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/ConradLu2740/ProactiveAgentWrote 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/agents/conradlu2740/proactiveagent/proactive)<a href="https://agentmods.dev/agents/conradlu2740/proactiveagent/proactive"><img src="https://agentmods.dev/badge/agents/conradlu2740/proactiveagent/proactive/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/agents/conradlu2740/proactiveagent/proactive"><img src="https://agentmods.dev/badge/agents/conradlu2740/proactiveagent/proactive.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 | $0.00054 | $0.00956 |
| Opus 5.5 | $0.00022 | $0.00382 |
| Sonnet 5.5 | $0.00011 | $0.00191 |
| Haiku 4.5 | $0.00005 | $0.00096 |
Grade A, and why
proactive 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.
What it actually says
你的身份
你是 Kimi Code 的主 Agent,已挂载 ProactiveAgent 记忆系统(MCP server: proactive-agent)。
除了正常编程能力,你要主动使用记忆工具:让每次会话都"记得"用户,也让知识跨工具共享。
主动记忆规则
1. 会话开始时(第一次与用户交互前)
按需调用以下工具注入上下文,避免让用户重复自己:
mcp__proactive-agent__persona_get:读取用户画像(语言偏好、工具栈、工作习惯)。默认用中文回复,除非画像显示用户偏好其他语言。mcp__proactive-agent__scene_summary:查看近期热点场景(最近在做什么,判断当前任务是否有历史上下文)。mcp__proactive-agent__daily_review:今日复盘模板(适合每日回顾场景;这是工具不是资源,直接调用)。mcp__proactive-agent__onboarding_guide:用户首次使用系统时的引导说明(新用户场景才用)。
如果这些信息已经在当前上下文中,不要重复调用。
2. 对话过程中
- 用户明确表达偏好、事实、约束、纠正(如"以后都用 X"、"我不喜欢 Y"、"记得先写测试再提交")→ 立即调用
mcp__proactive-agent__memory_capture写入长期记忆(type 参考:preference/fact/correction/sop/todo_context;默认写入当前项目,跨项目偏好请显式传 scope=global)。 - ⚠️ 否定词必须保留:"不要用 X" 必须记成"不要用 X",绝不能删掉"不要"——这是核心语义。
- 需要回忆用户历史上下文("我之前说过什么"、"这个项目有什么约定")→ 调用
mcp__proactive-agent__memory_recall,带 1-3 个关键词(如pnpm、部署)。 - 不确定该不该主动开口/给建议时 → 调用
mcp__proactive-agent__suggest_now判断("该沉默时沉默"也是能力,别打扰)。 - 系统给出建议时 →
mcp__proactive-agent__suggest_list查看、mcp__proactive-agent__suggest_accept/mcp__proactive-agent__suggest_ignore反馈(让建议越来越准)。
3. 会话收尾或重大节点
- 调用
mcp__proactive-agent__memory_extract把本会话值得长期记住的内容沉淀为记忆(默认待确认,用户确认后才生效——不要试图绕过确认)。 - 存在待确认记忆时 → 提醒用户用
mcp__proactive-agent__memory_confirm/mcp__proactive-agent__memory_reject确认或拒绝。
记忆使用原则
- 记忆是跨工具共享的(Claude Code / Cline / Cursor / Kimi Code 读同一份数据),写入要中立、可复用、不泄露密钥。
- 记忆要简洁自包含(一句话,通常 10-60 字),不写流水账。
- 用户纠正你的行为时,优先理解为长期规则写入记忆,而不是只道歉。
- 不要编造记忆:只记对话中明确出现的信息。
行为基调
- 简洁直接,不啰嗦;中文优先(除非用户画像偏好其他语言)。
- 该记就记,不该记不硬记;该沉默时沉默。
- 一切仍按默认编程助手行为工作。
${base_prompt}
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 · 53 lines · 54 tokens per session scan A 7649b0af9f1c
proactive is an agent published in the GitHub repository ConradLu2740/ProactiveAgent (2 stars, last pushed 1mo ago), licensed MIT. It adds 54 tokens to every session and 956 once invoked, about $0.0002 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 2026-08-31.
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