DeepScientist is a local research studio that manages the cycle from baseline experiments through research findings and paper-ready outputs. Researchers use it to organize autonomous scientific investigations, review progress, and take control when needed. The catalogue add-ons provide workflows and agent integrations for running research projects with it.
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 ResearAI/DeepScientist --skill paper-outlinegit clone --depth 1 https://github.com/ResearAI/DeepScientistWrote 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/researai/deepscientist/paper-outline)<a href="https://agentmods.dev/skills/researai/deepscientist/paper-outline"><img src="https://agentmods.dev/badge/skills/researai/deepscientist/paper-outline/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/researai/deepscientist/paper-outline"><img src="https://agentmods.dev/badge/skills/researai/deepscientist/paper-outline.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00053 | $0.02220 |
| Opus 5 | $0.00026 | $0.01110 |
| Sonnet 5 | $0.00011 | $0.00444 |
| Haiku 4.5 | $0.00005 | $0.00222 |
Grade A, and why
paper-outline 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 11d 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 — 228 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Paper Outline
Use this before write when the outline feels like a run log, result dump, engineering note, or group-meeting report instead of a paper plan.
One-Sentence Summary
Keep one selected outline, but split two views:
paper_view: what the paper will say to readers.evidence_view: where the exact runs, paths, rows, settings, and reproducibility details live.
The paper should be faithful to the actual evidence, but it should not repeat the agent workflow.
Basic Workflow
- Read the current paper state.
Use
artifact.get_paper_contract(detail='full'),artifact.list_paper_outlines(...), and thenartifact.validate_academic_outline(detail='full')if an outline exists. - Find the one-sentence paper idea. Ask: "What should a researcher remember after reading this paper?" This is not a metric row and not an implementation setting.
- Separate facts from interpretation. Facts are measured results. Interpretations are the careful academic lesson supported by those facts. Unsupported claims go into "must not claim."
- Write or repair
paper_view. Fill the paper idea, problem/gap/method/result/limit, 1-3 scoped claims, method intuition, evaluation plan, and 4-8 useful analysis jobs. - Keep engineering details out of the story.
Put ports, worktrees, batch shorthand, route decisions, user requests, artifact ids, exact file paths, and local commands into
evidence_viewor appendix-only reproducibility fields. - Validate and compile.
Run
artifact.validate_academic_outline(detail='full'). If it passes, runartifact.compile_outline_to_writing_plan(detail='full').
What Good Means
A good outline does three things:
- It has a point: one clear claim or lesson, not a list of what the agent did.
- It is honest: every claim is tied to durable evidence, and limits are explicit.
- It is useful to a reader: the method and analyses teach something beyond "this setup got a number."
Strong papers often start from simple code but make a useful idea legible. Residual connections are more than a code shortcut; the paper teaches how to make depth trainable. Attention is more than a module; the paper teaches how to remove a bottleneck. Do the same only when the quest evidence supports that kind of interpretation.
What ships with it
1 file 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.
- 11d ago First seen · 228 lines · 53 tokens per session scan A 79ae0ec49d75
paper-outline is a skill published in the GitHub repository ResearAI/DeepScientist (3,323 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 53 tokens to every session and 2,220 once invoked, about $0.0003 per session on Opus 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.
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