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-semantic-simgit 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-semantic-sim)<a href="https://agentmods.dev/skills/aaaaqwq/agi-super-team/claw-semantic-sim"><img src="https://agentmods.dev/badge/skills/aaaaqwq/agi-super-team/claw-semantic-sim/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-semantic-sim"><img src="https://agentmods.dev/badge/skills/aaaaqwq/agi-super-team/claw-semantic-sim.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 | $0.00021 | $0.01523 |
| Opus 5 | $0.00010 | $0.00762 |
| Sonnet 5 | $0.00004 | $0.00305 |
| Haiku 4.5 | $0.00002 | $0.00152 |
Grade A, and why
claw-semantic-sim 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 5d 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 — 163 lines — stays where its author put it; the contents beside it link to each section on GitHub.
🦖 Semantic Similarity Index
Measure how isolated or connected disease research is across the global biomedical literature, using PubMedBERT embeddings on PubMed abstracts spanning 175 GBD diseases.
What it does
- Takes a disease list (GBD taxonomy) as input
- Retrieves PubMed abstracts (2000-2025) for each disease with quality filtering
- Generates 768-dimensional PubMedBERT embeddings for every abstract
- Computes four semantic equity metrics per disease:
- Semantic Isolation Index (SII): average cosine distance to k-nearest disease neighbours; higher = more isolated, less connected research
- Knowledge Transfer Potential (KTP): cross-disease centroid similarity; higher = more potential for research spillover
- Research Clustering Coefficient (RCC): within-disease embedding variance; higher = more diverse research approaches
- Temporal Semantic Drift: cosine distance between yearly centroids; measures how research focus evolves
- Generates publication-quality multi-panel figures:
- Panel A: Semantic isolation by disease category (boxplot)
- Panel B: Top 20 most semantically isolated diseases (bar chart, NTD/Global South colour-coded)
- Panel C: Semantic isolation vs research volume (scatter with regression)
- Panel D: NTD vs non-NTD significance test (Welch's t-test, Cohen's d)
- Produces a markdown report with all metrics, rankings, and reproducibility bundle
Why this exists
If you ask ChatGPT to "measure research neglect for diseases," it will:
- Not know which embedding model to use for biomedical text
- Hallucinate metrics that sound plausible but have no methodological grounding
- Skip quality filtering (year coverage, abstract coverage, minimum papers)
- Not handle MPS acceleration or checkpointed batch processing
- Produce a single scatter plot with no disease classification
This skill encodes the correct methodological decisions:
- Uses PubMedBERT (the gold-standard biomedical language model)
- Fetches from PubMed with exponential backoff and NCBI rate limiting
- Quality filters: year coverage >= 70%, abstract coverage >= 95%, minimum 50 papers
- Batch embedding with Apple MPS acceleration and CPU fallback
- Checkpointed processing (resume after interruption)
- HDF5 storage with gzip compression and SHA-256 checksums
- Classification against WHO NTD list and Global South priority diseases
- Statistical significance testing (Welch's t-test, Cohen's d)
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.
- 5d ago First seen · 163 lines · 21 tokens per session scan A 4f2f53d1cb81
claw-semantic-sim is a skill published in the GitHub repository aAAaqwq/AGI-Super-Team (91 stars, last pushed yesterday), licensed MIT. It adds 21 tokens to every session and 1,523 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.
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