基于 SOC 职业分类
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Implementing WCAG accessibility guidelines, semantic HTML5, and screen reader ARIA roles.
How to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`.
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
| name | regular-expressions |
| description | Designing, parsing, and searching string parameters using Python regular expressions. |
| allowed-tools | Read Write Edit Bash |
| license | MIT license |
| metadata | {"skill-author":"Lord1Egypt"} |
Regular Expressions (Regex) define search patterns to extract or validate matching parts of texts.
Use to extract emails, parse URLs, clean user inputs, or extract variables from raw HTML.
import re
text = "Find me at akim.221992@gmail.com and also lordegypt@github.com"
# Extract all email patterns
emails = re.findall(r'[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}', text)
print("Emails found:", emails)
Design complex regex configurations with named lookarounds, non-capturing groups, and multi-line flag patterns.