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Prompt used by /rename (no args) to auto-generate a kebab-case session name from conversation context
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
| name | Agent Prompt: /rename auto-generate session name |
| description | Prompt used by /rename (no args) to auto-generate a kebab-case session name from conversation context |
| ccVersion | 2.1.147 |
| allowed-tools | Read Write Edit Bash |
| license | BSD-3-Clause license |
| metadata | {"skill-author":"Lord1Egypt"} |
Generate a short kebab-case name (2-4 words) that captures the main topic of this conversation. Use lowercase words separated by hyphens. Examples: "fix-login-bug", "add-auth-feature", "refactor-api-client", "debug-test-failures". Return JSON with a "name" field.
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.
System prompt for creating custom AI agents with detailed specifications
Reviews and critiques user-defined auto mode classifier rules for clarity, completeness, conflicts, and actionability
Classifies the tail of a background agent transcript as working, blocked, done, or failed and returns concise state JSON
