紫苏叶方法 Perilla-Leaf Method · 通用「证据→推断」判断法 Claude skill — judge anything (good/bad, A vs B) by chaining evidence→inference, with a minimal HTML reasoning-path diagram
「紫苏叶方法」（Perilla-Leaf Method）是一个通用「证据→推断」判断法 Claude 技能，旨在帮助您对任意事物（好/坏、A vs B）进行结构化、透明化的判断。它通过显式地链式推理证据和推断，生成一个最小化HTML推理路径图，使AI的决策过程一目了然。该技能支持Claude Code、Cursor、Codex等主流AI代理平台，特别适合需要可解释AI判断的场景，如代码审查、方案选型、内容审核等。其工作原理基于前端推理链：用户先提出判断议题，AI依次收集证据（如事实、数据、观察）→形成推断（如逻辑推理、因果分析、权衡）→递进式构建推理图，最终输出一个内嵌HTML的思维可视化报告。
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紫苏叶方法 Perilla-Leaf Method · 通用「证据→推断」判断法 Claude skill — judge anything (good/bad, A vs B) by chaining evidence→inference, with a minimal HTML reasoning-path diagram
来源：https://github.com/huangguang1999/perilla-leaf-method
推送时间：2026-06-02T05:59:42Z
USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.
Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.
Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.
The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
Agent skill repository discovered by 10x-chat research.
Review backend code for quality, security, maintainability, and best practices based on established checklist rules. Use when the user requests a review, analysis, or improvement of backend files (e.g., `.py`) under the `api/` directory. Do NOT use for frontend files (e.g., `.tsx`, `.ts`, `.js`). Supports pending-change review, code snippets review, and file-focused review.