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 asl-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/asl-skill)<a href="https://agentmods.dev/skills/cuhk-aim-group/neuroclaw/asl-skill"><img src="https://agentmods.dev/badge/skills/cuhk-aim-group/neuroclaw/asl-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/asl-skill"><img src="https://agentmods.dev/badge/skills/cuhk-aim-group/neuroclaw/asl-skill.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 | $0.00122 | $0.02097 |
| Opus 5 | $0.00061 | $0.01048 |
| Sonnet 5 | $0.00024 | $0.00419 |
| Haiku 4.5 | $0.00012 | $0.00210 |
Grade A, and why
asl-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 — 169 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ASL Skill (Modality Layer)
Overview
asl-skill is the NeuroClaw modality-layer interface skill responsible for all Arterial Spin Labeling (ASL) perfusion MRI data processing tasks.
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:
fsl-tool,nibabel-skill, andclaw-shell. - Companion scripts in
scripts/provide reference implementations for CBF quantification.
Core workflow (never bypassed):
- Identify input ASL data and labeling strategy (pCASL, CASL, or PASL).
- Ensure T1w structural data is available (via
smri-skillif not yet processed). - 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 (
asl_output/).
Research use only.
Quick Reference (Common ASL Tasks)
| Task | What needs to be done | Delegate to which tool skill | Expected output |
|---|---|---|---|
| ASL preprocessing | Motion correction, masking, registration to T1w | fsl-tool (ASL_PREPCORE) |
Preprocessed ASL in T1w space |
| M0 normalization | Divide ASL difference image by M0 reference image to get perfusion signal | fsl-tool or scripts/compute_cbf.py |
Normalized perfusion map |
| CBF quantification | Convert perfusion signal to absolute CBF (mL/100g/min) using Buxton model | scripts/compute_cbf.py |
CBF map (NIfTI) + ROI summary (CSV) |
| Partial volume correction | Correct CBF for gray/white matter partial volume effects | fsl-tool + tissue segmentation |
PVC-corrected CBF map |
| ASL-to-MNI normalization | Warp CBF map to MNI152 template for group analysis | fsl-tool (FNIRT) or smri-skill |
CBF in MNI152 space |
| ROI-based CBF extraction | Extract mean CBF from atlas-defined ROIs | fsl-tool + atlas |
Per-region CBF values (CSV) |
| Quality control | Check for outliers, low SNR, motion artifacts in ASL series | scripts/compute_cbf.py (--qc) |
QC report |
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.
- 11d ago First seen · 169 lines · 122 tokens per session scan A d1ec8bd111fc
asl-skill is a skill published in the GitHub repository CUHK-AIM-Group/NeuroClaw (84 stars, last pushed 4d ago), licensed MIT. It adds 122 tokens to every session and 2,097 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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