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 claw-ancestry-pcagit 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/claw-ancestry-pca)<a href="https://agentmods.dev/skills/aaaaqwq/agi-super-team/claw-ancestry-pca"><img src="https://agentmods.dev/badge/skills/aaaaqwq/agi-super-team/claw-ancestry-pca/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/claw-ancestry-pca"><img src="https://agentmods.dev/badge/skills/aaaaqwq/agi-super-team/claw-ancestry-pca.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 | $0.00020 | $0.01204 |
| Opus 5 | $0.00010 | $0.00602 |
| Sonnet 5 | $0.00004 | $0.00241 |
| Haiku 4.5 | $0.00002 | $0.00120 |
Grade A, and why
claw-ancestry-pca 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 6d 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:
- claw-ancestry-pca — 95% identical, 4 lines differ
How it starts
The opening of the file, as written. The whole thing — 146 lines — stays where its author put it; the contents beside it link to each section on GitHub.
🦖 Ancestry Decomposition PCA
Place your study cohort in global genetic context by computing a joint PCA against the Simons Genome Diversity Project (SGDP) — 345 samples from 164 populations spanning every inhabited continent.
What it does
- Takes your VCF + population map as input
- Finds common variants between your cohort and the SGDP reference panel (bundled)
- Runs PLINK PCA on the merged dataset
- Separates your cohort from SGDP reference samples
- Matches SGDP samples to their population labels (164 populations)
- Generates a publication-quality multi-panel figure:
- Panel A: PC1 vs PC2 — main population structure of your cohort
- Panel B: PC3 vs PC2 with regional groupings and confidence ellipses
- Panel C: PC3 vs PC1 with language/cultural groupings
- Panel D: Global context — your samples (circles) vs SGDP (triangles)
- Produces a markdown report with variance explained, population assignments, and reproducibility bundle
Why this exists
If you ask ChatGPT to "run a PCA against a global reference panel," it will:
- Not know which reference panel to use
- Hallucinate PLINK flags for merging datasets with different variant sets
- Skip IBD removal (related individuals distort PCA)
- Not normalise contig names between your VCF and the reference
- Produce a single scatter plot with no population labels
This skill encodes the correct methodological decisions:
- Uses SGDP (the gold-standard reference for global diversity)
- Handles contig normalisation (chr1 vs 1)
- Filters to common biallelic SNPs shared between datasets
- Removes related individuals via IBD checks
- Produces publication-quality multi-panel figures with confidence ellipses
- Differentiates your samples (circles) from reference (triangles)
Reference Panel
The skill bundles the SGDP v4 dataset (Mallick et al., 2016, Nature):
- 345 samples from 164 populations
- Whole-genome sequencing at high coverage
- MAF > 0.1% filter applied
- Populations span: Africa, Americas, Central/South Asia, East Asia, Europe, Middle East, Oceania
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.
- 6d ago First seen · 146 lines · 20 tokens per session scan A cc8199935491
claw-ancestry-pca is a skill published in the GitHub repository aAAaqwq/AGI-Super-Team (91 stars, last pushed yesterday), licensed MIT. It adds 20 tokens to every session and 1,204 once invoked, about $0.0001 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.
Other skills, from other repositories
claw-ancestry-pca
Ancestry decomposition PCA against the Simons Genome Diversity Project.
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