL.mdSave it as .claude/skills/grant-mock-reviewer/SKILL.md (or your agent's skills folder). This skill also uses 8 other files; get the full folder from GitHub.
name
grant-mock-reviewer
description
Simulate structured grant peer review for biomedical proposals; use when stress-testing significance, innovation, approach, feasibility, and reviewer-facing weaknesses before submission.
license
MIT
author
AIPOCH

Source: https://github.com/aipoch/medical-research-skills

Grant Mock Reviewer

A simulated NIH study section reviewer that provides structured, rigorous critique of grant proposals using the official NIH scoring criteria and methodology.

Quick Check

Use this command to verify that the packaged script entry point can be parsed before deeper execution.

bash
python -m py_compile scripts/main.py

Audit-Ready Commands

Use these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths.

bash
python -m py_compile scripts/main.py
python scripts/main.py --help
python scripts/main.py -h
python scripts/main.py --help

When to Use

  • Use this skill when the task needs Simulates NIH study section peer review for grant proposals. Triggers.
  • Use this skill for academic writing tasks that require explicit assumptions, bounded scope, and a reproducible output format.
  • Use this skill when you need a documented fallback path for missing inputs, execution errors, or partial evidence.

Workflow

  1. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work.
  2. Validate that the request matches the documented scope and stop early if the task would require unsupported assumptions.
  3. Use the packaged script path or the documented reasoning path with only the inputs that are actually available.
  4. Return a structured result that separates assumptions, deliverables, risks, and unresolved items.
  5. If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion.

Capabilities

  1. NIH Scoring Rubric Application: Official 1-9 scale scoring across all 5 criteria
  2. Weakness Identification: Systematic detection of common proposal flaws
  3. Critique Generation: Structured written critiques for each review criterion
  4. Summary Statement: Complete mock Summary Statement output
  5. Revision Guidance: Prioritized, actionable recommendations for improvement

Usage

Command Line
text
# Full mock review with Summary Statement
python3 scripts/main.py --input proposal.pdf --format pdf --output review.md

# Review Specific Aims only
python3 scripts/main.py --input aims.pdf --section aims --output aims_review.md

# Targeted review (specific criterion focus)
python3 scripts/main.py --input proposal.pdf --focus approach --output approach_critique.md

# Generate NIH-style scores only
python3 scripts/main.py --input proposal.pdf --scores-only --output scores.json

# Compare before/after revision
python3 scripts/main.py --original original.pdf --revised revised.pdf --compare
As Library
python
from scripts.main import GrantMockReviewer

reviewer = GrantMockReviewer()
result = reviewer.review(
    proposal_text=proposal_content,
    grant_type="R01",
    section="full"
)
print(result.summary_statement)
print(result.scores)

Parameters

ParameterTypeDefaultRequiredDescription
--inputstring-YesPath to proposal file (PDF, DOCX, TXT, MD)
--formatstringautoNoInput file format (pdf, docx, txt, md)
--sectionstringfullNoSection to review (full, aims, significance, innovation, approach)
--grant-typestringR01NoGrant mechanism (R01, R21, R03, K99, F32)
--focusstring-NoFocus on specific criterion (significance, investigator, innovation, approach, environment)
--scores-onlyflagfalseNoOutput scores only (JSON)
--output, -ostringstdoutNoOutput file path
--originalstring-NoOriginal proposal for comparison
--revisedstring-NoRevised proposal for comparison
--compareflagfalseNoEnable comparison mode

NIH Scoring System

Overall Impact Score (1-9)

The single most important score reflecting the likelihood of the project to exert a sustained, powerful influence on the research field.

ScoreDescriptorLikelihood of Funding
1ExceptionalVery High
2OutstandingHigh
3ExcellentGood
4Very GoodModerate
5GoodLow-Moderate
6SatisfactoryLow
7FairVery Low
8MarginalUnlikely
9PoorNot Fundable
Individual Criteria (1-9 each)
  1. Significance: Does the project address an important problem? Will scientific knowledge be advanced?
  2. Investigator(s): Are the PIs well-suited? Adequate experience and training?
  3. Innovation: Does it challenge current paradigms? Novel concepts, approaches, methods?
  4. Approach: Sound research design? Appropriate methods? Adequate controls? Address pitfalls?
  5. Environment: Adequate institutional support? Scientific environment conducive to success?
Score Interpretation
  • 1-3 (High Priority): Compelling, well-developed proposals with strong approach
  • 4-5 (Medium Priority): Good proposals with some weaknesses
  • 6-9 (Low Priority): Significant weaknesses that diminish enthusiasm

Review Output Format

1. Score Summary
Overall Impact: [Score] - [Descriptor]

Criterion Scores:
- Significance: [Score]
- Investigator(s): [Score]
- Innovation: [Score]
- Approach: [Score]
- Environment: [Score]
2. Strengths

Bullet-point list of major strengths by criterion

3. Weaknesses

Bullet-point list of major weaknesses by criterion

4. Detailed Critique

Paragraph-form critique for each criterion following NIH style

5. Summary Statement

Complete narrative synthesis of the review

6. Revision Recommendations

Prioritized, actionable suggestions for improvement

Common Weaknesses Detected

Significance
  • Insufficient justification for the research problem
  • Incremental rather than transformative impact
  • Unclear connection to human health/disease
  • Overstatement of clinical significance without evidence
Investigator
  • Lack of relevant expertise for proposed aims
  • Insufficient track record in key methodologies
  • PI overcommitted (excessive effort on other grants)
  • Missing key collaborator expertise
Innovation
  • Straightforward extension of published work
  • Methods are standard rather than novel
  • No challenging of existing paradigms
  • Incremental rather than breakthrough potential
Approach
  • Aims too ambitious for timeframe
  • Insufficient preliminary data
  • Inadequate experimental controls
  • No discussion of pitfalls and alternatives
  • Statistical analysis plan missing or inadequate
  • Sample size/power calculations absent
Environment
  • Inadequate institutional resources
  • Missing core facility access
  • Lack of relevant equipment
  • Insufficient collaborative environment

Technical Difficulty

High - Requires deep understanding of NIH peer review processes, ability to apply standardized scoring rubrics consistently, and generation of clinically/scientifically accurate critique across diverse research domains.

Review Required: Human verification recommended before deployment in production settings.

References

  • references/nih_scoring_rubric.md - Complete NIH scoring guidelines
  • references/review_criteria_explained.md - Detailed criterion descriptions
  • references/common_weaknesses_catalog.md - Database of typical proposal flaws
  • references/summary_statement_templates.md - NIH-style statement templates
  • references/score_calibration_guide.md - Score assignment guidelines

Best Practices for Users

  1. Provide Complete Proposals: The tool works best with full Research Strategy sections
  2. Include Preliminary Data: Approach critique depends on feasibility evidence
  3. Review Multiple Times: Use iteratively as you revise
  4. Compare Versions: Track improvement between drafts
  5. Consider Multiple Perspectives: Supplement with human reviewer feedback

Limitations

  1. Cannot access external literature to verify claims
  2. May not capture domain-specific methodological nuances
  3. Scoring is simulated and may not match actual study section scores
  4. Best used as preparatory tool, not replacement for human review

Version

1.0.0 - Initial release with NIH R01/R21/R03 support

Risk Assessment

Risk IndicatorAssessmentLevel
Code ExecutionPython/R scripts executed locallyMedium
Network AccessNo external API callsLow
File System AccessRead input files, write output filesMedium
Instruction TamperingStandard prompt guidelinesLow
Data ExposureOutput files saved to workspaceLow

Security Checklist

  • No hardcoded credentials or API keys
  • No unauthorized file system access (../)
  • Output does not expose sensitive information
  • Prompt injection protections in place
  • Input file paths validated (no ../ traversal)
  • Output directory restricted to workspace
  • Script execution in sandboxed environment
  • Error messages sanitized (no stack traces exposed)
  • Dependencies audited
Show full SKILL.md (614 more words)Show less

Prerequisites

text
# Python dependencies
pip install -r requirements.txt

Evaluation Criteria

Success Metrics
  • Successfully executes main functionality
  • Output meets quality standards
  • Handles edge cases gracefully
  • Performance is acceptable
Test Cases
  1. Basic Functionality: Standard input → Expected output
  2. Edge Case: Invalid input → Graceful error handling
  3. Performance: Large dataset → Acceptable processing time

Lifecycle Status

  • Current Stage: Draft
  • Next Review Date: 2026-03-06
  • Known Issues: None
  • Planned Improvements:
    • Performance optimization
    • Additional feature support

Output Requirements

Every final response should make these items explicit when they are relevant:

  • Objective or requested deliverable
  • Inputs used and assumptions introduced
  • Workflow or decision path
  • Core result, recommendation, or artifact
  • Constraints, risks, caveats, or validation needs
  • Unresolved items and next-step checks

Error Handling

  • If required inputs are missing, state exactly which fields are missing and request only the minimum additional information.
  • If the task goes outside the documented scope, stop instead of guessing or silently widening the assignment.
  • If scripts/main.py fails, report the failure point, summarize what still can be completed safely, and provide a manual fallback.
  • Do not fabricate files, citations, data, search results, or execution outcomes.

Input Validation

This skill accepts requests that match the documented purpose of grant-mock-reviewer and include enough context to complete the workflow safely.

Do not continue the workflow when the request is out of scope, missing a critical input, or would require unsupported assumptions. Instead respond:

grant-mock-reviewer only handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill.

Response Template

Use the following fixed structure for non-trivial requests:

  1. Objective
  2. Inputs Received
  3. Assumptions
  4. Workflow
  5. Deliverable
  6. Risks and Limits
  7. Next Checks

If the request is simple, you may compress the structure, but still keep assumptions and limits explicit when they affect correctness.

When Not to Use

  • Do not proceed when required input files, identifiers, parameters, or context are missing — ask the user to provide them first.
  • Do not assume capabilities beyond this skill's declared scope when the user requests external operations or inferences.
  • Do not proceed without user confirmation when overwriting existing results, executing high-cost batch operations, or expanding task scope.

Required Inputs

FieldRequiredFormat/SourceExampleIf Missing
User task descriptionYesTextResearch question, writing goal, analysis objectiveStop and ask user to provide
Primary input materialDepends on taskText, file path, ID, table, or literaturePMID, PDF, CSV, DOCX, keywords, etc.Specify which material type is missing
Output preferenceNoTextLanguage, format, target journal, templateUse skill default format

Output Contract

  • Primary output: Structured result or target file aligned with this skill's objective.
  • Optional output: Intermediate check notes, issue list, supplementary suggestions, or generated file paths.
  • Format requirement: Unless the user specifies otherwise, prefer stable, reviewable Markdown or JSON; if the skill's bundled script requires a fixed format, use that format.
  • If partially complete: Must explicitly mark as PARTIAL and state which steps are completed and which remain.

Failure Handling

  • Missing critical input: Explicitly state which fields, files, or identifiers are missing and pause.
  • Script, template, or resource execution failure: Report the failing step, likely cause, and recovery suggestions — do not silently degrade.
  • Partial completion only: Return the verified portion first, then list remaining blockers and suggested next steps.

User Checkpoints

  • Before executing batch processing, overwriting files, long-running searches, or multi-stage generation, confirm scope and output format with the user.
  • Before proceeding when a key judgment is ambiguous, evidence is insufficient, or the workflow is entering the next stage, confirm with the user.

Quick Validation

  • Check that key scripts, templates, or reference file paths this skill depends on exist.
  • Check that the final output contains the core fields, sections, or files specified for this task.
  • Check that results clearly mark assumptions, limitations, and incomplete items.

© aipoch, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 8 other files (scripts, references) in scientific-skills/Academic Writing/grant-mock-reviewer of aipoch/medical-research-skills.

  • SKILL.md
  • POLISH_CHANGELOG.md
  • eval_report_grant-mock-reviewer_result.json
  • references/common_weaknesses_catalog.md
  • references/nih_scoring_rubric.md
  • references/review_criteria_explained.md
  • references/score_calibration_guide.md
  • references/summary_statement_templates.md
  • scripts/main.py

Open the folder on GitHubat commit 686e09d

Compare with similar skills

Grant Mock Reviewer next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Grant Mock Reviewer compared with similar skills
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Grant Mock Reviewer this skillaipoch/medical-research-skills1.9k—~3.5kAutomated safety check: PassMIT
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LLM Counciltenfoldmarc/llm-council-skill8231 repos~4.2kAutomated safety check: PassNone
Paper ReviewCamusGIT/EvoQuant1512 repos~2.6kAutomated safety check: PassApache-2.0
Scientific Thinking Scholar Evaluationaffaan-m/ECC277k1 repos~1.2kAutomated safety check: PassMIT
Paper ReviewEvoScientist/EvoSkills478—~4.5kAutomated safety check: PassApache-2.0

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Questions about Grant Mock Reviewer

What does Grant Mock Reviewer do?

Simulate structured grant peer review for biomedical proposals; use when stress-testing significance, innovation, approach, feasibility, and reviewer-facing weaknesses before submission. Grant Mock Reviewer is an agent skill from aipoch/medical-research-skills. Simulate structured grant peer review for biomedical proposals; use when stress-testing significance, innovation, approach, feasibility, and reviewer-facing weaknesses before submission.

When should I use Grant Mock Reviewer?

Grant Mock Reviewer fits situations like: stress-testing significance; reviewer-facing weaknesses before submission.

How do I install Grant Mock Reviewer in Claude Code?

Run `npx skills add aipoch/medical-research-skills --skill grant-mock-reviewer -a claude-code`. Or copy the skill folder (scientific-skills/Academic Writing/grant-mock-reviewer in aipoch/medical-research-skills) into .claude/skills/grant-mock-reviewer in your project. Claude Code loads it when a task matches its description.

How do I install Grant Mock Reviewer in Codex?

Run `npx skills add aipoch/medical-research-skills --skill grant-mock-reviewer -a codex`. Or copy the skill folder (scientific-skills/Academic Writing/grant-mock-reviewer in aipoch/medical-research-skills) into .agents/skills/grant-mock-reviewer in your project. Codex loads it when a task matches its description.

Can I use Grant Mock Reviewer in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add aipoch/medical-research-skills --skill grant-mock-reviewer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/grant-mock-reviewer, .gemini/skills/grant-mock-reviewer, .github/skills/grant-mock-reviewer and .opencode/skills/grant-mock-reviewer in your project.

What does Grant Mock Reviewer need to run?

Going by SKILL.md and its folder, Grant Mock Reviewer needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Grant Mock Reviewer access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Grant Mock Reviewer safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Grant Mock Reviewer use?

Grant Mock Reviewer is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Grant Mock Reviewer use?

About 3.5k tokens (SKILL.md is roughly 14k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 13k tokens, read only when the agent opens those files.

What are the alternatives to Grant Mock Reviewer?

Skills that share tags, products or a category with Grant Mock Reviewer: Peer Review (K-Dense-AI/claude-scientific-writer, 2.4k stars), LLM Council (tenfoldmarc/llm-council-skill, 823 stars), Paper Review (CamusGIT/EvoQuant, 151 stars) and Scientific Thinking Scholar Evaluation (affaan-m/ECC, 277k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Grant Mock Reviewer?

aipoch (a GitHub organization) maintains it in aipoch/medical-research-skills, which has 1,937 GitHub stars. The repository holds 578 skills in this directory. The repository was last updated on September 17, 2026.

Source: aipoch/medical-research-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.