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 annual-contentgit 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/annual-content)<a href="https://agentmods.dev/skills/text2future/flowix/annual-content"><img src="https://agentmods.dev/badge/skills/text2future/flowix/annual-content/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/annual-content"><img src="https://agentmods.dev/badge/skills/text2future/flowix/annual-content.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 | $0.00028 | $0.00684 |
| Opus 5.5 | $0.00011 | $0.00274 |
| Sonnet 5.5 | $0.00006 | $0.00137 |
| Haiku 4.5 | $0.00003 | $0.00068 |
Grade A, and why
annual-content 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
80% identical to ai-assistant — 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/年度内容策略.md。所有路径相对主题根目录。
推进本主题
本主题流程：年度目标 → 主题与资源 → 季度排期 → 月度执行 → 季度校准。
- 年度目标：明确全年服务的读者阶段和希望形成的内容资产。
- 主题与资源：划分主题与渠道，核对人员、资料和可持续产能。
- 季度排期：先细化近期季度，给每周内容明确对象和交付形式。
- 月度执行：按月执行并登记实际发布与延期原因，不反改原计划。
- 季度校准：按季度反馈调整主题和资源，后续季度数量随产能确认。
- 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：
年度/计划表.csv、年度/资料/资料清单.csv。 - 新任务：复制
公共/季度计划单.md，在年度/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/内容产出.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：目标、资源和季度重点获确认。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
年度/第一季度新手入门专题/任务记录.md 与 年度/第一季度新手入门专题/第一季度专题排期.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 年度/首月选题资料确认/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 · 28 tokens per session scan A b268a09ddcbe
annual-content is a skill published in the GitHub repository text2future/flowix (469 stars, last pushed 2d ago), licensed MIT. It adds 28 tokens to every session and 684 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 80% identical to ai-assistant, differing in 30 lines, and is treated as a copy.
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Build a throwaway interactive prototype to validate one unresolved interaction or behavior before writing production code. Use when the user explicitly asks to "mock it up first" or "prototype this", or when competing UI behaviors need a disposable hands-on comparison.
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Comprehension gate before porting, copying, or adapting an existing implementation. Proves understanding of the reference code before any new code is written. Use when the task is "port X", "copy how Y does it", "adapt Z", or reimplementing existing behavior.
log-deviation
Record a place where the code forced a deviation from the agreed plan. Use mid-implementation whenever reality contradicts the plan, or when the user says "log this deviation". The log feeds future planning and the merge quiz.