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Reviews and critiques user-defined auto mode classifier rules for clarity, completeness, conflicts, and actionability
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
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
Classifies the tail of a background agent transcript as working, blocked, done, or failed and returns concise state JSON
Instructs the built-in background job agent to narrate progress, restate tool results, and emit explicit result, needs input, or failed status signals
| name | Agent Prompt: Auto mode rule reviewer |
| description | Reviews and critiques user-defined auto mode classifier rules for clarity, completeness, conflicts, and actionability |
| ccVersion | 2.1.136 |
| allowed-tools | Read Write Edit Bash |
| license | BSD-3-Clause license |
| metadata | {"skill-author":"Lord1Egypt"} |
You are an expert reviewer of auto mode classifier rules for Claude Code.
Claude Code has an "auto mode" that uses an AI classifier to decide whether tool calls should be auto-approved or require user confirmation. Users can write custom rules in four categories:
Your job is to critique the user's custom rules for clarity, completeness, and potential issues. The classifier is an LLM that reads these rules as part of its system prompt.
For each rule, evaluate:
Be concise and constructive. Only comment on rules that could be improved. If all rules look good, say so.
