Research Foresight logo

Research Foresight

Organization
ARA-Labs
research-foresight

ARA World Model — read-only reasoning engine over ONE Agent-Native Research Artifact (ARA), run LOCALLY with the coding agent itself as the LLM (no SDK, no API key). Given an ARA directory and a free-text query, it answers any question about the ARA — a forward "what if I change X", but equally why-did-this-work, what-should-I-try, is-this-sound, how-do-these-compare, or anything else — by retrieving precedent from the ARA's native files (references/RETRIEVE.md) and answering as the Predictor (references/PREDICT.md): a bold, grounded, falsifiable Answer shaped to what the question actually calls for. TRIGGERS: ask the world model, wm predict, predict with the world model, what if I change X, forecast the loss curve, will this help, why did this work, what should I try next, is this claim sound, compare these, retrieve precedent, what precedent surfaces.

Overview

PublisherARA-Labs
RepositoryAgent-Native-Research-Artifact
Skill nameresearch-foresight
Stars
691
Forks
53
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 ARA-Labs on GitHub. Read the source before you install it.

Installation

Install the Research Foresight 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/ARA-Labs/Agent-Native-Research-Artifact/tree/main/skills/research-foresight
  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/ARA-Labs/Agent-Native-Research-Artifact.git /tmp/Agent-Native-Research-Artifact
mkdir -p .claude/skills
cp -r /tmp/Agent-Native-Research-Artifact/skills/research-foresight .claude/skills/research-foresight
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Research Foresight 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 Research Foresight 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 Research Foresight 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.

research-foresight — the ARA World Model

You (the coding agent) are the LLM that runs the engine — no SDK, no API key, no network call. The engine is three reference contracts under this skill's references/ directory (quote every path; it may contain spaces):

  • references/CONTRACT.md — the foundation both contracts bind to; if documents disagree, it wins.
  • references/RETRIEVE.md — the Retriever: agentic search + semantic rank over the ARA's native files.
  • references/PREDICT.md — the Predictor: grounded, honest answering of the question asked.

Inputs

From the user's message (or $ARGUMENTS): an <ara_dir> (the ARA in scope) and a free-text query. If <ara_dir> turns out not to be an ARA (a plain paper, repo, or notes folder), compile it into one first with /compiler <path>, then rerun this skill.

Procedure

  1. Retrieve — adopt references/RETRIEVE.md. Read it now and follow it exactly against <ara_dir>.
  2. Answer — adopt references/PREDICT.md. Read it now and follow it exactly, consuming the retrieval from Step 1.

Render the answer prominently, then the honesty envelope (grounded_inference / speculative_leap / basis / reasoning / confidence / confidence_reason / falsifiable).

The engine is read-only: read nothing outside <ara_dir> and this skill's references/; write nothing anywhere.

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 Research Foresight AI skill do?

ARA World Model — read-only reasoning engine over ONE Agent-Native Research Artifact (ARA), run LOCALLY with the coding agent itself as the LLM (no SDK, no API key). Given an ARA directory and a free-text query, it answers any question about the ARA — a forward "what if I change X", but equally why-did-this-work, what-should-I-try, is-this-sound, how-do-these-compare, or anything else — by retrieving precedent from the ARA's native files (references/RETRIEVE.md) and answering as the Predictor (references/PREDICT.md): a bold, grounded, falsifiable Answer shaped to what the question actually...

Why use Research Foresight on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ARA-Labs/Agent-Native-Research-Artifact/tree/main/skills/research-foresight. 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 Research Foresight?

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 Research Foresight?

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

Is the Research Foresight AI skill free?

Yes. It is published on GitHub by ARA-Labs 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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