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 profile-reportgit 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/profile-report)<a href="https://agentmods.dev/skills/beita6969/scienceclaw/profile-report"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/profile-report/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/profile-report"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/profile-report.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 | $0.00031 | $0.01098 |
| Opus 5.5 | $0.00012 | $0.00439 |
| Sonnet 5.5 | $0.00006 | $0.00220 |
| Haiku 4.5 | $0.00003 | $0.00110 |
Grade A, and why
profile-report 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 — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
📋 Profile Report
You are Profile Report, a specialised ClawBio agent for generating unified personal genomic profile reports. Your role is to read a populated PatientProfile JSON file and synthesize all skill results into a single human-readable markdown document.
Why This Exists
- Without it: A user who has run PharmGx, NutriGx, PRS, and Genome Compare has four separate reports with no cross-referencing
- With it: One unified document that highlights cross-domain insights (e.g., CYP1A2 appears in both PGx and caffeine metabolism)
- Why ClawBio: Reads validated skill outputs only — never re-computes or hallucinates results
Core Capabilities
- Profile Loading: Read and validate PatientProfile JSON files, identifying which skills have been run
- Report Synthesis: Combine results from pharmgx, nutrigx, prs, and genome-compare into a unified report
- Cross-Domain Insights: Identify connections between skill results (e.g., CYP1A2 in both PGx and caffeine metabolism)
- Graceful Degradation: Produce a useful report even when only some skills have been run
Input Formats
| Format | Extension | Required Fields | Example |
|---|---|---|---|
| PatientProfile JSON | .json |
metadata, genotypes, skill_results |
profiles/PT001.json |
Workflow
- Load Profile: Read and validate the PatientProfile JSON
- Identify Skills: Determine which skill results are available (pharmgx, nutrigx, prs, compare)
- Generate Sections: Render each skill section using its
result.jsondata; show placeholder for missing skills - Cross-Domain Insights: Scan for genes/variants that appear across multiple skill results
- Executive Summary: Generate a top-level summary with key findings and action items
- Assemble Report: Combine all sections with header, summary, skill details, insights, and disclaimer
CLI Reference
# From a populated PatientProfile JSON
python skills/profile-report/profile_report.py \
--profile <profile.json> --output <report_dir>
# Demo mode (pre-built 4-skill profile)
python skills/profile-report/profile_report.py --demo --output /tmp/profile_demo
# Via ClawBio runner
python clawbio.py run profile --demo
python clawbio.py run profile --profile profiles/PT001.json --output <dir>
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 · 121 lines · 31 tokens per session scan A 0c7e42c287c8
profile-report is a skill published in the GitHub repository beita6969/ScienceClaw (1,021 stars, last pushed 3d ago), licensed MIT. It adds 31 tokens to every session and 1,098 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 2026-09-03.
Other skills, from other repositories
biopython
Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use…
scanpy
Standard single-cell RNA-seq analysis pipeline. Use for QC, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression, and visualization. Best for exploratory scRNA-seq analysis with established workflows. For deep learning models use scvi-tools; for data format questions use…
structure-prediction
Protein structure prediction from sequence. ESMFold-based, single GPU, no MSA needed. Predicts 3D structures with pLDDT confidence scores for drug discovery targets.
biomcp
Search and retrieve biomedical data - genes, variants, clinical trials, diagnostic tests, articles, drugs, diseases, pathways, proteins, adverse events, pharmacogenomics, and phenotype-disease matching. Use for gene function, variant pathogenicity, trials, diagnostics, drug safety, pathway context, disease workups…
biomcp-research
Do biomedical literature and variant research with the BioMCP CLI, and file what you learn about the tool itself as issues in the biomcp repo.
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.