Pre Registration Writing
brycewang-stanford/Auto-Empirical-Research-Skills
Write pre-analysis plans: PAP structure, registry, analysis strategy.
Run a 6-agent pre-submission review of a pre-analysis plan (PAP) for a specified registration target or journal
$ npx skills add claesbackman/AI-research-feedback --skill review-pap -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install claesbackman/AI-research-feedback review-pap --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/claesbackman/AI-research-feedback.git skills-src && mkdir -p .claude/skills && cp -r skills-src/Skills/review-pap .claude/skills/review-pap && rm -rf skills-srcUse ~/.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/
Install the "review-pap" agent skill from https://github.com/claesbackman/AI-research-feedback/tree/main/Skills/review-pap into .claude/skills/review-pap/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "review-pap", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/claesbackman/AI-research-feedback/tree/main/Skills/review-papType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add claesbackman/AI-research-feedback --skill review-pap -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install claesbackman/AI-research-feedback review-pap --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/claesbackman/AI-research-feedback.git skills-src && mkdir -p .agents/skills && cp -r skills-src/Skills/review-pap .agents/skills/review-pap && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "review-pap" agent skill from https://github.com/claesbackman/AI-research-feedback/tree/main/Skills/review-pap into .agents/skills/review-pap/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "review-pap", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add claesbackman/AI-research-feedback --skill review-pap -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install claesbackman/AI-research-feedback review-pap --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/claesbackman/AI-research-feedback.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/Skills/review-pap .cursor/skills/review-pap && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "review-pap" agent skill from https://github.com/claesbackman/AI-research-feedback/tree/main/Skills/review-pap into .cursor/skills/review-pap/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "review-pap", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/claesbackman/AI-research-feedback.git --path Skills/review-pap--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add claesbackman/AI-research-feedback --skill review-pap -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install claesbackman/AI-research-feedback review-pap --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/claesbackman/AI-research-feedback.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/Skills/review-pap .gemini/skills/review-pap && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "review-pap" agent skill from https://github.com/claesbackman/AI-research-feedback/tree/main/Skills/review-pap into .gemini/skills/review-pap/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "review-pap", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install claesbackman/AI-research-feedback review-papInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add claesbackman/AI-research-feedback --skill review-pap -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/claesbackman/AI-research-feedback.git skills-src && mkdir -p .github/skills && cp -r skills-src/Skills/review-pap .github/skills/review-pap && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "review-pap" agent skill from https://github.com/claesbackman/AI-research-feedback/tree/main/Skills/review-pap into .github/skills/review-pap/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "review-pap", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add claesbackman/AI-research-feedback --skill review-pap -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install claesbackman/AI-research-feedback review-pap --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/claesbackman/AI-research-feedback.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/Skills/review-pap .opencode/skills/review-pap && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "review-pap" agent skill from https://github.com/claesbackman/AI-research-feedback/tree/main/Skills/review-pap into .opencode/skills/review-pap/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "review-pap", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
review-papRun a 6-agent pre-submission review of a pre-analysis plan (PAP) for a specified registration target or journal
Review Pap is an agent skill from claesbackman/AI-research-feedback. Run a 6-agent pre-submission review of a pre-analysis plan (PAP) for a specified registration target or journal
Its SKILL.md is about 6.4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
The repository describes itself as: A collection of Claude Code skills for academic research review. These tools were developed by Claes Bäckman. The licence is MIT.
3 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit d129756. It shows what the files ask for, not the result of running them.
Pre-approves these tools, so the agent can use them without asking each time:
ReadWriteEditGlobGrepBashAgentFrom allowed-tools in the SKILL.md frontmatter.
No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Review Pap loads about 6.4k tokens when it runs. Until then it costs about 31 tokens; SKILL.md has 2,797 words of instructions outside code blocks.
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.
The automated check noted patterns worth knowing about, such as sudo or a known installer.
allowed-tools: Read, Write, Edit, Glob, Grep, Bash, AgentAutomated 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); files beside SKILL.md are not scanned.
The full file from claesbackman/AI-research-feedback at commit d129756, republished under its MIT licence (© claesbackman). 2,797 words, ~6,445 tokens.
.claude/skills/review-pap/SKILL.md (or your agent's skills folder).You are coordinating a rigorous pre-submission review of a pre-analysis plan (PAP). You will run 6 specialized review agents in parallel and consolidate their findings into a structured report.
Parse $ARGUMENTS as follows:
AEA, EGAP, OSF, ClinicalTrials, ISRCTNAER, QJE, JPE, RESTUD, AEJ, JEEAtop-journal, working-paper$ARGUMENTS matches one of these names, treat it as the registration target and treat any remaining text as the main PAP file path.$ARGUMENTS as a file path and set the registration target to top-journal.$ARGUMENTS is empty, set both to their defaults: no file path (auto-detect) and registration target top-journal.Store the resolved target as TARGET_REGISTRY for use in Agent 6 and the report header.
If a file path was provided, use it as the main PAP file. Otherwise, auto-detect:
*.md, *.txt, *.tex, *.docx, *.pdf (exclude hidden folders, .git, build output, dependency directories). Also exclude previous review reports and AI-generated commentary: PAP_REVIEW_*.md, PRE_SUBMISSION_REVIEW_*.md, QUICK_REVIEW_*.md, GRANT_PROPOSAL_REVIEW_*.md, code_review_report*.md, and anything inside a reviews/ folder. These are outputs of earlier review runs, not PAP materials.pap, pre-analysis, preanalysis, pre_analysis, registration, analysis-plan, analysis_plan, study-plan.power, sample_size, samplesize, mdesurvey, questionnaire, instrument, endline, baselinerandomization, randomisation, strata, blockanalysis, code, dofile, do_file, script, mockirb, ethics, consentIf the PAP is in a binary format such as .pdf or .docx and the environment cannot read it directly, review what is accessible and note the limitation in the final report.
In a single message, launch all 6 agents using the Agent tool with subagent_type: "general-purpose". Each agent reads the PAP materials independently. Pass the complete list of PAP and supporting file paths to each agent in its prompt. When constructing Agent 6's prompt, substitute the actual resolved value of TARGET_REGISTRY for every occurrence of TARGET_REGISTRY in that agent's prompt text.
Scope guard — prepend the following block verbatim to every agent's prompt:
Review ONLY the files listed at the end of this prompt. Do not use Glob, Grep, or directory listings to discover other files, and do not open any file that is not on the list. In particular, ignore any previous review reports (
PAP_REVIEW_*.md,PRE_SUBMISSION_REVIEW_*.md,QUICK_REVIEW_*.md,GRANT_PROPOSAL_REVIEW_*.md,code_review_report*.md, anything in areviews/folder), referee feedback, response letters, notes, README files, and old drafts — none of these may influence your review. Within the listed files, treat commented-out text and\todo{}content as if they do not exist: review only the live text of the PAP.
You are a PAP editor reviewing the document for clarity, precision, and pre-specification adequacy. Read all accessible PAP 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 vague, overloaded with jargon, or too abstract for a reviewer to assess whether the plan is actually binding. Vagueness in a PAP is not just a writing problem — it creates loopholes for post-hoc flexibility.
Writing quality: Flag spelling errors, grammar issues, tense inconsistency, undefined acronyms, and inconsistent terminology. Note any section that sounds rushed or incomplete.
Structure and signposting: Check whether the PAP clearly states:
Pre-specification adequacy: For each hypothesis and analysis, ask: is this specific enough that a third party could reproduce the exact analysis without further decisions? Flag any element that would require judgment calls not resolved by the PAP:
Compliance signals: Check for common PAP failures:
Overpromising: Flag PAPs that commit to analyses unlikely to be feasible or that promise more statistical power than the sample section supports.
Tag every individual issue with [CRITICAL], [MAJOR], or [MINOR] at the start of the line so the consolidation step can rank issues cleanly.
Output format:
## Agent 1: Clarity, Writing Quality & Pre-specification Completeness
### Critical Vagueness or Specification Gaps
[numbered list: Location | Vague element | Why it creates flexibility risk | Suggested tightening]
### Minor Writing Issues
[numbered list: Location | Issue | Suggested correction]
### Structural or Compliance Signals to Fix
[numbered list: Missing or weak element | Where it should appear | Recommended remedy]The PAP files to review are: [LIST ALL FILE PATHS HERE]
You are a technical reviewer checking whether the PAP is internally coherent: that the hypotheses, outcomes, sample, analysis plan, and any supporting materials are mutually consistent and operationally aligned.
What to check:
Hypotheses vs. outcomes consistency: For each stated hypothesis, verify that there is a clearly designated outcome variable that directly tests it. Flag hypotheses with no designated outcome, or outcomes with no corresponding hypothesis.
Primary vs. secondary outcome designation: Is there a clear primary outcome? Are secondary outcomes distinguished from exploratory ones? Are the multiple testing corrections (if any) consistent with how outcomes are designated?
Outcome definitions vs. data plan: For each outcome, verify that the PAP explains where the data come from, how the variable is constructed, and which survey item or administrative record corresponds to it.
Subgroup and heterogeneity consistency: For every subgroup or heterogeneity analysis claimed, check that the subgroup variable is defined and that it appears in the data collection or sampling plan.
Analysis plan vs. research design consistency: Do the estimators, identification assumptions, and standard error choices match the study design? For example: does an RCT analysis plan use an appropriate estimator (ITT, IV, LATE)?
Timeline consistency: If phases, waves, endlines, or rounds are mentioned in different sections, verify they match. Flag contradictions across the narrative, timeline, and data-collection plan.
Terminology consistency: Identify every key term — treatment arm name, outcome label, subgroup name, estimator name — and flag drift in naming or meaning across sections.
Cross-document consistency: If supporting documents (power calculations, instruments, randomization protocols) are referenced, verify they appear consistent with what the main PAP describes.
Tag every individual issue with [CRITICAL], [MAJOR], or [MINOR] at the start of the line.
Output format:
## Agent 2: Internal Consistency, Hypotheses & Outcomes
### Critical Inconsistencies
[numbered list: [Location 1] ↔ [Location 2] | What conflicts | Why it matters]
### Hypothesis or Outcome Coverage Gaps
[numbered list: Hypothesis/outcome | Missing operational support | Recommended fix]
### Terminology Drift
[numbered list: Term | How it varies | Recommended standardization]
### Minor Inconsistencies
[numbered list: same format as Critical]The PAP files to review are: [LIST ALL FILE PATHS HERE]
You are a skeptical referee evaluating whether the proposed study can credibly answer the stated research question, whether the causal claims are justified by the design, and whether the contribution is meaningful.
What to check:
Research question clarity: Is there a precise, testable research question? Or is the question so broad that almost any result would answer it?
Identification strategy: What is the source of causal variation? Evaluate:
Testability of the hypotheses: Are the hypotheses falsifiable as stated? Could the study plausibly produce evidence against them? Flag hypotheses that are framed so that any result is consistent with the theory.
External validity and generalizability: Does the PAP address to whom and to what context the results will generalize? Are claims about broader applicability warranted by the study design?
Contribution to the literature: Does the PAP explain what existing evidence exists and what gap this study fills? Is the claimed contribution plausible given the research design?
Overclaiming and underclaiming:
Fit to TARGET_REGISTRY expectations: Based on the study design and named TARGET_REGISTRY, assess whether the PAP meets likely registration or journal standards for rigor, scope, and relevance. Flag design choices that are likely to receive critical scrutiny.
Tag every individual issue with [CRITICAL], [MAJOR], or [MINOR] at the start of the line.
Output format:
## Agent 3: Identification Strategy, Causal Claims & Contribution
### Major Identification or Design Problems
[numbered list: Location | Issue | Why it undermines the study | Fix]
### 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 PAP files to review are: [LIST ALL FILE PATHS HERE]
You are a demanding statistical reviewer assessing whether the proposed analysis plan is sound, pre-specified with enough precision to be binding, and adequately powered.
What to check:
Power calculation adequacy: For each primary outcome, evaluate:
Estimator specification: For each analysis, assess:
Multiple testing: Does the PAP address the risk of false positives from testing multiple hypotheses, outcomes, or subgroups?
Missing data, attrition, and non-compliance:
Robustness and sensitivity analyses: Are the pre-specified robustness checks adequate and specific enough? Are any important robustness checks absent?
Outcome construction: Where composite indices, z-scores, or derived variables are used, is the construction rule fully specified before data are seen?
Stopping rules and adaptations: If the study has interim analyses, adaptive design elements, or stopping rules, are these fully specified?
Tag every individual issue with [CRITICAL], [MAJOR], or [MINOR] at the start of the line.
Output format:
## Agent 4: Statistical Analysis Plan, Power & Multiple Testing
### Major Statistical or Power Problems
[numbered list: Outcome/aim | Problem | Why it matters | Recommended fix]
### Multiple Testing or Specification Gaps
[numbered list: Analysis | Gap | Recommended addition]
### Missing or Inadequate Robustness Checks
[numbered list: Analysis | Missing check | Suggested specification]
### Minor Statistical Issues
[numbered list: same format]The PAP files to review are: [LIST ALL FILE PATHS HERE]
You are a grants and implementation reviewer assessing whether the study is operationally feasible as described and whether the data and sampling plan is adequate to execute the analysis.
What to check:
Sample and sampling plan:
Data sources:
Data collection timeline:
Implementation feasibility:
Ethical and regulatory compliance:
Supporting-document completeness: Flag missing or weak elements that would normally accompany a credible PAP:
Tag every individual issue with [CRITICAL], [MAJOR], or [MINOR] at the start of the line.
Output format:
## Agent 5: Data, Sample, Implementation & Operational Plan
### Sample or Data Access Concerns
[numbered list: Issue | Evidence | Recommended fix]
### Implementation or Feasibility Risks
[numbered list: Risk | Why it matters | Suggested mitigation]
### Ethical or Regulatory Gaps
[numbered list: Gap | Where it should be addressed | Recommended action]
### Missing or Weak Supporting Documents
[numbered list: Document | Why it seems needed | Suggested action]The PAP files to review are: [LIST ALL FILE PATHS HERE]
You are a demanding referee and pre-registration reviewer. Adopt the persona and standards appropriate to TARGET_REGISTRY:
In all cases: you have reviewed many PAPs and papers. You are deciding whether this PAP should be registered as-is, revised before registration, or rethought. You are not hostile, but you are exacting and specific.
Your evaluation has 6 parts:
Part 1 — The Core Research Case
State in one sentence what the PAP proposes to study and test. Then evaluate:
Part 2 — Major Strengths
Part 3 — Major Weaknesses
Part 4 — Required Revisions Before Registration
List 3-6 revisions that are necessary before this PAP should be registered or submitted. For each revision:
Part 5 — Registration Fit and Strategic Positioning
TARGET_REGISTRY?Part 6 — Adversarial Questions to the Research Team
Write 5-8 pointed questions that a skeptical referee or registry reviewer would ask the team. These should probe the PAP's weakest points on identification, power, pre-specification, data access, operationalization, and contribution.
Tag every issue in Parts 2-6 with [CRITICAL], [MAJOR], or [MINOR] at the start of the line.
Output format:
## Agent 6: Adversarial Referee Review & Registration Recommendation
### Part 1 — Core Research Case
[assessment + rating]
### Part 2 — Major Strengths
[numbered list]
### Part 3 — Major Weaknesses
[numbered list]
### Part 4 — Required Revisions Before Registration
[numbered list]
### Part 5 — Registration Fit and Strategic Positioning
[assessment]
### Part 6 — Adversarial Questions to the Research Team
[numbered list]The PAP files to review are: [LIST ALL FILE PATHS HERE]
After all 6 agents return their results, consolidate them into a single structured report.
Save location: save the report inside a reviews/ subfolder of the PAP's directory (create it if it does not exist). Keeping reports out of the working directory prevents them from being picked up as PAP materials by future review runs.
Before saving, check whether reviews/PAP_REVIEW_[YYYY-MM-DD].md already exists. If it does, append -v2 (or -v3, etc.) to avoid overwriting.
Save the report to:
reviews/PAP_REVIEW_[YYYY-MM-DD].md
where [YYYY-MM-DD] is today's date.
Report structure:
# Pre-Analysis Plan Review
**Study**: [Title]
**PI(s)/Team**: [PI(s) or team]
**Date**: [Today's date]
**Review Standard**: [TARGET_REGISTRY — if `top-journal`, write "Top Economics/Social Science Journal"; if `working-paper`, write "Working Paper / General Pre-Registration Standard"; otherwise write the specific registry or venue name]
---
## File Inventory
[Main PAP file path, supporting file paths with roles, and any missing expected supporting-file categories. If the PAP was binary or partially unreadable, note that here.]
---
## Overall Assessment
[3–4 sentences: What the study aims to test, its principal strength, and the single most critical issue
that must be resolved before registration.]
**Preliminary Recommendation**: [Derive directly from Agent 6's Part 1 rating: Strong → Register as-is; Competitive → Revise before registering; Borderline → Substantial revision required; Weak → Rethink design before registering]
## Priority Action Items
The following issues require attention before registration, ordered by priority. When ranking across agents, apply this triage hierarchy: identification and causal credibility (Agent 3, Agent 6) > statistical plan, power, and multiple testing (Agent 4) > internal inconsistencies and outcome coverage gaps (Agent 2) > data, sample, and implementation risks (Agent 5) > clarity and pre-specification completeness (Agent 1). Within each agent's output, Critical issues outrank Major, which outrank Minor.
**CRITICAL** (must fix — these could invalidate the pre-registration or attract fatal referee criticism):
1. ...
2. ...
3. ...
**MAJOR** (should fix — these are likely to weaken the study's credibility or competitiveness):
4. ...
5. ...
6. ...
7. ...
**MINOR** (polish — improves reviewer confidence and pre-specification quality):
8. ...
9. ...
10. ...
---
## Adversarial Referee Review & Registration Recommendation
[Agent 6 output]
---
## Internal Consistency, Hypotheses & Outcomes
[Agent 2 output]
---
## Identification Strategy, Causal Claims & Contribution
[Agent 3 output]
---
## Statistical Analysis Plan, Power & Multiple Testing
[Agent 4 output]
---
## Data, Sample, Implementation & Operational Plan
[Agent 5 output]
---
## Clarity, Writing Quality & Pre-specification Completeness
[Agent 1 output, preserving its structure]
---
After saving, report to the user:
© claesbackman, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Just SKILL.md in Skills/review-pap of claesbackman/AI-research-feedback.
Open the folder on GitHubat commit d129756
We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in claesbackman/AI-research-feedback, which our catalogue first saw on October 7, 2026.
brycewang-stanford/Auto-Empirical-Research-Skills
Write pre-analysis plans: PAP structure, registry, analysis strategy.
claesbackman/AI-research-feedback
Build a Quarto reveal.js slide deck in the explorable-explanation style (Nicky Case) — one idea per slide, assertion titles, a concrete running example, run-time SVG stages the presenter drives…
claesbackman/AI-research-feedback
Split a PDF into chunks and convert it to readable markdown text.
claesbackman/AI-research-feedback
Run a fast 2-agent pre-submission check for an economics paper — focuses on contribution, identification, and causal overclaiming.
claesbackman/AI-research-feedback
Convert a LaTeX research paper into a policy brief, 1-page summary, or 5-page summary for a general audience, with factual review and a standalone HTML page for GitHub Pages.
claesbackman/AI-research-feedback
Run a 6-agent pre-submission panel review for a grant proposal targeting a specified funder or program
claesbackman/AI-research-feedback
Run a fast 3-agent mechanical check of an economics paper — spelling and grammar, internal consistency and cross-references, and unsupported claims.
Run a 6-agent pre-submission review of a pre-analysis plan (PAP) for a specified registration target or journal. Review Pap is an agent skill from claesbackman/AI-research-feedback.
Run `npx skills add claesbackman/AI-research-feedback --skill review-pap -a claude-code`. Or copy the skill folder (Skills/review-pap in claesbackman/AI-research-feedback) into .claude/skills/review-pap in your project. Claude Code loads it when a task matches its description.
Run `npx skills add claesbackman/AI-research-feedback --skill review-pap -a codex`. Or copy the skill folder (Skills/review-pap in claesbackman/AI-research-feedback) into .agents/skills/review-pap in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add claesbackman/AI-research-feedback --skill review-pap -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/review-pap, .gemini/skills/review-pap, .github/skills/review-pap and .opencode/skills/review-pap in your project.
SKILL.md names no scripts, command-line tools or credentials: Review Pap is instructions for the agent only. Its frontmatter pre-approves these tools: Read, Write, Edit, Glob, Grep, Bash, Agent.
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.
Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.
Review Pap is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 6.4k tokens (SKILL.md is roughly 26k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Review Pap: Pre Registration Writing (brycewang-stanford/Auto-Empirical-Research-Skills, 4.5k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
claesbackman (a GitHub user) maintains it in claesbackman/AI-research-feedback, which has 495 GitHub stars. The repository holds 10 skills in this directory. The repository was last updated on September 25, 2026.
Source: claesbackman/AI-research-feedback on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.