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Mathodology Evidence Search

Community
sweetcornna
mathodology-evidence-search

Use when finding literature, datasets, domain facts, citation details or licensed figure references.

Overview

Publishersweetcornna
Repositorymathodology
Skill namemathodology-evidence-search
Stars
312
Forks
14
Bundled files
3
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.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Mathodology Evidence Search 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/sweetcornna/mathodology/tree/main/.claude/skills/mathodology-evidence-search
  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/sweetcornna/mathodology.git /tmp/mathodology
mkdir -p .claude/skills
cp -r /tmp/mathodology/.claude/skills/mathodology-evidence-search .claude/skills/mathodology-evidence-search
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Mathodology Evidence Search 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 Mathodology Evidence Search 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 Mathodology Evidence Search 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.

Mathodology Evidence Search

Use evidence to support a claim or model choice. Prefer primary work and official data. Mark assumptions and unresolved gaps; never fill them with invented facts.

Search and read

Inspect available tools. Normally combine built-in WebSearch with mcp__search__search: complementary discovery queries can find different sources. Record which channels actually contributed. If a channel is unavailable, continue with the working one and note the coverage limitation; an unavailable search is not evidence that a source does not exist. With no search, work from supplied material and state which external claims remain unverified.

The project .mcp.json registers the keyless free-search-mcp server and a staged download directory. Tool signatures vary by installed version: read the live schema instead of assuming the argument names in an old example.

CapabilityAppropriate use
search / researchFocused discovery / an open research question
cache_searchReuse material already read
fetch / fetch_batchRead shortlisted web pages
read_docRead PDF and structured or office documents, with pagination
extract_structuredConfirm publisher metadata such as DOI and title
compareReconcile conflicting sources or definitions
paper_graphExplore prior work and check available correction/retraction notices
downloadRetain a file needed for reproduction or a licensed reference
enginesDiagnose thin results or unavailable sources

Use the narrowest category the live tool exposes. Typical groups include paper (with index, preprint, biomed, cs, openaccess, trial and math subgroups), dataset (repository, ml, gov), news, finance, github, forum and image. Subgroup availability depends on the server. A general web domain filter is not equivalent to a bibliographic search.

Retrieve before writing: find and read a source before drafting the sentence that cites it. A reference recalled from memory is a lead to check, not a citation. Merge results by DOI or canonical URL. Read an accepted source once, using the appropriate reader. When sources disagree, cite both, prefer the official or primary record, and record why the adopted value fits the model.

Verify citations and retain evidence

Resolve every DOI or arXiv identifier with a tool, including papers you are sure of, and take title, authors, year and venue from the retrieved record rather than from recall. Cite only URLs that were actually retrieved. Cite a preprint as a preprint; look for the published version and prefer it when it exists. State a finding no more strongly than its study design allows: an association, a simulation or a single case does not establish a cause or a general law. When an identifier or detail cannot be confirmed, record the gap instead of guessing; an omitted DOI is better than an invented one. For a load-bearing paper, use paper_graph when available and inspect publisher notices. A lack of a returned notice does not prove that a paper has never been corrected or retracted; record the coverage limitation when that matters.

Before submitting a paper, and on any bibliography an agent wrote, run the optional reference checker. It uses only the Python standard library, reads .bib, Markdown and LaTeX files or bare DOIs, and writes nothing:

bash
python3 .claude/skills/mathodology-evidence-search/scripts/verify_refs.py refs.bib paper.tex

It resolves identifiers as Mathodology Studio does: Crossref first, otherwise the registration agency named by doi.org, with metadata from DataCite or from doi.org for agencies such as mEDRA, JaLC, ISTIC and CNKI. It compares each entry's title, first author and year with the registered work, reports retraction, correction and withdrawal notices, flags preprints and suggests a DOI for entries without one. Exit status 1 means at least one failure; --json gives per-item results. A DOI that the DOI system reports as non-existent is probably mistyped or invented. A timeout or rate limit is reported as inconclusive and says nothing about the reference. A clean run shows that identifiers exist and match their records, not that a source supports the claim it is cited for.

Keep a readable source note: supported claim, URL/DOI, version or date, access date, discovery channel, extracted quantity, credibility, and uncertainty. Record queries briefly so another researcher can retrace discovery. No fixed YAML handoff or separate ledger file is required. Verification belongs in these notes; the deliverable cites its sources without narrating the checking ("all DOIs verified").

Numerical inputs need a retained data file or reproducible acquisition procedure. For retained downloads, record source URL, license and SHA-256. MCP downloads are staged and may expire; copy needed files to the working directory promptly and verify that the retained bytes match the recorded hash. Do not leave the only copy in staging. Do not redistribute material without permission to do so.

If download is unavailable, use a permitted existing file-transfer tool or give the user a source link and explain the gap. Do not reconfigure custom MCP settings or bypass access controls. Avoid collecting credentials in the conversation.

Figure references

Use figure presets for visual work. Its reference collection distinguishes originals, counterexamples and synthetic previews. For additional references, verify the individual asset's license and any third-party credit lines. Open access alone is not a reuse license.

Save a small, relevant selection with attribution, version, original URL, access date and hash. Prefer an immutable source revision for code examples. Keep copied source as reference text, review it before adaptation, and do not execute it automatically. Extract design principles; do not present another paper's image or numerical results as the current model's output.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Mathodology Evidence Search AI skill do?

Use when finding literature, datasets, domain facts, citation details or licensed figure references.

Why use Mathodology Evidence Search on TypingMind?

Because you install it once and use it with any model. Mathodology Evidence Search 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 Mathodology Evidence Search in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/sweetcornna/mathodology/tree/main/.claude/skills/mathodology-evidence-search. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Mathodology Evidence Search?

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 Mathodology Evidence Search?

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

Is the Mathodology Evidence Search AI skill free?

Yes. It is published on GitHub by sweetcornna 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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