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 text2future/flowix --skill english-learninggit clone --depth 1 https://github.com/text2future/flowixWrote 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/text2future/flowix/english-learning)<a href="https://agentmods.dev/skills/text2future/flowix/english-learning"><img src="https://agentmods.dev/badge/skills/text2future/flowix/english-learning/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/text2future/flowix/english-learning"><img src="https://agentmods.dev/badge/skills/text2future/flowix/english-learning.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 | $0.00027 | $0.00710 |
| Opus 5.5 | $0.00011 | $0.00284 |
| Sonnet 5.5 | $0.00005 | $0.00142 |
| Haiku 4.5 | $0.00003 | $0.00071 |
Grade A, and why
english-learning 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 7d 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.
This is a copy
84% identical to family-event — 30 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
先读主题根目录 AGENTS.md、公共/项目启动书.md 和 公共/英语能力提升计划.md。所有路径相对主题根目录。
推进本主题
本主题流程：水平诊断 → 目标拆分 → 日常练习 → 阶段测评 → 计划调整。
- 水平诊断：用实际听说读写任务识别当前困难。
- 目标拆分：按目标场景拆分表达、词汇和理解任务。
- 日常练习：安排短练习并保留错误与修改，不只记录学习时长。
- 阶段测评：用相近任务检查表达是否更清楚、理解是否准确。
- 计划调整：根据测评调整练习内容与频率，保留有效方法。
- 任务入口：
进度视图.csv。按事项ID查找记录，展示给用户时使用任务名称。 - 决策与依据：
目标与计划/计划表.csv、目标与计划/资料/资料清单.csv。 - 新任务：复制
公共/学习周记录.md，在练习/下用可读的任务名称建目录；场次、批次等重复任务加日期。 - 填写案例与真实任务时沿用空白模板的标题和字段，不另造一套简化结构。字段不适用写明原因；资料缺失时说明缺口及影响。过程写已发生的动作，产物保存到任务目录并登记实际路径。
- 结果写入
数据/练习记录.csv，沿用同一事项ID。检查字段单位、时间范围和对象粒度；单次任务不填项目累计数。缺失值不得用0代替。 - 本主题完成条件：测评来源与练习完成情况核对。先逐项核对模板中的审核字段，再更新状态；缺少影响验收的证据时保持待确认。
本库中的教学记录
练习/2026-10-28 每周会议口语练习/任务记录.md 与 练习/2026-10-28 每周会议口语练习/会议口语练习稿.md 展示一次任务的记录及产物。其中人物和数据为模拟；未经用户要求，不把模拟数据当作真实事实或继续虚构实际结果。外部发布、支付和联系按用户授权执行。
数据记录先确认一行代表什么对象、哪段时间与什么单位。原始记录、分类合计和项目汇总分别维护；验收表的期望结果必须与实际结果逐项对应，不能只填写“通过”。
接续任务
第二项教学任务为 练习/2026-11-04 会议澄清问句练习/任务记录.md。从前序反馈提取下一步，先建立新ID与计划行，再填写同一公共模板；复用数据不重复登记。更新视图前核对产物存在、审核结论与状态一致。
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.
- 7d ago First seen · 34 lines · 27 tokens per session scan A 3e0101b35867
english-learning is a skill published in the GitHub repository text2future/flowix (469 stars, last pushed 2d ago), licensed MIT. It adds 27 tokens to every session and 710 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 84% identical to family-event, differing in 30 lines, and is treated as a copy.
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