Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add happyhappy-jun/writing-driven-autoresearch --skill world-model-ideationgit clone --depth 1 https://github.com/happyhappy-jun/writing-driven-autoresearchWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/happyhappy-jun/writing-driven-autoresearch/world-model-ideation)<a href="https://agentmods.dev/skills/happyhappy-jun/writing-driven-autoresearch/world-model-ideation"><img src="https://agentmods.dev/badge/skills/happyhappy-jun/writing-driven-autoresearch/world-model-ideation/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/happyhappy-jun/writing-driven-autoresearch/world-model-ideation"><img src="https://agentmods.dev/badge/skills/happyhappy-jun/writing-driven-autoresearch/world-model-ideation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00085 | $0.01206 |
| Opus 5.5 | $0.00034 | $0.00482 |
| Sonnet 5.5 | $0.00017 | $0.00241 |
| Haiku 4.5 | $0.00009 | $0.00121 |
Grade A, and why
world-model-ideation scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
World Model Ideation
Overview
Turn a demo concept into a research question, fair comparison, and smallest useful experiment before designing a polished interface. This skill selects and frames the work; auto-research owns evidence management, execution, and submission artifacts.
Read the evaluation patterns and claim guardrails before ranking candidates. Copy the research-spec template into the participant workspace as the handoff to auto-research.
Preflight
Identify the environment, observations, actions or interventions, future target, available data and time, intended Track, and external claims requiring public verification.
If action is unavailable, unobserved, or cannot plausibly change the target, do not call the work action-conditioned or counterfactual. Reframe it as forecasting or representation learning, or reject it as a world-model candidate.
Workflow
Candidate Gate
Generate three research questions, not product slogans. Score each from 0–2 for world-model necessity, intervention identifiability, falsifiability, fair-baseline feasibility, and deadline fit using the reference rubric. Reject a candidate with no observable state/action/target path, no disconfirming outcome, or no fair baseline.
Select the highest-scoring viable candidate and state why the others lost. Do not use a visual simulation, generic predictor, or prompt-only demo as proof of a world model.
Research Handoff
Choose one research pattern from the reference: action-conditioned state prediction, planning with a learned model, representation for downstream control, or counterfactual intervention modeling. Complete research-spec.md with:
- state, observation, action/intervention, target horizon, and why the question needs a world model;
- falsifiable hypothesis, named baseline, primary evaluation metric, success threshold, and claim limit;
- smallest end-to-end comparison, split or sampling rule, and one appropriate smoke test;
- likely failure modes and the intended General Track 1, Training Track 1, or Track 2 route.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2mo ago First seen · 85 lines · 85 tokens per session scan A 9ad67443a5a1
world-model-ideation is a skill published in the GitHub repository happyhappy-jun/writing-driven-autoresearch (23 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 85 tokens to every session and 1,206 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
world-model-ideation compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| world-model-ideationhappyhappy-jun/writing-driven-autoresearch | Skill | 23 | 3mo ago | Apache-2.0 | A | 85 | Claude Code |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |