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 intake-auditgit 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/intake-audit)<a href="https://agentmods.dev/skills/researai/deepscientist/intake-audit"><img src="https://agentmods.dev/badge/skills/researai/deepscientist/intake-audit/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/intake-audit"><img src="https://agentmods.dev/badge/skills/researai/deepscientist/intake-audit.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 | $0.00046 | $0.02717 |
| Opus 5 | $0.00023 | $0.01358 |
| Sonnet 5 | $0.00009 | $0.00543 |
| Haiku 4.5 | $0.00005 | $0.00272 |
Grade A, and why
intake-audit 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 9d 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 — 321 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Intake Audit
Use this skill when the quest already has meaningful state and the first job is to normalize that state instead of restarting the canonical research loop from zero. The goal is to recover one trustworthy starting state from messy existing assets, not to re-audit everything forever.
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.
- Message templates are references only. Adapt to the actual context and vary wording so updates feel natural and non-robotic.
- If a threaded user reply arrives, interpret it relative to the latest intake-audit progress update before assuming the task changed completely.
- When the audit reaches a durable route recommendation, send one richer
artifact.interact(kind='milestone', reply_mode='threaded', ...)update that says what state is trusted, what still needs work, and which anchor should run next.
Tool discipline
- Do not use native
shell_command/command_executionin this skill. - Any shell, CLI, Python, bash, node, git, npm, uv, or repo-audit execution must go through
bash_exec(...). - For git inspection or maintenance inside the current quest repository or worktree, prefer
artifact.git(...)before raw shell git commands. - Use shell execution only when durable quest files, artifacts, and memory are insufficient; do not bypass durable state just because shell feels faster.
Three-layer todo contract
- treat quest-root
plan.mdas the top-level research map whose next active node must become explicit after intake - if the audit is multi-step, use workspace
PLAN.mdas the current intake-node contract andCHECKLIST.mdas the execution frontier - when the audit resolves the route, update quest-root
plan.mdinstead of leaving the recommendation only in a report artifact
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.
- 9d ago First seen · 321 lines · 46 tokens per session scan A 2ac59d8695b1
intake-audit is a skill published in the GitHub repository ResearAI/DeepScientist (3,319 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 46 tokens to every session and 2,717 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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