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 agentlas-ai/agentlas-desktop --skill genomic-intelligencegit clone --depth 1 https://github.com/agentlas-ai/agentlas-desktopWrote 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/agentlas-ai/agentlas-desktop/genomic-intelligence)<a href="https://agentmods.dev/skills/agentlas-ai/agentlas-desktop/genomic-intelligence"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/genomic-intelligence/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/agentlas-ai/agentlas-desktop/genomic-intelligence"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/genomic-intelligence.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 | $0.00150 | $0.05520 |
| Opus 5.5 | $0.00060 | $0.02208 |
| Sonnet 5.5 | $0.00030 | $0.01104 |
| Haiku 4.5 | $0.00015 | $0.00552 |
Grade A, and why
genomic-intelligence scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
r = requests.post(f"{BASE}/v1/tasks/{task}/predict", headers=HEADERS, json=body) This is a copy
98% identical to genomic-intelligence — 7 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 — 364 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Genomic Intelligence — DNA Sequence Models
Genomic Intelligence (GI) serves transformer DNA language models over six sequence-analysis tasks on managed GPUs. Give it a gene symbol, a genomic region, or a DNA/FASTA sequence; it returns structured predictions — promoter regions, splice sites, enhancer activity, chromatin state, expression (log TPM), and de-novo gene annotation. Nothing runs locally: no model weights, no GPU, no heavy Python stack. It is a thin client over a hosted, versioned inference API.
Official docs: docs.genomicintelligence.ai ·
REST contract at api.genomicintelligence.ai/v1/openapi.json ·
hosted MCP server at https://mcp.genomicintelligence.ai/mcp
When to use this skill
Use GI when the user has DNA and wants a model prediction:
- Find promoters in a genomic region (
promoter) - Predict splice donor/acceptor sites (
splice) - Score enhancer activity — developmental & housekeeping (
enhancer) - Annotate chromatin state across hundreds of tracks (
chromatin) - Predict expression as log(TPM+1) from a sequence + cell-type context (
expression) - Annotate genes/transcripts de novo, no reference needed (
annotation) - Find the genes in a region and predict each one's expression (composite)
Not for local alignment, variant calling, or file I/O — use a local tool (BioPython, bcftools) for those. GI is for model inference from sequence.
Research and development use. Not for clinical or diagnostic decisions.
Two ways to call GI
Hosted MCP server (keyless; preferred on MCP hosts)
GI hosts an MCP server at https://mcp.genomicintelligence.ai/mcp (Streamable
HTTP). When your agent host supports MCP, prefer it: it works keyless against
a rate- and concurrency-limited public demo tier, and an optional gi_ bearer
key raises those limits. It exposes acquisition tools that return a sequence handle
(sequence_ref) and predict_* tools that take that handle, so large sequences
stay out of the context. See MCP workflow below and
references/mcp.md.
What ships with it
4 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 · 364 lines · 150 tokens per session scan A bc3ec5c3fd64
genomic-intelligence is a skill published in the GitHub repository agentlas-ai/agentlas-desktop (6 stars, last pushed today), licensed Apache-2.0. It adds 150 tokens to every session and 5,520 once invoked, about $0.0006 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). It is 98% identical to genomic-intelligence, differing in 7 lines, and is treated as a copy.
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