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 optimizegit 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/optimize)<a href="https://agentmods.dev/skills/researai/deepscientist/optimize"><img src="https://agentmods.dev/badge/skills/researai/deepscientist/optimize/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/optimize"><img src="https://agentmods.dev/badge/skills/researai/deepscientist/optimize.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 | $0.00032 | $0.02825 |
| Opus 5.5 | $0.00013 | $0.01130 |
| Sonnet 5.5 | $0.00006 | $0.00565 |
| Haiku 4.5 | $0.00003 | $0.00282 |
Grade A, and why
optimize 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 — 255 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Optimize
Use this skill for algorithm-first quests where the goal is the strongest justified optimization result rather than paper packaging. The goal is to move the frontier by one justified step at a time, not to generate a large pile of low-information candidates.
Match signals
Use optimize when:
- the quest is algorithm-first
- the baseline gate is already confirmed or waived
- the task has at least one plausible optimization direction
- multiple candidate directions exist and the system should rank them before promotion
- a durable line exists and the next step is to manage explore, exploit, fusion, debug, or stop
Do not use optimize when:
- the baseline gate is unresolved
- the main need is a paper draft, rebuttal, review, or finalize task
- the quest is still in broad literature scouting with no concrete optimization handle
- the real blocker is still idea-family selection rather than bounded optimization search inside an accepted family
One-sentence summary
Recover the current frontier, choose one optimize submode, advance one justified move, then record the new frontier or explicit stop condition.
Control workflow
- Recover the current frontier and recent durable optimization state. Read the frontier, recent memory, and current quest state before creating or promoting anything.
- Choose exactly one primary optimize submode for this pass. Keep the pass legible: one dominant optimize move, not several unrelated route changes.
- Keep the candidate slate or active pool small and differentiated. If the direction is still fuzzy, shape and rank branchless candidate briefs; if a durable line already exists, manage a bounded implementation pool inside that line.
- Promote or execute only bounded candidates with explicit evidence criteria. Promote only the strongest briefs into durable lines, and record implementation-level attempts separately from durable line creation.
- Route from evidence to exactly one dominant next action.
End in
explore,exploit,fusion,debug, orstop, and record that route durably.
What ships with it
13 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.
- references/brief-shaping-playbook.md 3.4 KB
- references/candidate-board-template.md 660 B
- references/candidate-ranking-template.md 1.0 KB
- references/codegen-route-playbook.md 1.4 KB
- references/debug-response-template.md 582 B
- references/frontier-review-template.md 595 B
- references/fusion-playbook.md 916 B
- references/method-brief-template.md 1.3 KB
- references/operational-guidance.md 23 KB
- references/optimization-memory-template.md 467 B
- references/optimize-checklist-template.md 924 B
- references/plateau-response-playbook.md 887 B
- references/prompt-patterns.md 1.3 KB
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 · 255 lines · 32 tokens per session scan A 495cd3cee9a5
optimize is a skill published in the GitHub repository ResearAI/DeepScientist (3,376 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 32 tokens to every session and 2,825 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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