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-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-research)<a href="https://agentmods.dev/skills/chuwu57/chuwu-pr-os/industry-research"><img src="https://agentmods.dev/badge/skills/chuwu57/chuwu-pr-os/industry-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-research"><img src="https://agentmods.dev/badge/skills/chuwu57/chuwu-pr-os/industry-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 | $0.02544 |
| Opus 5.5 | $0.00041 | $0.01018 |
| Sonnet 5.5 | $0.00021 | $0.00509 |
| Haiku 4.5 | $0.00010 | $0.00254 |
Grade A, and why
industry-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 — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Industry 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。
目标
把行业研究做成可复核的决策系统：明确边界，恢复历史，解释机制，建模利润，验证公司，暴露缺口，并给出会随新证据更新的行动。
开始前
先写一份研究合同，至少固定：
- 决策、决策人和使用时间；
- 行业、客户、价值链、地域和时间边界；
- 核心层、相邻层和排除层；
- 必答P0、应答P1和非目标；
- 可用数据、访问限制与完成标准。
先确定核心问题和可修正的假设，最终结论随证据形成。交付按读者任务选择决策简报、数据查阅、研究综述或公众解读；呈现方式与研究深度分别确定，不因格式调整降低证据要求。
如果用户要求「全面行业研究」，默认覆盖下方所有P0与P1。若裁剪模块，要说明对结论的影响。
研究档位
- 快速版：服务一个明确决策，完成最小P0闭环，只纳入会改变该决策的P1。研究合同、指标字典、证据账本和样本登记可放在报告附录，不强制拆成四个独立文件。不得省略边界、口径、反证、缺口和完成状态。
- 标准版：覆盖全部P0和相关P1，四类底稿独立保存，至少完成一次来源／计算复核和一次红队。
- 深度版：覆盖全部P0与P1，建立完整历史、情景、单位经济、样本索引、监测系统与一手研究缺口，可并行分工并进行独立红队。
报告模板是最大结构。快速版可合并章节；技术、资本、政策等模块只在影响决策时保留。裁剪不能删除决定结论成立与否的证据链。
证据纪律
1.先建指标字典，再搜索规模。区分收入、GMV、出货、装机、激活、活跃、ASP、毛利、利润与现金流。 2.每条核心主张同时评价「来源可追溯性」与「对总体的外推能力」。论坛原帖可证明个案，不能证明行业比例；公司官网可证明公司自述，不能独立证明竞争优势。 3.将结论标为「已核事实」「有边界的样本信号」「研究推断」「情景模型」或「待验证假设」。 4.口径冲突要解释或保留多版本，不选择最顺眼的数字。 5.没有真实参数时只给公式、敏感变量和取数计划，不伪造行业平均单位经济。 6.公开VOC与媒介样本必须披露窗口、n、平台、纳入／排除、偏差和商业关系盲区。
开始研究时完整读取research-contract-and-evidence.md。标准版和深度版复制assets/中的研究合同、指标字典、证据账本和样本登记模板；快速版可把四类底稿内嵌在报告附录，但字段要求不变。
执行顺序
P0：决定行业机会是否成立
1.历史与情景：建立同口径历史序列，拆数量、价格、组合、地域、渠道与利用率；把第三方预测、本报告模型和公司目标分开，给保守／基准／积极情景及失效信号。 2.利润池与单位经济：画价值流和现金流；用公司分部、费用、库存和现金验证设备／产品、耗材、软件、服务、渠道和售后；为关键参与者建立参数化单位经济。 3.客户与采用：还原情境、触发、任务、替代、选择、购买／采用、激活、使用、复购／扩产、放弃；区分机制、发生率和因果。 4.竞争战场：按客户任务、技术路线、价格带、渠道或区域分战场；公司只设一个主表，比较可持续优势、收入质量和执行能力。 5.需求形成与商业化：按认知、考虑、验证、转化、激活和留存拆解品牌、PR、媒介、达人、电商、直销、经销、门店、社区和售后。
P1：决定如何进入、执行和监测
1.技术与成本：解释性能—成本边界、替代技术、规模化约束、BOM／材料／软件／后处理和供应链风险。 2.区域与政策：比较需求、供给、价格、渠道、服务、法规、关税、数据和本地化；草案与生效规则分开。 3.资本与组织：核对IPO、融资、并购、重组和破产的交易事实；用研发、销售、交付、本地服务和现金解释战略可执行性。 4.监测系统：为未来12—24个月设置指标、基线、频率、信号源、阈值、责任人和触发动作。每个阈值标注为「监管硬阈值」「行业基准」「企业内部基线」或「建议决策门槛」；建议门槛可以用于验证设计，但不能冒充行业事实。缺企业基线的异常阈值标「方案待实施」。
具体模块问题、计算和证据要求见module-playbook.md。聚焦任务只读取相关章节；全面研究完整读取该文件。
分工与并行
深度研究可按以下独立任务并行：
- 历史／预测／区域政策；
- 公司经营／利润池／单位经济；
- 客户／VOC／媒介达人／渠道；
- 最终红队。
What ships with it
11 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.
- agents/openai.yaml 254 B
- assets/evidence-ledger-template.csv 246 B
- assets/industry-research-report-template.md 2.2 KB
- assets/metric-dictionary-template.csv 201 B
- assets/research-contract-template.md 1.1 KB
- assets/sample-registry-template.csv 170 B
- references/cross-source-synthesis.md 3.7 KB
- references/module-playbook.md 6.4 KB
- references/output-and-quality-gates.md 8.2 KB
- references/research-contract-and-evidence.md 6.5 KB
- scripts/audit_research.py 4.8 KB runs code
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 · +6 lines · -29 tokens per session 1d0c114e77d4
- 1mo ago First seen · 114 lines · 132 tokens per session scan A cfaf79816158
industry-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 2,544 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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