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High-effort /code-review prompt that favors recall with three finder angles, recall-biased verification, and up to ten JSON findings
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/Lord1Egypt/ai-skillforge --skill agent-prompt-code-review-part-7-high-effort-mode命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
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.
基于 SOC 职业分类
正在显示 SKILL.md
| name | Agent Prompt: /code-review part 7 high effort mode |
| description | High-effort /code-review prompt that favors recall with three finder angles, recall-biased verification, and up to ten JSON findings |
| ccVersion | 2.1.152 |
| variables | ["DIFF_GATHERING_PHASE","AGENT_TOOL_NAME","BASE_FINDER_ANGLES_BLOCK","REUSE_FINDER_ANGLE_BLOCK","SIMPLIFICATION_FINDER_ANGLE_BLOCK","EFFICIENCY_FINDER_ANGLE_BLOCK","ALTITUDE_FINDER_ANGLE_BLOCK","CLEANUP_AND_ALTITUDE_CANDIDATES_NOTE","RECALL_BIASED_VERIFY_PHASE","OUTPUT_FORMAT_FN"] |
| allowed-tools | Read Write Edit Bash |
| license | BSD-3-Clause license |
| metadata | {"skill-author":"Lord1Egypt"} |
high effort → 3+4 angles × 6 candidates → 1-vote verify (recall-biased) → ≤10 findings
You are reviewing for recall at high effort: catch every real bug a careful reviewer would catch in one sitting. At this level, catching real bugs matters more than avoiding false positives. Err on the side of surfacing.
${DIFF_GATHERING_PHASE}
Run 7 independent finder angles via the ${AGENT_TOOL_NAME} tool. Each
surfaces up to 6 candidate findings with file, line, a one-line
summary, and a concrete failure_scenario.
${BASE_FINDER_ANGLES_BLOCK} ${REUSE_FINDER_ANGLE_BLOCK} ${SIMPLIFICATION_FINDER_ANGLE_BLOCK} ${EFFICIENCY_FINDER_ANGLE_BLOCK} ${ALTITUDE_FINDER_ANGLE_BLOCK} ${CLEANUP_AND_ALTITUDE_CANDIDATES_NOTE} Pass every candidate with a nameable failure scenario through — finders that silently drop half-believed candidates bypass the verify step and are the dominant cause of misses.
${RECALL_BIASED_VERIFY_PHASE} ${OUTPUT_FORMAT_FN(10)}