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Release Research Artifacts

Community
gxCaesar
release-research-artifacts

Use when packaging the code and data a reader or reviewer will download, including releasing the code for a paper, building an anonymized repository for double-blind submission, preparing an archive with a DOI for a journal, writing citation metadata, producing a data card or model card, or preparing a submission for artifact evaluation. Use once the results are frozen, so that what ships is what produced them.

Overview

PublishergxCaesar
Repositoryopen-research-skills
Skill namerelease-research-artifacts
Stars
63
Forks
3
Bundled files
11
LicenseApache-2.0
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.

  • 11 bundled files

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

  • Open source

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

Installation

Install the Release Research Artifacts 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/gxCaesar/open-research-skills/tree/main/skills/release-research-artifacts
  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/gxCaesar/open-research-skills.git /tmp/open-research-skills
mkdir -p .claude/skills
cp -r /tmp/open-research-skills/skills/release-research-artifacts .claude/skills/release-research-artifacts
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Release Research Artifacts 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 Release Research Artifacts 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 Release Research Artifacts 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.

Release Research Artifacts

Build the package someone else downloads, then verify the package rather than the workspace it came from. Almost every release defect is invisible from inside the working directory, because the working directory contains the missing file.

Select one mode

ModeResponsibilityRead first
anonymized-submissionBuild a package that carries no author or institution identity, for double-blind reviewreferences/anonymized-submission.md
named-archiveBuild the citable, deposited version with persistent identifiersreferences/named-archive.md
data-cardDescribe the data and any model well enough to be used without asking youreferences/cards.md
manifest-verificationProve the built package is complete, runnable and unchangedreferences/verification.md

Resolve the installed skill and components

Resolve SKILL_DIR as the directory containing this SKILL.md. Every runtime resource is below that directory; never resolve through a package parent.

Resolve the file set by allowlist

List what goes in. Do not build the package by excluding what should stay out.

An exclusion list is a claim about everything you did not think of, and the things nobody thinks of are exactly the ones that carry identity: editor backup files, notebook checkpoints, cached credentials, environment dumps naming a home directory, log files with absolute paths, and files with no extension that a text scan skips by default.

Write the allowlist as paths and patterns, resolve it into an explicit file list, and review that list before staging. The reviewed list is the artifact; the command that produced it is not.

Stage, then scan every byte

Copy the allowlisted files into an empty staging directory. Scan the staged tree, not the source tree, for author names, institutions, email addresses, absolute home paths, internal hostnames, project codenames, credentials and tokens.

Scan file contents and file names. Scan inside container formats that hold text: office documents, notebooks, archives. A scanner that reads only files with known text extensions will pass a tree whose cache files are the leak.

Seed a known string that should be caught and confirm the scan catches it. A scan reporting zero findings on a clean tree and a scan that is silently not running produce the same output.

Plant the seed in the real formats the tree actually holds, not in a convenient one. A seed placed in a plain text file proves only that the scanner reads plain text files; the formats that leak are the notebook output cell, the archive member, the office document body and the file name itself. A control built in a format the tool already handles validates the control, not the tool.

Test inside the built package

Unpack the built archive into an empty directory and run the project's own test or reproduction command there, with no access to the working repository.

This is the step that catches the file that was never committed, the import that resolves only because of the working directory, the data path that points outside the package, and the dependency that is installed on your machine and nowhere else.

A test run in the workspace proves nothing about the archive.

Record what shipped

Write a listing of the released files with their sizes and a cryptographic hash for each, computed over the built package. Record the commit the package was built from, the interpreter and dependency versions used for the verification run, and the date.

Someone who downloads the package later needs to answer two questions from that record alone: is this the same package, and what produced it.

Boundaries

This skill builds and verifies the package. It does not write the availability statements that point at it, does not grant rights you do not hold over third-party data, and cannot make a restricted dataset shareable. Where the data cannot be released, the package carries the access route and the exact identifiers instead, and says which.

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 Release Research Artifacts AI skill do?

Use when packaging the code and data a reader or reviewer will download, including releasing the code for a paper, building an anonymized repository for double-blind submission, preparing an archive with a DOI for a journal, writing citation metadata, producing a data card or model card, or preparing a submission for artifact evaluation. Use once the results are frozen, so that what ships is what produced them.

Why use Release Research Artifacts on TypingMind?

Because you install it once and use it with any model. Release Research Artifacts 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 Release Research Artifacts in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/gxCaesar/open-research-skills/tree/main/skills/release-research-artifacts. 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 Release Research Artifacts?

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 Release Research Artifacts?

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

Is the Release Research Artifacts AI skill free?

Yes. It is published on GitHub by gxCaesar under the Apache-2.0 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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