Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/xiaobei930/cc-bestWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/xiaobei930/cc-best/analyze)<a href="https://agentmods.dev/commands/xiaobei930/cc-best/analyze"><img src="https://agentmods.dev/badge/commands/xiaobei930/cc-best/analyze/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/xiaobei930/cc-best/analyze"><img src="https://agentmods.dev/badge/commands/xiaobei930/cc-best/analyze.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00015 | $0.02390 |
| Opus 5.5 | $0.00006 | $0.00956 |
| Sonnet 5.5 | $0.00003 | $0.00478 |
| Haiku 4.5 | $0.00002 | $0.00239 |
Grade A, and why
analyze scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 339 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/analyze - 代码库分析
分析项目的 Git 历史和代码结构，提取编码模式和团队实践，生成可复用的知识文档。
用法
/cc-best:analyze # 分析当前代码库
/cc-best:analyze --commits 100 # 分析最近 100 次提交
/cc-best:analyze --output ./docs # 指定输出目录
/cc-best:analyze --domain api # 仅分析特定领域
角色定位
- 身份: 代码考古学家
- 目标: 从历史中发现隐藏的团队智慧
- 原则: 让沉淀的经验浮出水面
核心理念
代码库是团队智慧的结晶，Git 历史是团队习惯的日记
分析维度
1. 提交模式分析
# 获取提交消息模式
git log --oneline -n 200 | cut -d' ' -f2- | head -50
# 检测提交前缀
git log --oneline -n 200 | grep -oE "^[a-f0-9]+ (feat|fix|chore|docs|test|refactor):" | cut -d' ' -f2 | sort | uniq -c
检测目标:
- Conventional Commits 使用率
- 提交粒度（大提交 vs 小提交）
- 提交消息语言（中文/英文）
2. 文件共变分析
# 找出经常一起修改的文件
git log --oneline -n 200 --name-only --pretty=format:"" |
grep -v "^$" | sort | uniq -c | sort -rn | head -20
检测目标:
- 架构层关联（修改 API 时总是修改 types）
- 测试覆盖习惯（修改源码时是否修改测试）
- 文档同步习惯
3. 目录结构分析
检测项目架构风格:
├─ 按功能划分 (feature-based)
│ └─ src/features/auth/, src/features/user/
├─ 按层次划分 (layer-based)
│ └─ src/components/, src/services/, src/utils/
└─ 混合模式 (hybrid)
4. 命名约定分析
# 检测文件命名风格
find src -name "*.ts" -o -name "*.tsx" | xargs basename -a | sort | uniq
# 检测函数命名风格
grep -rE "^(export )?(async )?function [a-zA-Z]+" src/ --include="*.ts"
检测目标:
- 文件命名：PascalCase / camelCase / kebab-case
- 函数命名：动词前缀风格
- 常量命名：UPPER_SNAKE_CASE
5. 依赖使用分析
# 检测常用导入
grep -rh "^import" src/ | sort | uniq -c | sort -rn | head -20
检测目标:
- 首选的工具库（date-fns vs moment）
- 状态管理方案（Pinia vs Vuex vs Redux）
- HTTP 客户端（axios vs fetch）
工作流程
1. 环境检查
├─ 确认是 Git 仓库
├─ 检查提交数量
└─ 确定分析范围
2. 数据收集
├─ 提取 Git 历史
├─ 扫描目录结构
├─ 分析代码模式
└─ 统计使用频率
3. 模式识别
├─ 识别提交约定
├─ 识别架构模式
├─ 识别命名规范
└─ 识别工作流程
4. 知识生成
├─ 生成 SKILL.md（技能文档）
├─ 生成规则建议
└─ 输出置信度评估
5. 整合建议
├─ 建议更新 CLAUDE.md
├─ 建议创建 rules/*.md
└─ 建议添加 hooks
输出格式
分析报告
# 代码库分析报告
**分析日期**: 2026-01-27
**分析范围**: 最近 200 次提交
**代码库**: my-project
---
## 提交约定
| 模式 | 使用率 | 置信度 |
| -------------------- | ------ | -------------- |
| Conventional Commits | 87% | ████████░░ 85% |
| 中文提交消息 | 65% | ██████░░░░ 60% |
**建议**: 统一使用 Conventional Commits，消息使用中文描述
---
## 架构模式
检测到: **按功能划分 (feature-based)**
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 339 lines · 15 tokens per session scan A 8c13a967b6d6
analyze is a command published in the GitHub repository xiaobei930/cc-best (48 stars, last pushed 4mo ago), licensed MIT. It adds 15 tokens to every session and 2,390 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
audit-agents-skills
Audit quality of agents, skills, and commands in a Claude Code project.
audit-whitepapers
Audit version freshness, FR/EN parity, and metadata quality of all whitepapers and recap cards.
audit-repo-docs
Audit repository documentation against 85+ best practices from claude-code-ultimate-guide.
boldguy-linkedin-answer
Draft a LinkedIn comment on a post, referencing the guide's relevant section, in Flow style with AI markers removed.
methodology-advisor
Analyzes your codebase and asks 3 targeted questions to recommend the right AI-assisted development methodology stack.
security-audit
Comprehensive security audit of your project AND Claude Code configuration. Analyzes secrets exposure, injection surfaces, dependencies, hook security, and produces a scored security posture assessment.