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 hydah/thequeen --skill context-summarizergit clone --depth 1 https://github.com/hydah/thequeenWrote 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/hydah/thequeen/context-summarizer)<a href="https://agentmods.dev/skills/hydah/thequeen/context-summarizer"><img src="https://agentmods.dev/badge/skills/hydah/thequeen/context-summarizer/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/hydah/thequeen/context-summarizer"><img src="https://agentmods.dev/badge/skills/hydah/thequeen/context-summarizer.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 | $0.00113 | $0.00945 |
| Opus 5.5 | $0.00045 | $0.00378 |
| Sonnet 5.5 | $0.00023 | $0.00189 |
| Haiku 4.5 | $0.00011 | $0.00094 |
Grade A, and why
context-summarizer 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 2mo 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
Context Summarizer
Overview
将一次讨论、排查、评审或脑暴，收束成可持续复用的 Markdown 资产。 优先解决两个问题：不要丢结论，以及不要过早抽象。
When to Use
- 讨论刚结束，需要整理上下文、结论与行动项
- 已有很多聊天内容，但还没有形成结构化文档
- 需要判断这次内容应该沉淀到哪个目录
- 需要把一次混合型讨论拆成"实例记录 + 长期资产"
Workflow
Step 1: 读取路由信息
读取 bootstrap.md，理解目录职责和路由规则。
Step 2: 识别主类型
判断讨论主要属于哪一类：
| 信号 | 主目录 |
|---|---|
| 追问问题本质、拆假设 | question/ |
| 做设计、选型、方案比较 | tech/ |
| 排查故障、定位根因 | issue/ |
| 拍板、冻结边界、确定取舍 | decision/ |
| 多人会议（有参会人、议程） | meeting/ |
| 修改稳定背景（角色、工作方式） | context/ |
| 提炼重复方法、流程 | playbook/ |
混合型讨论：选一个主落点，再判断是否需要派生 decision/、playbook/、context/ 更新。
Step 3: 抽取五类信息
- 事实：已确认的背景、现象、约束
- 判断：分析、取舍、风险结论
- 决策：已拍板或接近拍板的内容
- 行动：后续动作、责任、待确认项
- 复用价值：值得长期保留的方法、规则
Step 4: 决定沉淀去向
- 是否需要实例文档？（有独立存档价值的新内容才需要）
- 是否包含明确决策？→ 考虑
decision/ - 是否暴露重复方法？→ 考虑
playbook/ - 是否改变稳定背景？→ 更新
context/
不要硬造文件：
meeting/仅放真正的多人会议纪要question/仅放有深度追问的思考- 没有新决策/新实例/新 playbook 时，不必强行创建
Step 5: 选择"更新已有"还是"新建"
- 同主题已有活跃文档 → 优先更新
- 本次结论属于长期规则 → 更新
context/或playbook/ - 本次只是一次性讨论 → 只保留实例
Step 6: 产出文档
如仓库存在 templates/，优先复用模板。产出至少包含：
- 一句话摘要
- 背景/事实
- 核心判断或结论
- 后续动作
- 是否值得继续沉淀
Step 7: 沉淀报告
结束时输出：
- 创建或更新了哪些文件
- 为什么放到这些位置
- 下一步最合理的动作是什么
Quality Checklist
- 产出文件放在正确目录
- 包含一句话摘要、核心判断和后续动作
- 没有硬造不必要的文件
- 目录语义正确
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.
- 2mo ago First seen · 93 lines · 113 tokens per session scan A 1e5826b7b8aa
context-summarizer is a skill published in the GitHub repository hydah/thequeen (2 stars, last pushed 6mo ago), licensed MIT. It adds 113 tokens to every session and 945 once invoked, about $0.0005 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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