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 camcan-skillgit 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/camcan-skill)<a href="https://agentmods.dev/skills/cuhk-aim-group/neuroclaw/camcan-skill"><img src="https://agentmods.dev/badge/skills/cuhk-aim-group/neuroclaw/camcan-skill/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/camcan-skill"><img src="https://agentmods.dev/badge/skills/cuhk-aim-group/neuroclaw/camcan-skill.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 | $0.00113 | $0.02205 |
| Opus 5 | $0.00056 | $0.01103 |
| Sonnet 5 | $0.00023 | $0.00441 |
| Haiku 4.5 | $0.00011 | $0.00220 |
Grade A, and why
camcan-skill 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 — 214 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cam-CAN Skill (Dataset-Orchestration Layer)
Overview
camcan-skill is the NeuroClaw orchestration skill for the Cam-CAN (Cambridge Centre for Ageing and Neuroscience) dataset.
It strictly follows the NeuroClaw hierarchical design principles:
- This skill only describes WHAT needs to be done and which tool skill to delegate to.
- It contains no implementation code or concrete commands.
- All concrete execution is delegated to existing base/tool skills via
claw-shell. - Companion scripts in
scripts/provide reference implementations for BIDS validation, phenotype extraction, and QC.
Core workflow (never bypassed):
- Identify input Cam-CAN data and target modalities.
- Generate a numbered execution plan clearly stating WHAT needs to be done and which tool skill will handle each step.
- Present the full plan, estimated runtime, resource requirements, and risks to the user and wait for explicit confirmation ("YES" / "execute" / "proceed").
- On confirmation, delegate every step to the appropriate skill via
claw-shell. - After execution, save all outputs in a clean directory structure (
camcan_output/).
Research use only.
Quick Reference
| Task | What needs to be done | Delegate to | Expected output |
|---|---|---|---|
| BIDS validation | Validate Cam-CAN BIDS structure | scripts/validate_camcan.py |
Validation report |
| sMRI processing | Brain extraction, tissue segmentation, cortical reconstruction | smri-skill |
smri_output/ derivatives |
| rs-fMRI processing | Preprocessing, denoising, connectivity | fmri-skill |
fmri_output/ derivatives |
| task-fMRI processing | Movie-watching task GLM, activation analysis | fmri-skill |
fmri_output/ task results |
| MEG processing | Source localization, time-frequency analysis | meg-skill |
meg_output/ TFR and source |
| Phenotype extraction | Cognitive, sensory, health measures | scripts/extract_camcan_phenotype.py |
Merged phenotype CSV |
| QC summary | Per-subject quality control | scripts/camcan_qc_summary.py |
QC summary + exclusion list |
What ships with it
3 files 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.
- 11d ago First seen · 214 lines · 113 tokens per session scan A a6f8e1e09f27
camcan-skill is a skill published in the GitHub repository CUHK-AIM-Group/NeuroClaw (84 stars, last pushed 5d ago), licensed MIT. It adds 113 tokens to every session and 2,205 once invoked, about $0.0006 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.
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