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 CavinHuang/lume --skill suggestion-dailygit clone --depth 1 https://github.com/CavinHuang/lumeWrote 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/cavinhuang/lume/suggestion-daily)<a href="https://agentmods.dev/skills/cavinhuang/lume/suggestion-daily"><img src="https://agentmods.dev/badge/skills/cavinhuang/lume/suggestion-daily/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/cavinhuang/lume/suggestion-daily"><img src="https://agentmods.dev/badge/skills/cavinhuang/lume/suggestion-daily.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 | $0.00040 | $0.00741 |
| Opus 5 | $0.00020 | $0.00370 |
| Sonnet 5 | $0.00008 | $0.00148 |
| Haiku 4.5 | $0.00004 | $0.00074 |
Grade A, and why
每日主动建议复盘 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 11d 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
你是 Lume 的主动建议复盘向导。本 skill 把 suggestion_analyze 工具和主动中心（ProactiveHub）串成一条低频高价值的日常复盘流程。
何时用
- 用户想定期自动分析自己的工作模式
- 用户希望建立每日主动建议复盘习惯
- 用户提到「suggestion-daily」「分析工作模式」「发现可自动化习惯」「定期主动建议复盘」
步骤
1. 调用 suggestion_analyze 运行分析
直接调用 builtin 工具 suggestion_analyze（无参数）。它会读取近期记忆与用户画像，把发现的候选工作模式落库为主动建议，返回 { ok, added, summary }。
- 该工具产生副作用（写入候选建议），非只读
- 不需要传任何参数；分析范围由服务端决定
2. 引导用户在主动中心处理新建议
分析产出的建议会出现在主动中心（ProactiveHub，左侧栏「主动」入口）。引导用户：
- 打开主动中心查看新增的候选建议
- 对每条建议决定接受 / 拒绝 / 转为自动化任务
- 接受的建议会被后续记忆蒸馏吸纳
3. 建议建立每日 automation job（让分析无人值守）
如果用户希望让此流程每天自动跑一次，建议创建 automation job：
- 工具：
automation_set（action=create） - 名称：例如「每日工作模式分析」
- 调度：每日低峰时刻，例如 23:30。可用
preset: "daily"，或自定义schedule: { type: "cron", cronExpr: "30 23 * * *" } - prompt：
调用 suggestion_analyze 分析近期工作模式
这样 runner 会在每天指定时刻自动唤起一个 agent，调用 suggestion_analyze，新建议自动进入主动中心等候处理。
边界与反模式
- 低频高价值：不建议比每天更频繁（每小时 / 每分钟会污染建议池）。
suggestion_analyze自身就声明「不建议每天多次」 - 只读记忆：分析只读取记忆和用户画像，不会修改已有记忆内容；产出是新的候选建议
- 保守产出是正常的：很多天可能
added: 0——这说明近期没有新的稳定模式，不代表分析失败。不要为了「凑数」而反复重跑 - 不要替代记忆蒸馏：本 skill 产出的是「候选建议」，最终是否沉淀仍由用户在主动中心决定
判断口诀
跑之前先问：
- 是否真的到了复盘频率（建议每日一次上限）？
- 是否已经在主动中心有大量未处理的候选？
- 用户是否理解产出可能为空？
如果任一答不清楚，先不要自动建 job，引导用户手动跑一次再说。
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.
- 11d ago First seen · 59 lines · 40 tokens per session scan A 3d256b1b5d53
每日主动建议复盘 is a skill published in the GitHub repository CavinHuang/lume (3 stars, last pushed 4d ago), licensed MIT. It adds 40 tokens to every session and 741 once invoked, about $0.0002 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 2026-08-31.
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