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Related Paper Analyzer

CommunityPopular
zhnnky329
related-paper-analyzer

Collect and analyze relevant papers, reports, and reference methods to inform method selection without fabricating references or copying models blindly.

Overview

Publisherzhnnky329
RepositoryMathModeling-skills
Skill namerelated-paper-analyzer
Stars
1.1K
Forks
42
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by zhnnky329 on GitHub. Read the source before you install it.

Installation

Install the Related Paper Analyzer AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/zhnnky329/MathModeling-skills/tree/main/.claude/skills/related-paper-analyzer
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/zhnnky329/MathModeling-skills.git /tmp/MathModeling-skills
mkdir -p .claude/skills
cp -r /tmp/MathModeling-skills/.claude/skills/related-paper-analyzer .claude/skills/related-paper-analyzer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Related Paper Analyzer in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Related Paper Analyzer on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Related Paper Analyzer is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Purpose

Analyze user-provided papers and reports before final method selection.

This skill checks whether the user has placed original paper files under workspace/papers/, reads those originals, and extracts transferable method cues, assumptions, variables, validation ideas, and limitations for the current subquestions.

This skill does not fabricate references, browse for new papers, write the final method card, or copy a published model blindly.

When to use

Use this skill:

  • After problem-parser and problem-classifier have produced validated artifacts.
  • Before method-selector.
  • When the team wants to ground method selection in user-supplied literature rather than guessing from model names.
  • When original papers, reports, or extracted paper text are available under workspace/papers/.

Preconditions

The following should already exist or be provided:

  • A validated problem parse.
  • A validated problem classification artifact.
  • At least one original paper file under workspace/papers/.

If no paper originals are present under workspace/papers/, stop and remind the user to place them there before analysis begins.

Inputs

Use or request:

  • workspace/problem/problem-parser/problem_parse.json, if available.
  • workspace/problem/problem-classifier/problem_classification.json, if available.
  • Original paper files under workspace/papers/.
  • User notes about which papers matter most, if available.

Workflow

  1. Check workspace/papers/ first.

    • Look for user-supplied paper originals or extracted paper text.
    • Accept formats such as .pdf, .docx, .md, .txt, or clearly named extracted text files.
    • Ignore workspace/papers/related_paper_analysis.md if it already exists.
  2. Stop early if the paper folder is empty.

    • If no paper originals are found, tell the user to place the paper originals under workspace/papers/.
    • Do not fabricate literature summaries from memory.
  3. Inventory the available papers.

    • Record file name, apparent title if recoverable, likely language, and likely relevance.
    • Mark unreadable or weakly relevant files explicitly.
  4. Read papers for modeling signals.

    • Extract task type, assumptions, variables, data requirements, method families, outputs, validation patterns, and limitations.
    • Focus on transferable ideas rather than copying a complete published pipeline.
  5. Map literature cues to the current problem.

    • Link relevant papers to specific subquestions such as Q1, Q2, or Q3.
    • Distinguish useful method cues from paper-specific tricks that should not be copied directly.
  6. Summarize risks and reuse boundaries.

    • Flag assumptions that may not fit the current contest problem.
    • Flag data requirements that the current workspace may not satisfy.
    • Flag methods that look impressive in papers but may be infeasible under contest constraints.
  7. Write the literature analysis report.

    • Save the report as workspace/papers/related_paper_analysis.md.
    • Keep it concise, traceable, and ready for method-selector.
  8. Hand off to method-selector.

    • Pass forward the paper inventory, method cues, cautions, and unresolved evidence gaps.

Outputs

Produce one Markdown literature-analysis artifact:

  • workspace/papers/related_paper_analysis.md

The report should include:

  • reviewed paper inventory
  • per-paper summary
  • subquestion-to-paper mapping
  • transferable method cues
  • assumptions or limitations worth carrying forward
  • methods that should not be copied blindly
  • missing evidence or unresolved questions
  • recommended next skill

Output format

Write a Markdown report at workspace/papers/related_paper_analysis.md.

Suggested sections:

  • Reviewed papers
  • Transferable method cues by subquestion
  • Useful assumptions, variables, and validation ideas
  • Risks of direct reuse
  • Missing evidence
  • Recommended next skill

Rules

  • Analyze only user-supplied papers or extracted paper text under workspace/papers/.
  • Do not fabricate titles, authors, years, venues, DOIs, or conclusions.
  • Do not browse for new literature inside this skill.
  • Do not turn literature analysis into final method selection.
  • Do not override the validated problem parse or classification.
  • Do not copy a paper's model blindly without checking fit to the current subquestion, data, and contest constraints.
  • Do not skip the initial check for paper originals in workspace/papers/.

Verification

Before handoff, verify:

  • workspace/papers/ was checked for user-supplied paper originals.
  • At least one paper file was found, or a clear blocker was reported.
  • Each relevant subquestion has mapped paper cues or an explicit note that no relevant paper was found.
  • Transferable ideas are separated from risky direct reuse.
  • The report is saved at workspace/papers/related_paper_analysis.md.
  • The next skill is method-selector.

Failure modes

Stop and report a blocker if:

  • workspace/papers/ contains no paper originals.
  • All paper files are unreadable or corrupted.
  • The files are too incomplete to identify any reusable method cues.
  • The user asks for method selection or paper claims before literature analysis is available.

Stop conditions

This skill must stop instead of guessing when:

  • No original papers are available under workspace/papers/.
  • File contents are too incomplete to support a trustworthy summary.
  • Continuing would require inventing references, assumptions, or paper conclusions.

When stopping, output:

  • the blocker
  • why it matters
  • the checked folder path
  • readable files found, if any
  • missing paper files or missing readable text
  • recommended next action

Handoff

After producing workspace/papers/related_paper_analysis.md, hand off to:

method-selector

The handoff should include:

  • report path
  • reviewed paper list
  • per-subquestion literature cues
  • reusable method families
  • risky assumptions or direct-copy risks
  • validation or robustness ideas worth carrying forward
  • unresolved evidence gaps

Examples

Example 1: Papers missing

Input state:

  • Problem parse and classification exist.
  • workspace/papers/ contains no original paper files.

Output:

markdown
Blocker: no user-supplied paper originals were found under `workspace/papers/`.

Why it matters: this skill is designed to analyze original paper artifacts, not fabricate literature notes from memory.

Recommended next action: place the relevant paper originals or extracted paper text under `workspace/papers/`, then run `related-paper-analyzer` again.

Example 2: Papers available

Input state:

  • Problem parse and classification exist.
  • workspace/papers/ contains several relevant PDF papers.

Output:

  • workspace/papers/related_paper_analysis.md

The report summarizes:

  • which papers are relevant to Q1, Q2, and Q3
  • which method families appear reusable
  • which assumptions look risky under current contest data constraints
  • which validation ideas should be passed to method-selector

Frequently asked questions

What does the Related Paper Analyzer AI skill do?

Collect and analyze relevant papers, reports, and reference methods to inform method selection without fabricating references or copying models blindly.

Why use Related Paper Analyzer on TypingMind?

Because you install it once and use it with any model. Related Paper Analyzer is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Related Paper Analyzer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zhnnky329/MathModeling-skills/tree/main/.claude/skills/related-paper-analyzer. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Related Paper Analyzer?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Related Paper Analyzer?

As many as you like. As long as a model supports skills, you can use Related Paper Analyzer with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Related Paper Analyzer AI skill free?

Yes. It is published on GitHub by zhnnky329 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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