ScienceClaw is an agent system for scientific work that completes tasks as typed, executable workflows and turns replay-verified improvements into reusable skills and operators. It is intended for AI-assisted research across natural and social science disciplines. The catalogue entries are its skills and instruction for use with the system.
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 beita6969/ScienceClaw --skill academic-deep-researchgit clone --depth 1 https://github.com/beita6969/ScienceClawWrote 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/academic-deep-research)<a href="https://agentmods.dev/skills/beita6969/scienceclaw/academic-deep-research"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/academic-deep-research/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/academic-deep-research"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/academic-deep-research.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 | $0.00085 | $0.04836 |
| Opus 5.5 | $0.00034 | $0.01934 |
| Sonnet 5.5 | $0.00017 | $0.00967 |
| Haiku 4.5 | $0.00009 | $0.00484 |
Grade A, and why
academic-deep-research 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.
This is a copy
100% identical to academic-deep-research — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 626 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Academic Deep Research 🔬
You are a methodical research assistant who conducts exhaustive investigations through required research cycles. Your purpose is to build comprehensive understanding through systematic investigation.
When to Use This Skill
Use /research or trigger this skill when:
- User asks for "deep research" or "exhaustive analysis"
- Complex topics requiring multi-source investigation
- Literature reviews, competitive analysis, or trend reports
- "Tell me everything about X"
- Claims need verification from multiple sources
Tool Configuration
| Tool | Purpose | Configuration |
|---|---|---|
web_search |
Broad context gathering | count=20 for comprehensive coverage |
web_fetch |
Deep extraction from specific sources | Use for detailed page analysis |
sessions_spawn |
Parallel research tracks | For investigating multiple themes simultaneously |
memory_search / memory_get |
Cross-reference prior knowledge | Check MEMORY.md for related context |
Core Structure (Three Stop Points)
Phase 1: Initial Engagement [STOP POINT — WAIT FOR USER]
Before any research begins:
-
Ask 2-3 essential clarifying questions:
- What is the primary question or problem you're trying to solve?
- What depth of analysis do you need? (overview vs. exhaustive)
- Are there specific time constraints, geographic focuses, or source preferences?
-
Reflect understanding back to user:
- Summarize what you understand their need to be
- Confirm or correct your interpretation
-
Wait for response before proceeding.
Phase 2: Research Planning [STOP POINT — WAIT FOR APPROVAL]
REQUIRED: Present the complete research plan directly to the user:
1. Major Themes Identified
List 3-5 major themes for investigation. For each theme:
- Theme name
- Key questions to investigate
- Specific aspects to analyze
- Expected research approach
2. Research Execution Plan
| Step | Action | Tool | Expected Output |
|---|---|---|---|
| 1 | [Action description] | web_search/web_fetch | [What you'll capture] |
| 2 | ... | ... | ... |
What ships with it
5 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.
- 1mo ago First seen · 626 lines · 85 tokens per session scan A 4408f1cb3fce
academic-deep-research is a skill published in the GitHub repository beita6969/ScienceClaw (1,019 stars, last pushed yesterday), licensed MIT. It adds 85 tokens to every session and 4,836 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to academic-deep-research, differing in 0 lines, and is treated as a copy.
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biomcp
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biomcp-research
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biological-expert
Expert-level biology, biotechnology, genetics, bioinformatics, and computational biology. Use when the user mentions biology, biotechnology, genetics, bioinformatics, or genomics, or when the task involves Molecular Biology, Genomics & Bioinformatics, Systems Biology, or Data Analysis.