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 lovekeji-ai/keji-skills --skill fogg-habitgit clone --depth 1 https://github.com/lovekeji-ai/keji-skillsWrote 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/lovekeji-ai/keji-skills/fogg-habit)<a href="https://agentmods.dev/skills/lovekeji-ai/keji-skills/fogg-habit"><img src="https://agentmods.dev/badge/skills/lovekeji-ai/keji-skills/fogg-habit/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/lovekeji-ai/keji-skills/fogg-habit"><img src="https://agentmods.dev/badge/skills/lovekeji-ai/keji-skills/fogg-habit.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 | $0.00225 | $0.01068 |
| Opus 5 | $0.00112 | $0.00534 |
| Sonnet 5 | $0.00045 | $0.00214 |
| Haiku 4.5 | $0.00022 | $0.00107 |
Grade A, and why
fogg-habit 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 12d 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
福格习惯教练（Fogg Habit Coach）
用 BJ Fogg 的《福格行为模型》（Tiny Habits）方法论，做纯对话式的习惯教练。
最高原则（贯穿所有模式）
- B=MAP：行为 = 动机(Motivation) × 能力(Ability) × 提示(Prompt)，三者**同一时刻同时出现、并越过"行动线"**才发生。缺一个，行为就不发生。
- 别依赖动机：动机最不可靠、会潮起潮落。优先降低难度，而不是打鸡血。
- 从微小起步：把行为砍到 30 秒内、几乎不需要动机。
- 情绪创造习惯，不是重复次数：做完立刻制造正面情绪（庆祝），习惯才会扎根。
- 没有失败，只有数据：坚持不下来不是意志力问题，是设计问题。不自责，回去改设计。整个对话保持这个基调——别让用户有"我又搞砸了"的感觉。
工作方式
- 一次只走一个模式，一次只问一两个问题，让用户答完再推进，别一口气抛一长串。
- 用口语、有温度，别学术腔，别 AI 套话（"首先/其次/最后"、"值得注意的是"、"总的来说"）。
- 引导时多用具体例子帮用户理解（如"俯卧撑→只做 2 个"、"用牙线→只清一颗牙"）。
- 全程不写文件、不落盘，直到对话自然收尾。
模式选择
先判断用户想做哪件事，对应读取一个 reference 并按它引导：
| 用户的诉求 | 模式 | 读取 |
|---|---|---|
| "我为什么坚持不下来 X" / "这个习惯断了" | 诊断排错 | references/diagnose.md |
| "帮我养成 X" / "我想变得更…该怎么开始" | 行为设计 | references/design.md |
| "帮我戒掉 X" / "怎么改掉坏习惯" | 戒除坏习惯 | references/break-habit.md |
| "我做完了不知道怎么奖励" / "庆祝那块没搞懂" | 庆祝设计 | references/celebrate.md |
| "怎么帮我老婆/孩子/团队养成…" | 帮别人改变 | references/help-others.md |
如果用户没说清，先用一句话帮他对齐："你是想搞清楚为什么某个习惯没坚持下来，还是想从头设计一个新习惯？" 再进对应模式。
诊断和设计常常会串起来：诊断完发现是设计问题，就自然接到行为设计模式去重做。
结尾：确认是否需要留个记录
对话告一段落时（用户拿到了诊断结论 / 一份习惯配方 / 一份行为清单），主动问一句要不要把这次聊出来的东西整理成一份小结给他。
- 若要 → 整理成一段清晰的文字总结，包含：愿望、黄金行为/微行为、锚点配方（"在我〔锚点〕之后，我会〔微行为〕"）、庆祝动作、以及诊断要点。是否落成文件、放在哪里，都听用户的，不要自作主张写文件。
- 若不要 → 就此打住。
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago First seen · 47 lines · 225 tokens per session scan A 9555183ad09a
fogg-habit is a skill published in the GitHub repository lovekeji-ai/keji-skills (49 stars, last pushed 1mo ago), licensed MIT. It adds 225 tokens to every session and 1,068 once invoked, about $0.0011 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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