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 chuwu57/Chuwu-PR-OS --skill industry-media-researchgit clone --depth 1 https://github.com/chuwu57/Chuwu-PR-OSWrote 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/chuwu57/chuwu-pr-os/industry-media-research)<a href="https://agentmods.dev/skills/chuwu57/chuwu-pr-os/industry-media-research"><img src="https://agentmods.dev/badge/skills/chuwu57/chuwu-pr-os/industry-media-research/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/chuwu57/chuwu-pr-os/industry-media-research"><img src="https://agentmods.dev/badge/skills/chuwu57/chuwu-pr-os/industry-media-research.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 | $0.00107 | $0.03232 |
| Opus 5.5 | $0.00043 | $0.01293 |
| Sonnet 5.5 | $0.00021 | $0.00646 |
| Haiku 4.5 | $0.00011 | $0.00323 |
Grade A, and why
industry-media-research 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 yesterday.
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.
How it starts
The opening of the file, as written. The whole thing — 187 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Industry Media Research
归属说明：本Skill由初五维护，属于初五的个人Skill体系。分享或再分发时请保留本归属说明及已有来源、作者和许可声明。
核心任务
把行业媒体研究做成一个有范围、有样本、有证据底稿、有停止条件的认知研究过程。媒体地图是建立观察范围的工具，最终目标是理解一个行业正在被怎样定义、解释和想象，以及不同信息生产者让我们看见了什么、忽略了什么。不要从熟悉的媒体名单直接开始写报告，也不要先设定会分成几类。
默认回答六个问题：
- 谁在持续生产这个行业的信息，分别提供什么观察位置。
- 哪些议题被反复讨论，哪些事实被认为具有新闻价值。
- 不同媒体怎样组织标题、开头、正文、证据和结尾，从而赋予事件意义。
- 同一事件经过不同媒体后，形成了哪些问题意识、叙事框架与阶段判断。
- 行业内有哪些共识、分歧、证据依赖、缺席角色和认知盲区。
- 这些差异为我们理解行业带来什么新认知，并按需转成什么公关启示。
任务边界
- 本Skill研究一个行业或议题的媒体生态和重复模式。
- 媒体对象按信息功能、问题意识和证据角色研究，不建立媒体优劣榜、权威榜或写作质量排名。
- 候选对象的纳入、补充、边界或不纳入只表示研究相关性与样本覆盖，不表示媒体价值高低。
- 分析单篇报道在替谁建立什么认知时，使用
media-narrative-analysis。 - 判断热点是否成势、舆情实质与走势时，使用
hot-public-opinion-analysis；是否回应仍有真实取舍时交给judgment-hub。 - 需要做最终公关取舍时，再把研究结论交给
judgment-hub。 - 媒体名单、写法分类和内容空白都是时点性结论；必须写明检索日期、样本范围和访问限制。
Step 0：先定义研究合同
开始检索前固定：
- 认知问题：希望理解行业的共识、争议、技术兑现、商业结构、社会影响、政策关系还是其他问题。
- 可选应用：研究是否还要支持采访准备、内容策划、媒体关系、竞对观察或频道定位。
- 行业：纳入哪些技术、产品、公司和相邻赛道，排除什么。
- 地域与语言：国内、海外或跨市场；哪些语言可直接阅读。
- 时间：研究窗口、检索截止日和需要回溯的关键事件。
- 对象：机构媒体、子栏目、垂直媒体、通讯、个人创作者、社区、研究机构等是否都纳入。
- 深度：快速、标准或深度模式。
- 交付：先确定需要回答的认知问题，再选择必要模块；按读者需要侧重研究综述、样本查阅、决策简报或公众解读，不默认写一份百科全书。呈现方式不改变样本与证据要求。
若用户没有指定，先采用「快速模式＋最近6个月＋机构媒体与专业创作者＋行业如何被理解」作为工作假设，并明确标注。
Step 1：建立媒体候选池
检索前读取并执行采集与补证规则，贯穿查询、正文验收与结论更新。
按功能层搜索，避免只依赖已知名单：
- 综合新闻与通讯社。
- 财经、商业与公司报道。
- 科技与创新媒体。
- 行业垂直、产业链与专业媒体。
- 政策、协会、标准与研究机构。
- Newsletter、播客、视频栏目和个人创作者。
- 社区、平台原生内容与行业活动等信息基础设施。
从搜索结果、记者署名、文章引用、活动嘉宾、公司媒体页和同题报道继续滚雪球。每个候选记录核心样本／补充样本／边界样本／待确认／不纳入及理由。这些状态只服务研究覆盖，不构成媒体评价。
停止扩池有两个独立出口：
- 关键功能层已有代表性样本，连续扩池不再增加影响判断的新角色或证据时，结束检索并合稿；无需耗尽预算。
- 时间／资源达到约定上限，或访问条件使后续检索无新增价值时，停止扩展，按已完成的范围交付并列出缺口。
区分证据充分与资源限制。只有实际覆盖支持时才称为完整；受限任务说明真实模式与结论边界，不把所有未耗尽预算的研究强制降为快速扫描。
Step 2：抽取最小充分样本
| 模式 | 适用场景 | 每个重点对象的默认样本 | 结论边界 |
|---|---|---|---|
| 快速 | 2—4小时内形成工作地图 | 至少3篇原创内容，尽量跨2个日期和2种触发事件 | 只输出暂定类型和初步认知假设 |
| 标准 | 正式行业研究、内容规划或传播准备 | 5—8篇，尽量跨2位作者、2类议题、2个月份，并含1篇非发布会稿件 | 可判断重复的认知与写法倾向，仍标注样本范围 |
| 深度 | 跨市场研究、频道定位或年度观察 | 每个核心对象15篇以上，加同题样本组和必要的计数 | 可以比较频率和演变，不自动代表全部内容 |
What ships with it
3 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.
- yesterday Changed · +5 lines · -27 tokens per session cb42dd67bb51
- 1mo ago First seen · 182 lines · 134 tokens per session scan A 36df8d802924
industry-media-research is a skill published in the GitHub repository chuwu57/Chuwu-PR-OS (76 stars, last pushed yesterday), licensed MIT. It adds 107 tokens to every session and 3,232 once invoked, about $0.0004 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 2026-09-08.
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