Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/beita6969/ScienceClawnpx agentmods add skills/beita6969/scienceclaw/deep-research-swarmWrote 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/beita6969/scienceclaw/deep-research-swarm)<a href="https://agentmods.dev/skills/beita6969/scienceclaw/deep-research-swarm"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/deep-research-swarm/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/beita6969/scienceclaw/deep-research-swarm"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/deep-research-swarm.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
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 →
- high Prompt Injection · line 6 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
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 | $0.00009 | $0.00431 |
| Opus 5 | $0.00005 | $0.00216 |
| Sonnet 5 | $0.00002 | $0.00086 |
| Haiku 4.5 | $0.00001 | $0.00043 |
Grade A, and why
deep-research-swarm 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.
What it actually says
name: deep-research-swarm description: Multi-agent research literature analysis keywords:
- research
- literature
- swarm
- multi-agent
- hypothesis measurable_outcome: Generates comprehensive literature review with >50 citations in <5 minutes. license: MIT metadata: author: Biomedical OS Team version: "1.0.0" compatibility:
- system: Python 3.10+ allowed-tools:
- run_shell_command
- read_file
- google_web_search
DeepResearch Swarm
A coordinated swarm of agents designed to perform deep, parallelized research into biomedical literature, aggregating findings into comprehensive reports.
When to Use This Skill
- When you need an exhaustive review of a specific medical topic.
- When connecting disparate pieces of evidence across thousands of papers.
- When generating hypotheses based on recent literature.
Core Capabilities
- Parallel Search: Querying multiple databases simultaneously.
- Evidence Synthesis: Combining facts into a coherent narrative.
- Citation Verification: Ensuring all claims are backed by sources.
Example Usage
User: "Research the latest advancements in mRNA cancer vaccines."
Agent Action:
python3 src/research/agents/agent_coordinator.py --topic "mRNA cancer vaccines" --depth "deep"
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 · 67 lines · 9 tokens per session scan A cf7772943a89
deep-research-swarm is a skill published in the GitHub repository beita6969/ScienceClaw (898 stars, last pushed 3mo ago), licensed MIT. It adds 9 tokens to every session and 431 once invoked, about $0.0000 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 2026-09-03.
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