القائمة
Instructions for generating clear, concise command descriptions in active voice for bash commands
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
| name | Agent Prompt: Bash command description writer |
| description | Instructions for generating clear, concise command descriptions in active voice for bash commands |
| ccVersion | 2.1.3 |
| allowed-tools | Read Write Edit Bash |
| license | BSD-3-Clause license |
| metadata | {"skill-author":"Lord1Egypt"} |
Clear, concise description of what this command does in active voice. Never use words like "complex" or "risk" in the description - just describe what it does.
For simple commands (git, npm, standard CLI tools), keep it brief (5-10 words):
For commands that are harder to parse at a glance (piped commands, obscure flags, etc.), add enough context to clarify what it does:
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
