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 CUHK-AIM-Group/NeuroClaw --skill connectome-discoverygit clone --depth 1 https://github.com/CUHK-AIM-Group/NeuroClawWrote 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/cuhk-aim-group/neuroclaw/connectome-discovery)<a href="https://agentmods.dev/skills/cuhk-aim-group/neuroclaw/connectome-discovery"><img src="https://agentmods.dev/badge/skills/cuhk-aim-group/neuroclaw/connectome-discovery/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/cuhk-aim-group/neuroclaw/connectome-discovery"><img src="https://agentmods.dev/badge/skills/cuhk-aim-group/neuroclaw/connectome-discovery.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, 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 Agent Snooping · line 102 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Agent Snooping · line 103 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00089 | $0.00665 |
| Opus 5 | $0.00044 | $0.00332 |
| Sonnet 5 | $0.00018 | $0.00133 |
| Haiku 4.5 | $0.00009 | $0.00067 |
Grade A, and why
connectome-discovery 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 11d 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 — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Connectome Discovery Workflow
Overview
connectome-discovery is a scientific interpretation workflow, not a second
CPM implementation. It consumes fitted-model outputs or aligned network maps
and produces map similarities, empirical significance, and candidate target
rankings.
| Stage | Canonical owner |
|---|---|
| Connectome prediction | cpm, BrainGNN, BNT, or another model skill |
| Atlas/space validation | fmri-skill, nibabel-skill |
| Map similarity and permutation | models/connectome_discovery/mapping.py |
| Surface/network rendering | brain-visualization |
Installation
pip install numpy scipy pandas
Workflows
1. Generate model evidence
Run cpm or another connectome model and freeze its held-out predictions,
selected edges, atlas, and node ordering.
2. Align maps
Reference and candidate maps must use the same atlas, node order, hemisphere convention, and value orientation. Resampling or atlas mapping must be recorded.
3. Score and rank targets
Use models/connectome_discovery/mapping.py for:
cosine_similarity_mappermutation_pvaluerank_targets
Save the observed score, null distribution settings, permutation count, random seed, atlas, and coordinate space.
4. Visualize
Route final ROI/network values to brain-visualization. Do not infer an
anatomical target from an unlabeled edge vector.
Input / Output Summary
| Item | Format |
|---|---|
| Input | aligned ROI/network maps or model-derived connectome signatures |
| Statistics | cosine similarity and empirical permutation P value |
| Ranking | target identifier, similarity, rank, atlas/space metadata |
| Visualization | publication-ready network or surface map |
Testing
pytest models/tests/test_extended_models.py -q
Directory Reference
models/connectome_discovery/
├── __init__.py
└── mapping.py map similarity, permutation, and ranking
skills/connectome-discovery/
└── SKILL.md
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.
- 11d ago First seen · 110 lines · 89 tokens per session scan A fc80734b9791
connectome-discovery is a skill published in the GitHub repository CUHK-AIM-Group/NeuroClaw (84 stars, last pushed 5d ago), licensed MIT. It adds 89 tokens to every session and 665 once invoked, about $0.0004 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 2026-08-30.
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…
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…
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.
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…