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.
npx skills add nongjun/feishu-cursor-claw --skill prompt-engineeringgit clone --depth 1 https://github.com/nongjun/feishu-cursor-clawWrote 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/skills/nongjun/feishu-cursor-claw/prompt-engineering)<a href="https://agentmods.dev/skills/nongjun/feishu-cursor-claw/prompt-engineering"><img src="https://agentmods.dev/badge/skills/nongjun/feishu-cursor-claw/prompt-engineering/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/skills/nongjun/feishu-cursor-claw/prompt-engineering"><img src="https://agentmods.dev/badge/skills/nongjun/feishu-cursor-claw/prompt-engineering.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 | $0.00052 | $0.00460 |
| Opus 5 | $0.00026 | $0.00230 |
| Sonnet 5 | $0.00010 | $0.00092 |
| Haiku 4.5 | $0.00005 | $0.00046 |
Grade A, and why
prompt-engineering 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 9d 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.
What it actually says
提示词工程
当前能力
| 能力 | 位置 | 说明 |
|---|---|---|
| 输入清洗 | shared_backend/utils/prompt_utils.py | 防 .format() 注入、移除控制字符 |
| 提示词注册 | AIService 内置方法 | scan_and_register_prompts() |
| 模型绑定 | 门户系统 | 按 function_code 绑定模型 |
| 自定义提示词 | 智能回复模块 | 数据库存储，用户可编辑 |
安全清洗函数
| 函数 | 用途 |
|---|---|
| sanitize_prompt_input(text) | 转义花括号防 .format() 注入 |
| sanitize_chat_message(text) | 更严格：移除控制字符+长度限制+伪装检测 |
SOP：新增 AI 功能
- 定义提示词模板文件（SYSTEM_PROMPT + USER_PROMPT + PROMPT_META）
- 调用 sanitize_prompt_input() 清洗用户输入后再填充模板
- 使用 AIService.chat() 调用，传入 prompt_name 用于统计
- 在门户后台注册功能点并绑定模型
提示词模板规范
- PROMPT_META：name、display_name、description、module、variables
- SYSTEM_PROMPT：角色定义和约束
- USER_PROMPT：带变量占位符的任务模板
- 可选：OUTPUT_SCHEMA 定义结构化输出格式
重点关注
- 用户输入必须经过 sanitize 后再填充模板，防止注入
- 提示词文件放在模块的 prompts/ 目录下
- 通过门户系统可动态切换某功能点使用的模型
- 日志记录依赖传入 prompt_name 参数
参考文件
- 公共模块/shared_backend/utils/prompt_utils.py
- 公共模块/shared_backend/services/ai_service.py
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.
- 9d ago First seen · 49 lines · 52 tokens per session scan A 948142606021
prompt-engineering is a skill published in the GitHub repository nongjun/feishu-cursor-claw (14 stars, last pushed 1mo ago), licensed MIT. It adds 52 tokens to every session and 460 once invoked, about $0.0003 per session on Opus 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.
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Creates reusable prompt templates with strict output contracts, style rules, few-shot examples, and do/don't guidelines. Provides system/user prompt files, variable placeholders, output formatting instructions, and quality criteria. Use when building "prompt templates", "LLM prompts", "AI system prompts", or "prompt…
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Compares old vs new prompts across test cases with diff summaries, stability metrics, breakage analysis, and fix suggestions. Use for "prompt testing", "A/B testing prompts", "prompt versioning", or "quality regression".