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 pr-consumer-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/pr-consumer-research)<a href="https://agentmods.dev/skills/chuwu57/chuwu-pr-os/pr-consumer-research"><img src="https://agentmods.dev/badge/skills/chuwu57/chuwu-pr-os/pr-consumer-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/pr-consumer-research"><img src="https://agentmods.dev/badge/skills/chuwu57/chuwu-pr-os/pr-consumer-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 | $0.00103 | $0.03244 |
| Opus 5.5 | $0.00041 | $0.01298 |
| Sonnet 5.5 | $0.00021 | $0.00649 |
| Haiku 4.5 | $0.00010 | $0.00324 |
Grade A, and why
pr-consumer-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 — 292 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PR Consumer 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。
定位
把消费者、市场和产品证据转换成PR可执行判断。
本Skill不替代通用研究,也不替代PR专项判断:
consumer-research负责研究设计、证据分级、样本、访谈、问卷、市场分析、竞品分析和产品测试。- 本Skill负责判断研究结果对公共理解、品牌信任、传播主张和业务动作意味着什么。
- PR专项Skill负责文案、舆情、媒体叙事、预案和新闻稿等专业任务。
judgment-hub负责最终取舍和拍板。
条件链路:
业务或PR问题
→ 判断消费者证据缺口
→ 仅在证据缺口会改变结论时由consumer-research补证据或设计验证
→ 本Skill完成PR转换
→ 仅在用户需要具体专业产物时进入一个PR专项Skill
→ 仅在仍有真实取舍时进入judgment-hub
不需要消费者、市场或产品证据的问题,不要强行调用本Skill。
判断风格
第一性原理
先回答「这项研究要改变哪个业务或PR决策」,再决定研究什么。
不要从「用户喜欢什么」出发,要从真实选择机制出发:
- 用户处于什么情境。
- 什么触发需求或不满。
- 用户在完成什么任务。
- 用户比较了哪些替代方案。
- 什么因素建立或破坏信任。
- 什么约束最终改变行为。
结论先行
输出第一屏先给决策卡:
- 当前建议:立即行动/补证据后行动/暂停。
- 最大支持证据。
- 最强反证。
- 最大不确定性。
- 对业务和信任的影响。
不要先铺研究背景。
反向验证
主动寻找不支持既定方案的证据,尤其在项目已经立项、老板期待上线或团队倾向某个答案时。
必须问:
- 什么证据会推翻当前判断。
- 是否因为想推进项目而降低了证据标准。
- 是否把内部愿望误写成消费者需求。
- 是否把传播语言误当作产品事实。
- 是否存在更简单的替代解释。
情绪与公共语境
事实成立不代表公众会接受。除功能、价格和行为外,必须检查:
- 消费者是否感到被忽视、欺骗、不尊重、不公平或被教育。
- 是否触发身份、阶层、性别、家庭、代际、地域、职业或弱势群体感受。
- 品牌意图和普通用户的直接理解是否一致。
- 一句话被截图后会形成什么公共标签。
- 个体不满是否具备串联成群体叙事的条件。
长期信任
不要只问「这次能不能发」,还要问:
- 这个主张能否长期重复。
- 以后是否需要持续解释。
- 产品体验能否兑现传播承诺。
- 是否用短期声量透支品牌信用。
- 同类问题再次发生时,当前表达会不会成为反证。
工作模式
根据决策时限和证据要求选择最小充分模式。
快速判断
适用于当天或短时间内需要方向的PR任务。
- 只使用现有材料和可核验公开信息。
- 输出初步判断、证据缺口和升级条件。
- 不把快速判断包装成完整消费者研究。
- 证据不足时给暂停条件,不强行给确定答案。
标准研究
适用于新品、传播策略、竞品判断和阶段性复盘。
- 组合业务数据、消费者反馈、市场资料、竞品表现和公共语境。
- 至少用不同性质的证据交叉验证关键判断。
- 输出消费者机制、市场条件、传播含义和行动建议。
深度验证
适用于高投入、高风险或长期决策。
- 调用
consumer-research设计访谈、问卷、产品测试或真实实验。 - 预设主要指标、成功阈值、暂停条件和反证标准。
- 完成真实验证前,不把假设升级为确定主张。
Step 1:定义PR决策
明确:
- 要决定什么:上市、延期、修改产品、调整包装、选择主张、回应、发稿、做预案或保持观察。
- 主要目标:认知、信任、差异、品类教育、转化承接、风险修复或长期话语位置。
- 哪类消费者、非目标用户和利益相关方会受到影响。
- 哪些事实和承诺准备公开。
- 决策时限、资源、不可说内容和组织承受能力。
把问题定成一个主类:消费者事实、市场条件、产品体验、表达、叙事、信任、业务或组织问题。只能选一个主类,其他作为影响因素。
Step 2:识别证据缺口
按consumer-research的主张与证据匹配规则,检查可追溯性、实际测量、外推范围与替代解释:
- 已知事实是什么。
- 哪些只是社交媒体信号或个别投诉。
- 哪些是团队解释和策略假设。
- 哪个未知问题会真正改变决策。
以下情况提示证据不足:可直接判断哪些主张尚不成立,交付有边界的PR结论。只有用户任务需要补齐、且新增研究会改变决策时,才调用consumer-research设计或执行相应研究;不为否定一个无依据主张先启动完整研究:
What ships with it
1 file 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 · +7 lines · +14 tokens per session 6d8d97cf459a
- 1mo ago First seen · 285 lines · 89 tokens per session scan A 76f9d2010fbb
pr-consumer-research is a skill published in the GitHub repository chuwu57/Chuwu-PR-OS (76 stars, last pushed yesterday), licensed MIT. It adds 103 tokens to every session and 3,244 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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