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 GGbond-bo/MemOmics-Agent --skill cross-species-regulatory-conservationgit clone --depth 1 https://github.com/GGbond-bo/MemOmics-AgentWrote 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/ggbond-bo/memomics-agent/cross-species-regulatory-conservation)<a href="https://agentmods.dev/skills/ggbond-bo/memomics-agent/cross-species-regulatory-conservation"><img src="https://agentmods.dev/badge/skills/ggbond-bo/memomics-agent/cross-species-regulatory-conservation/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/ggbond-bo/memomics-agent/cross-species-regulatory-conservation"><img src="https://agentmods.dev/badge/skills/ggbond-bo/memomics-agent/cross-species-regulatory-conservation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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 | $0.00142 | $0.02843 |
| Opus 5 | $0.00071 | $0.01422 |
| Sonnet 5 | $0.00028 | $0.00569 |
| Haiku 4.5 | $0.00014 | $0.00284 |
Grade A, and why
cross-species-regulatory-conservation 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 9d 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 — 248 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cross-Species Regulatory Element Conservation Assessment (CRCA)
Overview
This skill covers the five-layer CRCA framework for evaluating whether a gene's regulatory program is conserved between species — going beyond expression-level comparison to answer: "is the monkey model's gene regulation sufficiently similar to human for drug target validation?"
Core insight: Expression conservation ≠ regulatory conservation. The POU5F1 case from CroCoNet (2025 preprint) proves this — identical mRNA levels but maximally divergent TF driver networks. This framework is the first to systematically detect such "B-class genes."
Five-Layer Framework
R1: Sequence Conservation Layer (no ATAC needed)
Input: Genome sequences (GRCh38 + rheMac10) + JASPAR motifs
Methods:
- Promoter extraction (TSS ±2kb) for 1:1 orthologs
- liftOver coordinate mapping
- phastCons/phyloP conservation scoring
- TF motif scanning (JASPAR): presence/absence, position, copy number
Output: S_seq — per-gene sequence conservation score
R2: CRE Chromatin Accessibility Layer (ATAC-driven)
Input: Cross-species ATAC-seq (monkey + human hippocampus)
Methods:
- Peak calling (MACS2 via Signac/ArchR)
- liftOver peak coordinates (rheMac10 → hg38)
- R2a: Peak overlap rate (Jaccard index, bp-level)
- R2b: Signal intensity correlation (Spearman ρ per peak)
- R2c: Cell-type specificity conservation (same CRE open in same cell type?)
- R2d: Aging dynamics — species×age interaction on CRE accessibility change
Output: S_cre — per-gene CRE accessibility conservation score
ArchR advantage over Signac: co-accessibility analysis
(can detect enhancer-promoter linkage conservation — Signac cannot).
R3: TF Binding Dynamics Layer (ATAC-driven)
Input: Cross-species ATAC-seq
Methods:
- TF footprinting (TOBIAS/HINT-ATAC or Signac Footprint)
- Motif enrichment in age-varying CREs
- Cross-species comparison of footprint depth/specificity
Output: S_tf — TF binding conservation score
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.
- 9d ago First seen · 248 lines · 142 tokens per session scan A 9040723a9f14
cross-species-regulatory-conservation is a skill published in the GitHub repository GGbond-bo/MemOmics-Agent (19 stars, last pushed 2d ago), licensed MIT. It adds 142 tokens to every session and 2,843 once invoked, about $0.0007 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 2026-09-03.
Other skills, from other repositories
instrument-data-to-allotrope
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…
matlab
Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.
exploratory-data-analysis
Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…
phylogenetics
Build and analyze phylogenetic trees using MAFFT (multiple alignment), IQ-TREE 2 (maximum likelihood), and FastTree (fast NJ/ML). Visualize with ETE3 or FigTree. For evolutionary analysis, microbial genomics, viral phylodynamics, protein family analysis, and molecular clock studies.
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
mapping-to-snomed
Maps clinical concept spans extracted by OpenMed to SNOMED CT concepts through a USER-SUPPLIED terminology server (the user's own Ontoserver, Snowstorm, or UMLS/UTS), never a bundled vocabulary. Use when the user wants to code findings, disorders, procedures, body structures, or substances to SNOMED CT, run an ECL…