Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add claesbackman/AI-research-feedback --skill review-papgit clone --depth 1 https://github.com/claesbackman/AI-research-feedbackWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/claesbackman/ai-research-feedback/review-pap)<a href="https://agentmods.dev/skills/claesbackman/ai-research-feedback/review-pap"><img src="https://agentmods.dev/badge/skills/claesbackman/ai-research-feedback/review-pap/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/claesbackman/ai-research-feedback/review-pap"><img src="https://agentmods.dev/badge/skills/claesbackman/ai-research-feedback/review-pap.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00029 | $0.05609 |
| Opus 5 | $0.00015 | $0.02805 |
| Sonnet 5 | $0.00006 | $0.01122 |
| Haiku 4.5 | $0.00003 | $0.00561 |
Grade A, and why
review-pap scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 516 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
Phase 1: Parse Arguments and Discover the PAP
Parse $ARGUMENTS as follows:
- The recognized registration targets are:
- Trial registries:
AEA,EGAP,OSF,ClinicalTrials,ISRCTN - Journal standards:
AER,QJE,JPE,RESTUD,AEJ,JEEA - General standards:
top-journal,working-paper - (case-insensitive; users can extend this list by editing this skill file)
- Trial registries:
- If the first token of
$ARGUMENTSmatches one of these names, treat it as the registration target and treat any remaining text as the main PAP file path. - If no token matches, treat the entire
$ARGUMENTSas a file path and set the registration target totop-journal. - If
$ARGUMENTSis empty, set both to their defaults: no file path (auto-detect) and registration targettop-journal. - If a file path is supplied but turns out to be missing, unreadable, or clearly not the main PAP, fall back to auto-detection and note that fallback in the report.
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:
- Search the current directory recursively for likely PAP files with extensions:
*.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 areviews/folder. These are outputs of earlier review runs, not PAP materials. - Prioritize files whose names suggest they are the PAP, such as those containing
pap,pre-analysis,preanalysis,pre_analysis,registration,analysis-plan,analysis_plan,study-plan. - Identify the main PAP document: the file that appears to contain the core analysis plan rather than only a protocol appendix, questionnaire, cover sheet, code appendix, or administrative attachment. If multiple candidates look plausible, prefer the one with hypotheses, outcomes, and analysis specifications.
- Read the main PAP file and identify references to supporting documents:
- Power calculations or sample-size worksheets
- Survey instruments, questionnaires, or interview guides
- Randomization protocols or sampling frames
- Code skeletons, mock tables, or shells
- Data dictionaries or codebooks
- IRB/ethics protocols
- Search recursively for likely supporting files and record them if present:
- Power/sample: files containing
power,sample_size,samplesize,mde - Instruments: files containing
survey,questionnaire,instrument,endline,baseline - Randomization: files containing
randomization,randomisation,strata,block - Code: files containing
analysis,code,dofile,do_file,script,mock - Ethics: files containing
irb,ethics,consent
- Power/sample: files containing
- Record:
- Full path of the main PAP file and each supporting file with its likely role
- Study title, PI(s)/team, and abstract or research question if available
- Named registration registry, trial ID, or journal if any
- Whether any expected supporting file categories were not found
If 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.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 516 lines · 0 tokens per session scan A 97c76d94dfb7
review-pap is a skill published in the GitHub repository claesbackman/AI-research-feedback (478 stars, last pushed 15d ago), licensed MIT. It adds 29 tokens to every session and 5,609 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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