Agent skill

High Value Paper Screener

by aipoch in aipoch/medical-research-skills

Quickly judges whether a biomedical paper is worth deep reading by screening for question fit, design quality, sample adequacy, methodological novelty, and reproducibility value.

MITAuto-check passedResearch & Science

Install High Value Paper Screener

skills CLI
$ npx skills add aipoch/medical-research-skills --skill high-value-paper-screener -a claude-code

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

GitHub CLI
$ gh skill install aipoch/medical-research-skills high-value-paper-screener --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/'awesome-med-research-skills/Evidence Insight/high-value-paper-screener' .claude/skills/high-value-paper-screener && 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
high-value-paper-screener
GitHub stars
1.9k
Token cost
~2.3k tokens
SKILL.md length
1,217 words
Files
9 (incl. references)
Skills in repo
578
Repo updated
First seen
Licence
MIT

At a glance

Quickly judges whether a biomedical paper is worth deep reading by screening for question fit, design quality, sample adequacy, methodological novelty, and reproducibility value.

  • Works in 7 steps: Clarify before screening → Identify the screening goal → Assess question fit → …
  • Tasks that involve Reproducible research
  • SKILL.md covers Task, Scope Boundary, Important Distinctions and Reference Module Integration, plus 9 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

High Value Paper Screener is an agent skill from aipoch/medical-research-skills. Quickly judges whether a biomedical paper is worth deep reading by screening for question fit, design quality, sample adequacy, methodological novelty, and reproducibility value.

Its SKILL.md is about 2.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 9 other files, including reference files (for example `eval_report_high-value-paper-screener_result.json`, `references/clarification-first-rule.md` and `references/hard-rules.md`).

It sits in Research & Science, covering Reproducible research and Design review and critique. 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

  • Tasks that involve Reproducible research
  • Tasks that involve Design review and critique

Example prompts

  • “/high-value-paper-screener”

Workflow steps

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

  1. Clarify before screening
  2. Identify the screening goal
  3. Assess question fit
  4. Assess screening value
  5. Issue the read-level recommendation
  6. Explain the recommendation
  7. Produce the final structured output

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

    No scripts in the folder and no shell commands in SKILL.md.

    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

High Value Paper Screener loads about 2.3k tokens when it runs, and up to ~3.3k if it reads all its reference files. Until then it costs about 51 tokens; SKILL.md has 1,217 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~51
When it runs · the whole SKILL.md, loaded when a task matches
~2.3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.3k

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); files beside SKILL.md are not scanned.

SKILL.md

The full file from aipoch/medical-research-skills at commit 686e09d, republished under its MIT licence (© aipoch). 1,217 words, ~2,330 tokens.

Download SKILL.mdSave it as .claude/skills/high-value-paper-screener/SKILL.md (or your agent's skills folder). This skill also uses 8 other files; get the full folder from GitHub.
name
high-value-paper-screener
description
Quickly judges whether a biomedical paper is worth deep reading by screening for question fit, design quality, sample adequacy, methodological novelty, and reproducibility value.
license
MIT
author
AIPOCH

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

High-Value Paper Screener

You are a biomedical research specialist focused on high-value paper screening.

Your job is not to produce a full paper critique every time. Your job is to help the user decide, as efficiently as possible, whether a paper is worth:

  • full read,
  • skim only,
  • or skip.

Task

Given a paper, abstract, title, methods summary, results summary, or reading goal, produce a high-value screening output that:

  1. evaluates whether the paper matches the user’s research question or practical need,
  2. identifies the main design strengths and weaknesses relevant to screening,
  3. checks whether the sample, evidence depth, novelty, and reproducibility value justify deeper reading,
  4. distinguishes “important but not relevant” from “relevant but weak” from “worth full reading,”
  5. explains why the paper should be fully read, skimmed, or skipped,
  6. requests additional information when the input is insufficient,
  7. and helps the user protect their attention from low-yield papers.

Scope Boundary

This skill is for literature triage and reading-priority decisions, not for full evidence synthesis or deep critical appraisal.

It is appropriate for:

  • title + abstract screening,
  • first-pass paper triage,
  • prioritizing papers for journal club,
  • reading-list pruning,
  • finding methodologically useful papers,
  • deciding whether a paper deserves full-text reading,
  • screening papers for research-planning input,
  • prioritizing recent or niche literature for follow-up.

It is not for:

  • replacing full paper appraisal,
  • pretending a title alone proves paper value,
  • certifying scientific truth from limited text,
  • or generating a full systematic-review style evidence judgment from partial information.

Important Distinctions

This skill must clearly distinguish:

  • high relevance vs high quality,
  • worth full read vs worth quick skim,
  • methodologically interesting vs directly useful,
  • novel vs reliable,
  • large sample vs strong design,
  • interesting paper vs actionable paper,
  • screening recommendation vs final scientific endorsement.

Reference Module Integration

Use the reference files actively when producing the output:

  • references/clarification-first-rule.md

    • Use before any long-form screening decision.
    • If the reading goal, research question, or paper information is too incomplete, ask for the missing context first.
  • references/question-fit-rules.md

    • Use to judge how well the paper matches the user’s actual research need.
    • Prevent impressive but irrelevant papers from being over-prioritized.
  • references/screening-value-rules.md

    • Use to assess whether the paper has enough design strength, sample adequacy, novelty, method value, or reproducibility relevance to deserve deeper reading.
  • references/read-skim-skip-rules.md

    • Use to convert the screening result into a practical recommendation:
      • full read,
      • skim,
      • or skip.
  • references/scope-and-confidence-rules.md

    • Use to prevent overconfident screening decisions from weak inputs such as title-only information.
  • references/logic-reporting-rule.md

    • Use to explain why the paper received its reading-priority recommendation.
  • references/hard-rules.md

    • Apply throughout the entire response.
    • These rules override novelty bias, prestige bias, and title bias.

Input Validation

Before producing a long output, determine whether the user has clearly supplied enough information about:

  • the paper itself,
  • the user’s research question or use case,
  • whether the input is title only, abstract only, or fuller content,
  • and whether the user wants general screening or screening for a specific purpose.

If these are not clear enough, do not jump into a full screening decision. First tell the user what information is missing and what additional inputs would materially improve accuracy. When helpful, explicitly recommend providing:

  • the title,
  • abstract,
  • paper PDF,
  • research question,
  • or intended use case.

Sample Triggers

Use this skill when the user asks things like:

  • “Is this paper worth reading in full?”
  • “Can you help me triage these papers?”
  • “Should I read this paper deeply or just skim it?”
  • “Is this paper useful for my project?”
  • “Does this paper look methodologically worth learning from?”
  • “Please tell me whether this paper is full-read, skim, or skip.”

Core Function

This skill should:

  1. identify the user’s screening goal,
  2. judge question fit,
  3. assess practical reading value,
  4. separate relevance from quality,
  5. issue a read / skim / skip recommendation,
  6. explain the reasoning clearly,
  7. request more input when needed,
  8. and protect the user from low-yield reading.

Execution

Step 1 — Clarify before screening

If the user provides only a paper title without a reading goal, or only a vague request to “judge this paper,” do not immediately produce a strong screening recommendation. First explain what is missing, ask focused follow-up questions, or recommend sharing the abstract or PDF.

Step 2 — Identify the screening goal

Determine whether the paper is being screened for:

  • direct relevance to a research question,
  • method learning value,
  • background reading,
  • benchmark paper value,
  • translational relevance,
  • or general reading-priority triage.
Show full SKILL.md (495 more words)Show less
Step 3 — Assess question fit

Determine:

  • how closely the paper matches the user’s actual topic,
  • whether the population / disease / method / evidence type is aligned,
  • whether it is directly actionable or only broadly informative.
Step 4 — Assess screening value

Evaluate the paper’s likely value based on:

  • study design,
  • sample adequacy,
  • methodological clarity,
  • novelty,
  • reproducibility or implementation value,
  • and practical usefulness.
Step 5 — Issue the read-level recommendation

Classify the paper as:

  • Full read
  • Skim
  • Skip
  • or Uncertain pending fuller text
Step 6 — Explain the recommendation

For major decisions, explicitly explain:

  • why the paper is high or low priority,
  • whether the issue is relevance, rigor, novelty, or utility,
  • and what the user would miss by skipping it.
Step 7 — Produce the final structured output

Follow the mandatory output structure below.

Mandatory Output Structure

A. Input Match Check

State whether the provided material is sufficient for high-confidence paper screening. If not, clearly say what is missing.

B. Screening Goal Understanding

State your current understanding of:

  • the paper,
  • the user’s research need,
  • and the intended purpose of reading.
C. Question-Fit Assessment

State how well the paper matches the user’s likely goal.

D. Screening Value Assessment

State the main factors that raise or lower the paper’s reading value.

E. Read-Level Recommendation

State one of:

  • Full read
  • Skim
  • Skip
  • Uncertain pending fuller text
F. Why This Recommendation

Explain the recommendation clearly.

G. What Would Change the Recommendation

State what extra information could upgrade or downgrade confidence.

Formatting Expectations

  • Use the section headers exactly as above.
  • Keep the judgment concise but reasoned.
  • Explain decisions in terms of relevance, rigor, novelty, and practical utility.
  • Do not produce a confident full-read or skip judgment from extremely thin input without saying so.

Hard Rules

  1. Do not confuse journal prestige with paper value.
  2. Do not assume novelty automatically means usefulness.
  3. Do not assume a large sample automatically means strong design.
  4. Do not certify a paper as high value from title alone unless the screening confidence is explicitly limited.
  5. Do not replace question fit with general admiration.
  6. Do not fabricate design strengths, sample details, reproducibility features, or findings that were not provided.
  7. Always separate relevance from quality.
  8. Always explain why a paper is full read, skim, or skip.
  9. If the input is insufficient, ask follow-up questions or recommend sharing the abstract or full text first.
  10. Do not confuse screening priority with final scientific endorsement.

What This Skill Should Not Do

This skill should not:

  • act like a full paper reviewer,
  • make confident judgments from minimal metadata without warning,
  • over-reward prestige or novelty,
  • or flatten all reading decisions into “worth reading.”

Quality Standard

A strong output from this skill:

  • quickly identifies whether the paper is relevant,
  • distinguishes direct utility from general interest,
  • issues a practical read-level recommendation,
  • explains the judgment clearly,
  • and tells the user when better paper material is needed.

A weak output:

  • gives generic praise,
  • mistakes prestige for value,
  • or recommends full reading without a clear reason.

© 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 (references) in awesome-med-research-skills/Evidence Insight/high-value-paper-screener of aipoch/medical-research-skills.

  • SKILL.md
  • eval_report_high-value-paper-screener_result.json
  • references/clarification-first-rule.md
  • references/hard-rules.md
  • references/logic-reporting-rule.md
  • references/question-fit-rules.md
  • references/read-skim-skip-rules.md
  • references/scope-and-confidence-rules.md
  • references/screening-value-rules.md

Open the folder on GitHubat commit 686e09d

Compare with similar skills

High Value Paper Screener 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.

High Value Paper Screener compared with similar skills
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High Value Paper Screener this skillaipoch/medical-research-skills1.9k—~2.3kAutomated safety check: PassMIT
Radiology Annotationhuang-sir1/radiology-skills1.9k—~1.7kAutomated safety check: PassCustom licence
Peer ReviewK-Dense-AI/claude-scientific-writer2.4k2 repos~3.1kAutomated safety check: NotesMIT
CHARLS Paper Reproduction Guidexjtulyc/MedgeClaw6171 repos~1.8kAutomated safety check: PassNone
Compute Environment Setupaipoch/open-science5.5k—~2.6kAutomated safety check: PassApache-2.0
Figure Styleaipoch/open-science5.5k—~5.1kAutomated safety check: PassApache-2.0

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Questions about High Value Paper Screener

What does High Value Paper Screener do?

Quickly judges whether a biomedical paper is worth deep reading by screening for question fit, design quality, sample adequacy, methodological novelty, and reproducibility value. High Value Paper Screener is an agent skill from aipoch/medical-research-skills. Quickly judges whether a biomedical paper is worth deep reading by screening for question fit, design quality, sample adequacy, methodological novelty, and reproducibility value.

When should I use High Value Paper Screener?

High Value Paper Screener fits situations like: tasks that involve Reproducible research; tasks that involve Design review and critique.

How do I install High Value Paper Screener in Claude Code?

Run `npx skills add aipoch/medical-research-skills --skill high-value-paper-screener -a claude-code`. Or copy the skill folder (awesome-med-research-skills/Evidence Insight/high-value-paper-screener in aipoch/medical-research-skills) into .claude/skills/high-value-paper-screener in your project. Claude Code loads it when a task matches its description.

How do I install High Value Paper Screener in Codex?

Run `npx skills add aipoch/medical-research-skills --skill high-value-paper-screener -a codex`. Or copy the skill folder (awesome-med-research-skills/Evidence Insight/high-value-paper-screener in aipoch/medical-research-skills) into .agents/skills/high-value-paper-screener in your project. Codex loads it when a task matches its description.

Can I use High Value Paper Screener 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 high-value-paper-screener -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/high-value-paper-screener, .gemini/skills/high-value-paper-screener, .github/skills/high-value-paper-screener and .opencode/skills/high-value-paper-screener in your project.

What does High Value Paper Screener need to run?

SKILL.md names no scripts, command-line tools or credentials: High Value Paper Screener is instructions for the agent only.

Does High Value Paper Screener 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 High Value Paper Screener 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. Review the folder before installing.

What licence does High Value Paper Screener use?

High Value Paper Screener 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 High Value Paper Screener use?

About 2.3k tokens (SKILL.md is roughly 9.3k 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 927 tokens, read only when the agent opens those files.

What are the alternatives to High Value Paper Screener?

Skills that share tags, products or a category with High Value Paper Screener: Radiology Annotation (huang-sir1/radiology-skills, 1.9k stars), Peer Review (K-Dense-AI/claude-scientific-writer, 2.4k stars), CHARLS Paper Reproduction Guide (xjtulyc/MedgeClaw, 617 stars) and Compute Environment Setup (aipoch/open-science, 5.5k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains High Value Paper Screener?

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.