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 temporal-modelsgit 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/temporal-models)<a href="https://agentmods.dev/skills/cuhk-aim-group/neuroclaw/temporal-models"><img src="https://agentmods.dev/badge/skills/cuhk-aim-group/neuroclaw/temporal-models/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/temporal-models"><img src="https://agentmods.dev/badge/skills/cuhk-aim-group/neuroclaw/temporal-models.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 | $0.00102 | $0.00935 |
| Opus 5 | $0.00051 | $0.00467 |
| Sonnet 5 | $0.00020 | $0.00187 |
| Haiku 4.5 | $0.00010 | $0.00093 |
Grade A, and why
temporal-models 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 — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Temporal Models Skill
Overview
temporal-models trains sequence encoders on ordered neuroimaging or clinical
measurements. It supports variable sequence lengths and estimates normalization
statistics from training subjects only.
Supported models
| Model | Encoder | Typical use |
|---|---|---|
lstm |
long short-term memory | longitudinal visits |
gru |
gated recurrent unit | compact recurrent baseline |
tcn |
temporal convolutional network | local temporal patterns |
transformer |
masked temporal self-attention | longer dependencies |
Both classification and regression are supported.
Installation
pip install numpy torch scikit-learn pandas
Verify:
python -c "import torch; print('CUDA:', torch.cuda.is_available())"
Workflows
1. Prepare an NPZ sequence bundle
X: float array [subjects, time, features]
y: array [subjects]
lengths: integer array [subjects] (optional)
subject_id: string array [subjects] (optional)
Padded frames must occur after each valid sequence. If lengths is absent,
every sequence is treated as fully valid.
import numpy as np
np.savez(
"sequences.npz",
X=X.astype("float32"),
y=y,
lengths=lengths,
subject_id=subject_ids,
)
2. Classification with GRU
python skills/temporal-models/scripts/train_reference.py \
--input sequences.npz \
--model gru \
--task classification \
--hidden-dim 64 \
--layers 2 \
--epochs 100 \
--batch-size 32 \
--folds 5 \
--device cuda \
--output-dir run_models_output/gru
3. Regression with temporal Transformer
python skills/temporal-models/scripts/train_reference.py \
--input sequences.npz \
--model transformer \
--task regression \
--hidden-dim 128 \
--layers 3 \
--dropout 0.2 \
--lr 0.001 \
--weight-decay 0.0001 \
--output-dir run_models_output/temporal_transformer
Use subject-level folds; never split frames or visits from one subject across training and test sets.
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 · 157 lines · 102 tokens per session scan A 7b2777e3ca59
temporal-models is a skill published in the GitHub repository CUHK-AIM-Group/NeuroClaw (85 stars, last pushed 6d ago), licensed MIT. It adds 102 tokens to every session and 935 once invoked, about $0.0005 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.
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