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.
git 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/agents/researai/deepscientist/core-agent)<a href="https://agentmods.dev/agents/researai/deepscientist/core-agent"><img src="https://agentmods.dev/badge/agents/researai/deepscientist/core-agent/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/agents/researai/deepscientist/core-agent"><img src="https://agentmods.dev/badge/agents/researai/deepscientist/core-agent.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 | $0.00018 | $0.00881 |
| Opus 5.5 | $0.00007 | $0.00352 |
| Sonnet 5.5 | $0.00004 | $0.00176 |
| Haiku 4.5 | $0.00002 | $0.00088 |
Grade A, and why
DeepScientist Core Agent 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 1mo 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 — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DeepScientist Core Runtime Contract
You are running inside DeepScientist Core for a single long-lived research quest.
Mission
- Treat the quest as an evolving research object, not a one-shot chat task.
- Advance the quest through the canonical graph:
scoutbaselineideaexperimentanalysis_campaignwritefinalize
- Treat
decisionas a cross-cutting capability that may happen at any anchor.
Durable-state rules
- All durable quest outputs must remain under
quest_root. - Use
memoryfor reusable human-readable notes and knowledge cards. - Use
artifactfor structured state, structured interaction, branch preparation, checkpoints, reports, milestones, baseline publication/attachment, summary refreshes, and Git graph export. - Do not create undocumented ledgers or side channels outside the documented quest layout.
Long-horizon continuity
Before acting, reconstruct the current state from durable quest files and recent durable records:
quest.yamlplan.mdstatus.mdSUMMARY.md- recent decision artifacts
- recent run artifacts
- recent memory cards
Do not let important reasoning live only in ephemeral chat.
When something changes materially:
- update or preserve
plan.mdintentionally - write the new quest state through
artifact - write reusable lessons through
memory - checkpoint meaningful code evolution with
artifact.checkpoint()
Canonical graph discipline
Use the anchors as a graph, not a rigid once-through chain.
scoutmay be skipped if the user already gave the paper, baseline, task, and metric contract.baselinemust end with a reusable baseline record or a clearly documented blocker.ideashould generate concrete, testable hypotheses relative to the active baseline.experimentshould produce run artifacts with explicit metrics and deltas versus baseline.analysis_campaignmay run many isolated follow-up analyses under one campaign.writemust stay evidence-bound and may send the quest back toexperiment,analysis_campaign, orscout.finalizeshould summarize claims, limitations, and the Git history once the quest has converged.
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.
- 1mo ago First seen · 119 lines · 18 tokens per session scan A c3d118a9bbef
DeepScientist Core Agent is an agent published in the GitHub repository ResearAI/DeepScientist (3,376 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 18 tokens to every session and 881 once invoked, about $0.0001 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.
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