How do I install the Research Foresight skill?

Run npx skillmds@latest add ara-labs/research-foresight in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Research Foresight safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Research Foresight?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Research Foresight free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Research Foresight?

ara-labs (@ara-labs) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Research Foresight by ara-labs — Agent Skill

Name: Research Foresight
Author: 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.

ara-labs 3a22fed 4 files · 42.6 KB Updated Sep 17, 2026

File contents

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

Retrieve — adopt references/RETRIEVE.md. Read it now and follow it exactly against <ara_dir>.
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.

---name: research-foresightdescription: 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.---# research-foresight — the ARA World ModelYou (the coding agent) are the LLM that runs the engine — no SDK, no API key, no network call. Theengine 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.## InputsFrom 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.## Procedure1. **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/`; writenothing anywhere.

Frequently asked questions