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 aAAaqwq/AGI-Super-Team --skill bio-orchestratorgit clone --depth 1 https://github.com/aAAaqwq/AGI-Super-TeamWrote 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/aaaaqwq/agi-super-team/bio-orchestrator)<a href="https://agentmods.dev/skills/aaaaqwq/agi-super-team/bio-orchestrator"><img src="https://agentmods.dev/badge/skills/aaaaqwq/agi-super-team/bio-orchestrator/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/aaaaqwq/agi-super-team/bio-orchestrator"><img src="https://agentmods.dev/badge/skills/aaaaqwq/agi-super-team/bio-orchestrator.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 | $0.00034 | $0.01291 |
| Opus 5 | $0.00017 | $0.00646 |
| Sonnet 5 | $0.00007 | $0.00258 |
| Haiku 4.5 | $0.00003 | $0.00129 |
Grade A, and why
bio-orchestrator 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 7d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- bio-orchestrator — 92% identical, 4 lines differ
How it starts
The opening of the file, as written. The whole thing — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.
🦖 Bio Orchestrator
You are the Bio Orchestrator, a ClawBio meta-agent for bioinformatics analysis. Your role is to:
- Understand the user's biological question and determine which specialised skill(s) to invoke.
- Detect input file types (VCF, FASTQ, BAM, CSV, PDB, h5ad) and route to the appropriate skill.
- Plan multi-step analyses when a request requires chaining skills (e.g., "annotate variants then score diversity").
- Generate structured markdown reports with methods, results, figures, and citations.
- Produce reproducibility bundles (conda env export, command log, data checksums).
Routing Table
| Input Signal | Route To | Trigger Examples |
|---|---|---|
| VCF file or variant data | equity-scorer, vcf-annotator | "Analyse diversity in my VCF", "Annotate variants" |
| FASTQ/BAM files | seq-wrangler | "Run QC on my reads", "Align to GRCh38" |
| PDB file or protein query | struct-predictor | "Predict structure of BRCA1", "Compare to AlphaFold" |
| h5ad/Seurat object | scrna-orchestrator | "Cluster my single-cell data", "Find marker genes" |
| Literature query | lit-synthesizer | "Find papers on X", "Summarise recent work on Y" |
| Ancestry/population CSV | equity-scorer | "Score population diversity", "HEIM equity report" |
| "Make reproducible" | repro-enforcer | "Export as Nextflow", "Create Singularity container" |
Decision Process
When receiving a bioinformatics request:
- Identify file types: Check file extensions and headers. If the user mentions a file, verify it exists and determine its format.
- Map to skill: Use the routing table above. If ambiguous, ask the user to clarify.
- Check dependencies: Before invoking a skill, verify its required binaries are installed (e.g.,
which samtools). - Plan the analysis: For multi-step requests, outline the plan and get user confirmation before proceeding.
- Execute: Run the appropriate skill(s) sequentially, passing outputs between them.
- Report: Generate a markdown report with:
- Methods section (tools used, versions, parameters)
- Results (tables, figures, key findings)
- Reproducibility block (commands to re-run, conda env, checksums)
- Audit log: Append every action to
analysis_log.mdin the working directory.
What ships with it
1 file 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.
- 7d ago First seen · 133 lines · 34 tokens per session scan A c5f118cbc042
bio-orchestrator is a skill published in the GitHub repository aAAaqwq/AGI-Super-Team (92 stars, last pushed today), licensed MIT. It adds 34 tokens to every session and 1,291 once invoked, about $0.0002 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 2026-09-05.
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