Borrowing it
Nothing to install: this file belongs to cacr92/WeReply. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/cacr92/WeReply/main/.claude/commands/context-prime.mdgit clone --depth 1 https://github.com/cacr92/WeReplyWrote 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/cacr92/wereply/context-prime)<a href="https://agentmods.dev/commands/cacr92/wereply/context-prime"><img src="https://agentmods.dev/badge/commands/cacr92/wereply/context-prime.svg" alt="Measured on agentmods" 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 | $0.00000 | $0.01044 |
| Opus 5 | $0.00000 | $0.00522 |
| Sonnet 5 | $0.00000 | $0.00209 |
| Haiku 4.5 | $0.00000 | $0.00104 |
Grade A, and why
context-prime 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 6d 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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/context-prime - 项目上下文加载
用途
快速加载项目结构和关键信息，建立开发上下文。
加载内容
1. 项目概览
## 项目信息
- 名称: CaCrFeedFormula（饲料配方优化系统）
- 类型: Tauri 桌面应用
- 技术栈: Rust 2021 + React 19 + TypeScript 5.8
- 数据库: SQLite + SQLx
- 优化引擎: HiGHS 1.12
2. 项目结构
cacrfeedformula/
├── src/ # Rust 后端
│ ├── ai/ # AI 服务
│ ├── database/ # 数据库管理
│ ├── formula/ # 配方优化核心
│ ├── material/ # 原料管理
│ ├── species/ # 品种管理
│ ├── factory/ # 工厂管理
│ ├── premix/ # 预混料设计
│ ├── profit/ # 盈亏测算
│ ├── prediction/ # 营养预测
│ └── production_batch/ # 生产批次
├── frontend/ # React 前端
│ └── src/
│ ├── components/ # 组件
│ └── bindings.ts # 类型绑定
├── migrations/ # 数据库迁移
└── .claude/ # Claude Code ���置
├── rules/ # 开发规范
├── skills/ # 技能文件
├── commands/ # 命令文件
└── hooks/ # 钩子配置
3. 核心功能模块
- 配方优化: 线性规划优化、手工配方、预混料反向计算
- 数据管理: 原料、品种、工厂、生产批次、库存
- 分析决策: 盈亏测算、营养预测、敏感性分析
- AI 助手: 上下文感知的专业配方咨询
4. 关键规范
- 禁止
console.log（使用message组件） - 禁止
as any（使用精确类型） - 使用 SQLx 参数化查询
- 使用 specta 类型绑定
- 80% 测试覆盖率要求
5. 开发工作流
1. 需求分析
2. 任务分解
3. 创建 TodoWrite
4. 执行前说明
5. 标记完成状态
6. 逻辑验证
7. 代码审查
6. 提交前检查
- cargo clippy 无警告
- npm run lint 无错误
- 测试覆盖率 >= 80%
- 无硬编码密钥
- 已更新 CHANGELOG.md
- 使用 LSP 评估影响
快速参考
Rust 命令
cargo clippy # 代码检查
cargo test # 运行测试
cargo tarpaulin # 测试覆盖率
cargo audit # 安全审计
cargo run # 运行应用
TypeScript 命令
npm run lint # ESLint 检查
npm test # 运行测试
npm run test:coverage # 测试覆盖率
npm run dev # 开发模式
npm run build # 构建
Tauri 命令
cargo tauri dev # 开发模式
cargo tauri build # 构建应用
相关文档
.claude/CLAUDE.md- 核心规则.claude/rules/- 详细规范.claude/INTEGRATION_SUMMARY.md- 配置整合说明CHANGELOG.md- 变更日志
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.
- 6d ago First seen · 126 lines · 0 tokens per session scan A 3535ac1ff13f
context-prime is a command published in the GitHub repository cacr92/WeReply (6 stars, last pushed 7mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,044 tokens. 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 2026-08-31.
Other commands, from other repositories
minutes-ideas
Surface recent voice memos and ideas captured from any device. Use when the user asks "what ideas did I have?", "what were my recent memos?", "what did I record while walking?", or wants to recall a captured thought.
learn
Force claude-smart to extract learnings from this session now.
memory-store
Store an insight, decision, or pattern to memory.
cc-memory
Configure persistent memory that survives across sessions using a layered approach: split rule files for always-loaded context, auto-memory for organic learning, and optional MCP-backed long-term memory for large codebases.
analyze-context
USE WHEN you want to analyze project context before starting work on a task. Calls context + recall, then synthesizes goals, decisions, gotchas, and relevant memories into a pre-task brief.
lians-recall
Recall current (non-stale) facts from Lians memory, optionally as-of a past date.