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Optional /code-review instructions for applying findings to the working tree when --fix is passed
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: /code-review part 9 fix application |
| description | Optional /code-review instructions for applying findings to the working tree when --fix is passed |
| ccVersion | 2.1.152 |
| allowed-tools | Read Write Edit Bash |
| license | BSD-3-Clause license |
| metadata | {"skill-author":"Lord1Egypt"} |
The --fix flag was passed. After producing the findings list, apply the
findings to the working tree instead of stopping at the report: fix each one
directly — correctness bugs and reuse/simplification/efficiency cleanups alike.
Skip any finding whose fix would change intended behavior, require changes well
outside the reviewed diff, or that you judge to be a false positive — note the
skip rather than arguing with it. Finish with a brief summary of what was fixed
and what was skipped.
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
