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 fmri-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/fmri-skill)<a href="https://agentmods.dev/skills/cuhk-aim-group/neuroclaw/fmri-skill"><img src="https://agentmods.dev/badge/skills/cuhk-aim-group/neuroclaw/fmri-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/fmri-skill"><img src="https://agentmods.dev/badge/skills/cuhk-aim-group/neuroclaw/fmri-skill.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 MCP Rug Pull · line 121 Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.Fix: Pin the image: image:tag or image@sha256:abc123
- medium MCP Rug Pull · line 160 Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.Fix: Pin the image: image:tag or image@sha256:abc123
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 | $0.05862 |
| Opus 5.5 | $0.00049 | $0.02345 |
| Sonnet 5.5 | $0.00024 | $0.01172 |
| Haiku 4.5 | $0.00012 | $0.00586 |
Grade A, and why
fmri-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 2mo 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 — 371 lines — stays where its author put it; the contents beside it link to each section on GitHub.
fMRI Skill (Modality Layer)
Overview
fmri-skill is the NeuroClaw modality-layer interface skill responsible for all fMRI data processing and analysis 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:
fmriprep-tool,hcppipeline-tool,conn-tool,fsl-tool,bids-organizer, andclaw-shell.
Core workflow (never bypassed):
- Identify input data (BIDS dataset or preprocessed BOLD files).
- Generate a numbered execution plan that clearly states 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 (
fmri_output/).
Benchmark-Facing Default Mainline
For benchmark-style prompts, choose the narrowest task-faithful fMRI route first and do not widen into unrelated branches just because multiple downstream tools are available.
- If the prompt is task fMRI or mentions events, contrasts, design matrices, conditions, first-level, second-level, FEAT, cope, or z-stat maps:
- Default to
BIDS -> fMRIPrep -> first-level GLM -> group-level GLM if requested. - Keep the answer on the GLM/statistical path.
- Do not introduce resting-state connectivity, CONN, PPI, DCM, or EEG branches unless the prompt explicitly asks for them.
- Default to
- If the prompt is resting-state or asks for ROI time series / connectivity:
- Default to
BIDS -> fMRIPrep -> XCP-D or ROI/connectivity extraction. - Do not introduce task-GLM steps unless the prompt explicitly asks for task analysis.
- If the prompt is an ADNI-like or other raw-data resting-state benchmark, keep the answer on the narrow mainline
raw data -> minimal BIDS organization -> fMRIPrep -> resting-state ROI/connectivity outputs. - Do not expand the primary solution into EEG branches, CONN, effective connectivity, or broad multimodal orchestration unless the prompt explicitly asks for those branches.
- Default to
- If required task-fMRI inputs such as
events.tsv, contrasts, or condition timing are missing:- State
Missing required inputexplicitly. - Do not silently switch the task into a resting-state pipeline.
- State
- Do not delegate to unrelated modality skills such as EEG for fMRI-only tasks.
- In benchmark mode, do not make environment creation, broad project scaffolding, or long installation/setup sections the center of the answer when the task is asking for the executable imaging mainline.
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.
- 2mo ago First seen · 371 lines · 122 tokens per session scan A a733e4fd56e9
fmri-skill is a skill published in the GitHub repository CUHK-AIM-Group/NeuroClaw (85 stars, last pushed 2mo ago), licensed MIT. It adds 122 tokens to every session and 5,862 once invoked, about $0.0005 per session on Opus 5.5. 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.
fmri-skill compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| fmri-skillCUHK-AIM-Group/NeuroClaw | Skill | 85 | 2mo ago | MIT | A | 122 | Claude Code, Codex |
| instrument-data-to-allotropeanthropics/knowledge-work-plugins | Skill | 35k | 2d ago | Apache-2.0 | A | 123 | Claude Code |
| exploratory-data-analysisK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 83 | Claude Code |
| matlabK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 44 | Claude Code |
| phylogeneticsK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 71 | Claude Code, Codex |
| research-engineerdavila7/claude-code-templates | Skill | 33k | yesterday | MIT | A | 43 | Claude Code, Codex |
| mapping-to-snomedmaziyarpanahi/openmed | Skill | 5.8k | yesterday | Apache-2.0 | A | 205 | Claude Code |