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 reviewgit 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/review)<a href="https://agentmods.dev/skills/researai/deepscientist/review"><img src="https://agentmods.dev/badge/skills/researai/deepscientist/review.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 83 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Excessive Agency · line 388 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00032 | $0.04892 |
| Opus 5 | $0.00016 | $0.02446 |
| Sonnet 5 | $0.00006 | $0.00978 |
| Haiku 4.5 | $0.00003 | $0.00489 |
Grade A, and why
review 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 8d 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 — 507 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review
Use this skill when the quest already has a substantial draft, paper, or paper-like report and now needs an independent, skeptical, evidence-grounded audit. The goal is to reduce revision ambiguity, not to sound harsh for its own sake.
This is not the same as ordinary write.
It is also not the same as rebuttal.
writeturns accepted evidence into a narrative.reviewaudits that narrative like a harsh but constructive expert reviewer.rebuttalresponds to concrete external reviewer pressure that already exists.
Interaction discipline
- Follow the shared interaction contract injected by the system prompt.
- For ordinary active work, prefer a concise progress update once work has crossed roughly 6 tool calls with a human-meaningful delta, and do not drift beyond roughly 12 tool calls or about 8 minutes without a user-visible update.
- When the review report, revision plan, or follow-up experiment TODO list becomes durable, send a richer
artifact.interact(kind='milestone', reply_mode='threaded', ...)update that says what the main risks are, what should be fixed next, and whether the next route is writing, experiment, or claim downgrade. - Hard execution rule: if this stage needs terminal work such as document builds, scripted checks, Git inspection, or file inspection, every such command must go through
bash_exec.
Three-layer todo contract
- treat quest-root
plan.mdas the top-level research map whose next anchor must become explicit after the review - if the review pass is multi-step, use workspace
PLAN.mdas the current review-node contract andCHECKLIST.mdas the execution frontier - treat
paper/review/experiment_todo.mdas a review-facing frontier subset, not as a replacement for the quest-level map
Purpose
review is an auxiliary audit skill for paper-like deliverables.
It should convert “the draft feels almost done” into a durable, skeptical, technically grounded review workflow:
- identify the core claims and likely rejection reasons
- audit novelty, value, rigor, clarity, and evidence sufficiency
- write a reliable review note, not vague prose
- produce a concrete revision plan
- produce a follow-up experiment TODO list only when the paper truly needs more evidence
- route the next step cleanly to
write,analysis-campaign,baseline,scout, ordecision
What ships with it
3 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.
- 8d ago First seen · 507 lines · 32 tokens per session scan A b11c062ef0c6
review is a skill published in the GitHub repository ResearAI/DeepScientist (3,319 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 32 tokens to every session and 4,892 once invoked, about $0.0002 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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