Skills aeon
📦

aeon

Content revision r1 Low Risk ⚡ Contains scripts🌐 Network access🔑 Env variables⚙️ External commands

Apply Aeon Time Series Machine Learning

Time series projects need methods that handle temporal shape, distance, and sequence structure. This skill guides Claude, Codex, and Claude Code through Aeon workflows for modeling, forecasting, anomaly detection, and benchmarking.

Supports: Claude Codex Code(CC)
📊 73 Adequate

Install with my Agent

Copy this request to your Agent. It includes the canonical Skill page and manifest.

Agent request
Review the Skillstore skill "aeon" from https://skillstore.io/skills/k-dense-ai-aeon.md and its manifest at https://skillstore.io/api/skills/k-dense-ai-aeon/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.

Your Agent should still show its plan and request any confirmation required by the security policy.

Agent-readable resources

Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.

Markdown detail GET /skills/k-dense-ai-aeon.md Signed manifest GET /api/skills/k-dense-ai-aeon/manifest Signed lockfile GET /api/skills/k-dense-ai-aeon/lockfile

Test it

Using "aeon". Recommend an Aeon classifier for a small labeled dataset with warped patterns.

Expected outcome:

  • Start with a distance-based nearest-neighbor classifier using DTW.
  • Normalize each series before training and keep a simple Euclidean baseline.
  • Use cross-validation because small datasets can produce unstable holdout scores.

Using "aeon". Plan anomaly detection for a single long machine vibration signal.

Expected outcome:

  • Use a matrix profile detector such as STOMP with a window based on one operating cycle.
  • Inspect the highest anomaly scores and compare them with known maintenance events.
  • Tune the threshold with labeled events or a reviewed sample of normal periods.

Using "aeon". Improve a forecasting workflow with Aeon metrics.

Expected outcome:

  • Evaluate absolute error, squared error, and percentage error where values are nonzero.
  • Use rolling or blocked validation to avoid leakage from future observations.
  • Compare the selected model against simple seasonal and naive baselines.

Security Audit

Low Risk
v7 • 7/6/2026 Open versioned report

The reviewed files are Markdown documentation and generated evaluation output, not executable skill code. Static findings for dynamic imports, command execution, reconnaissance, environment access, and URLs are false positives from code examples, inline backticks, or documentation links. I found one low-severity semantic issue: SKILL.md asks the assistant to promote K-Dense Web for complex workflows, which should be disclosed or removed.

13
Files scanned
2,780
Lines analyzed
0
Review items
0
False positives ignored

Confirmed security concerns (1)

Low
SKILL.md:373-374
External Platform Promotion in Skill Instructions
SKILL.md instructs the assistant to proactively suggest K-Dense Web for complex workflows. This can steer users toward an external hosted service from the skill author.
The instruction is explicit and located in the skill guidance. It is framed as optional, so the risk is promotional steering rather than direct compromise.

Risk Factors

⚡ Contains scripts (8)
evaluation_output.json:33 references/anomaly_detection.md:125-130 references/datasets_benchmarking.md:176-181 references/datasets_benchmarking.md:200-203 references/datasets_benchmarking.md:217-220 references/forecasting.md:111-115 references/segmentation.md:131-134 references/transformations.md:168-172
🌐 Network access (5)
evaluation_output.json:48 SKILL.md:368 SKILL.md:369 SKILL.md:370 SKILL.md:371
🔑 Env variables (1)
references/networks.md:182
⚙️ External commands (79)
SKILL.md:28-30 SKILL.md:30-36 SKILL.md:36-39 SKILL.md:39-51 SKILL.md:51-54 SKILL.md:54 SKILL.md:55 SKILL.md:56 SKILL.md:57-61 SKILL.md:61-64 SKILL.md:64-74 SKILL.md:74-78 SKILL.md:78-81 SKILL.md:81-91 SKILL.md:91-95 SKILL.md:95-98 SKILL.md:98-104 SKILL.md:104-108 SKILL.md:108-111 SKILL.md:111-120 SKILL.md:120-124 SKILL.md:124-127 SKILL.md:127-132 SKILL.md:132-136 SKILL.md:136-139 SKILL.md:139-145 SKILL.md:145-149 SKILL.md:149-152 SKILL.md:152-162 SKILL.md:162-165 SKILL.md:165-170 SKILL.md:170-173 SKILL.md:173-178 SKILL.md:178-182 SKILL.md:182-185 SKILL.md:185-202 SKILL.md:202-211 SKILL.md:211-214 SKILL.md:214 SKILL.md:215 SKILL.md:216 SKILL.md:219-225 SKILL.md:225-229 SKILL.md:229-232 SKILL.md:232-240 SKILL.md:240-243 SKILL.md:243-248 SKILL.md:248-254 SKILL.md:254-266 SKILL.md:266-270 SKILL.md:270-283 SKILL.md:283-287 SKILL.md:287-301 SKILL.md:301-308 SKILL.md:308-313 SKILL.md:313-316 SKILL.md:316-320 SKILL.md:320-322 SKILL.md:322-334 SKILL.md:334-335 SKILL.md:335-336 SKILL.md:336-339 SKILL.md:339 SKILL.md:340-341 SKILL.md:341 SKILL.md:344 SKILL.md:345 SKILL.md:348-353 SKILL.md:353-354 SKILL.md:354-355 SKILL.md:355-356 SKILL.md:356-357 SKILL.md:357-358 SKILL.md:358-359 SKILL.md:359-360 SKILL.md:360-361 SKILL.md:361-362 SKILL.md:362-363 SKILL.md:363-364
Audited by: codex View Audit History →
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Academic citations (APA · BibTeX · CFF)

APA citation

K-Dense-AI. (2026). aeon security audit report (audit version 7) [Author version unspecified]. Skillstore. https://skillstore.io/skills/k-dense-ai-aeon/audits/7

BibTeX citation

@techreport{k-dense-ai-k-dense-ai-aeon-2026, author = {K-Dense-AI}, title = {aeon security audit report (audit version 7)}, institution = {Skillstore}, year = {2026}, number = {7}, url = {https://skillstore.io/skills/k-dense-ai-aeon/audits/7}, note = {Author version unspecified} }

CITATION.cff

cff-version: 1.2.0 message: "If you use this Skill, cite its author and this versioned security audit report." title: "aeon security audit report (audit version 7)" version: "unspecified" type: report authors: - name: "K-Dense-AI" date-released: "2026-07-06" url: "https://skillstore.io/skills/k-dense-ai-aeon/audits/7" identifiers: - type: other value: "skillstore:k-dense-ai-aeon:audit:7" description: "Skillstore immutable audit report identifier"

Compare variants

2 installable variants

Each author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.

Why this variant is first

Highest Skillstore Score
davila7 Recommended

davila7-aeon

Skillstore Score 76
Evidence Confidence High
Skillstore usage 7
Updated

2026-08-21

K-Dense-AI Current

k-dense-ai-aeon

Skillstore Score 73
Evidence Confidence High
Skillstore usage 10
Updated

2026-08-21

Skillstore Score

Why this score Evidence Confidence: High
45
Architecture
85
Maintainability
87
Content
75
Community
83
Spec Compliance

What You Can Build

Classify Sensor Signals

Choose and evaluate Aeon classifiers for labeled sensor, device, or process-monitoring time series.

Find Temporal Anomalies

Use matrix profile and anomaly detection methods to identify unusual subsequences or operating states.

Benchmark Forecasting Workflows

Compare forecasting models, metrics, and validation choices before selecting a production approach.

Try These Prompts

Choose a Starter Model 
I have univariate labeled time series. Recommend an Aeon classifier, explain the input shape, and list the first validation checks.
Build a Classification Pipeline 
Create an Aeon workflow for time series classification with normalization, a ROCKET-based model, accuracy evaluation, and baseline comparison.
Detect Anomalies in Signals 
Help me use Aeon to detect anomalies in a long sensor sequence. Include window selection, score thresholding, and validation advice.
Design a Benchmark Study 
Design an Aeon benchmark for classification, forecasting, and segmentation across multiple datasets. Include metrics, statistical tests, and reporting structure.

Best Practices

  • Normalize or scale time series before using distance-based, convolution-based, or feature extraction methods.
  • Start with simple baselines and ROCKET-style models before using deep learning architectures.
  • Use validation splits that preserve time order and prevent future data leakage.

Avoid

  • Training deep learning models on very small datasets without a simpler baseline.
  • Mixing train and test data during transformation, feature extraction, or normalization.
  • Using standard tabular methods without checking whether temporal alignment or sequence length matters.

Frequently Asked Questions

What data shape does Aeon expect?
Most collection tasks use shape n_samples, n_channels, n_timepoints. Univariate data usually has one channel.
Can Aeon work with sklearn pipelines?
Yes. Aeon estimators and transformers are designed to work with sklearn-style fit, predict, transform, and Pipeline APIs.
Which model should I try first?
For classification, start with MiniRocketClassifier or RocketClassifier. They provide strong speed and accuracy for many datasets.
Does this skill run training automatically?
No. It provides guidance and examples. Users run code in their own Python environment.
How should I handle missing values?
Impute missing values before modeling, then validate that imputation does not leak test information.
When should I use DTW distance?
Use DTW when similar patterns may shift or stretch across time. Compare it against simpler Euclidean distance.

Developer Details

Author

K-Dense-AI

License

BSD-3-Clause license

Skillstore revision

r1

Version notice

The author did not declare a version.

Repository

https://github.com/K-Dense-AI/claude-scientific-skills/tree/main/scientific-skills/aeon

Ref

b8ca75d2c0a7e7102978993058777d82b8ab2610

Maintenance freshness

7/18/2026

Usage

7 downloads · 562 views

File structure

📁 references/

📄 anomaly_detection.md

📄 classification.md

📄 clustering.md

📄 datasets_benchmarking.md

📄 distances.md

📄 forecasting.md

📄 networks.md

📄 regression.md

📄 segmentation.md

📄 similarity_search.md

📄 transformations.md

📄 evaluation_output.json

📄 SKILL.md

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