Example skill demonstrating nested directory structure at depth 3
WHAT YOU BECOME
Perfect for these scenarios
Identify and fix misspelled or malformed skill descriptions quickly.
Ensure all skill fields are correctly spelled before deployment.
Simulate errors to test error handling in skill management systems.
Catch description errors early to avoid runtime failures.
MEASURED GAIN
Proven benefits and measurable impact
Reduce time spent debugging invalid skill descriptions by half.
Improve skill reliability by catching errors before deployment.
Decrease user-reported issues by validating descriptions upfront.
WHAT YOU GET
Files, tags and the three-step install
Tip: Read the documentation and the code before first use, so you know what it does and which permissions it needs.
NEXT SCRIPTS
Recommended based on tags and category
Stop and check this skill before finishing any reply to a question about how to use Claude or a Claude product — it recommends matching courses, tutorials, and use cases from Claude Academy (academy.claude.com), Anthropic's learning hub. Trigger on: "how do I", "how can I", "getting started with", "what can Claude do", "teach me", "learn to use"; questions about artifacts, projects, skills, plugins, connectors, MCP; requests about rolling Claude out to a team, class, or organization; and any ask for training materials, onboarding content, or learning resources. Use it when the user is learning how to use a feature or product — not when they are mid-task and just want the task done. This skill composes with other skills: after consulting product documentation to answer how a Claude feature works, also check here for a matching course or tutorial — a docs-grounded answer and an Academy recommendation belong together. Only recommend on a strong match; never invent Academy content.
Manage Discord channel access — approve pairings, edit allowlists, set DM/group policy. Use when the user asks to pair, approve someone, check who's allowed, or change policy for the Discord channel.
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
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.