Evidence Skill — Data skill for Claude Code
Evidence BI Core 2026 knowledge, distilled from a real production cutover — 21 chapters: Chinese-schema SQL, nginx 5-patch, postgres_fdw cross-DB, theme.yaml truth, FastMCP self-host, report-page UI c.
How to install Evidence Skill
This entry records only its repository, not the path inside it, so there is no
exact command to give. Open kizemo/evidence-skill and copy the folder into
~/.claude/skills/, or the file into ~/.claude/agents/.
What Evidence Skill does
Evidence BI Core 2026 knowledge, distilled from a real production cutover — 21 chapters: Chinese-schema SQL, nginx 5-patch, postgres_fdw cross-DB, theme.yaml truth, FastMCP self-host, report-page UI contracts. LLM accuracy ~22% to ~98%.
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README
evidence-skill — Evidence BI Agent Skill
**English version**: see [README.en.md](README.en.md)
把一个 AI agent 在 **Evidence.dev / Evidence BI Core 2026** 上的工作准确度,从通用 LLM 的 **~22%** 拉到 **生产级 ~98%**(9 类真实任务 A/B 实测,见下文)。 来源: **2026-10-12 production cutover of bi.jury.xin (Sales BI)** + 2026-10-14 R 系列全面调研 + **2026-10-15 全站 docs 镜像(168 页)** + 2026-10-21 报表页 UI 契约(ch21)+ **2026-10-25 token 瘦身(触发成本 -42%)**。
[](LICENSE) [](CHANGELOG.md) [](#2026-10-15-docs-mirror-fold-in-168-页) [](docs/token-efficiency-audit.md)
**60 秒上手**
npx skills add https://github.com/kizemo/evidence-skill --skill evidence-skill装到 `.agents/skills/evidence-skill/`,并为 Claude Code 建 symlink(20+ 个 harness 通用)。 装完直接问 agent:`"我的 Evidence 报表页图表不渲染,怎么排查?"` → 它会加载 ch21,给出 4 条「静默不渲染」契约。
目录 / Table of Contents
- 项目目标 (Project Goals)
- 背景故事 (Why This Skill)
- 核心作用 (What It Does)
- **对 Agent 使用 Evidence 准确度的提升 (Accuracy Boost)** ← 重点
- 关键内容 (Key Content)
- 数据规模与覆盖度 (Coverage)
- **Token 效率 (Token Efficiency)** ← 2026-10-25
- 使用方式 (How to Use)
- 安装 (Install)
- 文件结构 (File Inventory)
- 源数据 (Source)
- 维护与版本 (Maintenance)
- 贡献 (Contributing)
- License
项目目标
**evidence-skill** 是一个专精于 [Evidence.dev](https://docs.evidence.dev) BI 平台的 **agent skill**(可装载到任何支持 agent-skills 协议的 harness 上,如 Claude Code / Codex / Copilot CLI / OpenCode / Amp)。
它要做三件事,目标排序按重要性递减:
- 消灭常见错误 — 让 agent 不再重复 LLM 通用知识里的"幻觉",例如:
- 写
date_substr而不是TO_CHAR(date, 'YYYY-MM') - 写
kebab-caseURL `/components/scat
- 写
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