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 AgenticAIPlan/AgenticAISkills --skill expert-ops-strategy-pipelinegit clone --depth 1 https://github.com/AgenticAIPlan/AgenticAISkillsWrote 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/agenticaiplan/agenticaiskills/expert-ops-strategy-pipeline)<a href="https://agentmods.dev/skills/agenticaiplan/agenticaiskills/expert-ops-strategy-pipeline"><img src="https://agentmods.dev/badge/skills/agenticaiplan/agenticaiskills/expert-ops-strategy-pipeline/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/agenticaiplan/agenticaiskills/expert-ops-strategy-pipeline"><img src="https://agentmods.dev/badge/skills/agenticaiplan/agenticaiskills/expert-ops-strategy-pipeline.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 | $0.00127 | $0.03991 |
| Opus 5 | $0.00063 | $0.01996 |
| Sonnet 5 | $0.00025 | $0.00798 |
| Haiku 4.5 | $0.00013 | $0.00399 |
Grade A, and why
expert-ops-strategy-pipeline 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 11d 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.
How it starts
The opening of the file, as written. The whole thing — 442 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Expert Ops Strategy Pipeline
把“全网搜集行业专家运营经验内容”这件事，稳定地执行成一条可复用流水线：
- 采集近 3 年高相关内容
- 用统一口径打分并分层
- 把高分内容拆成策略卡片
- 归并相似策略，形成整合策略库
- 生成《专家运营备选策略笔记-yyyymmdd》
What This Skill Is For
当用户的目标属于下面任何一种时，触发这个 skill：
- 要搭建或维护“专家运营策略库”
- 要搜集行业专家运营、KOC/KOL 运营、知识付费、私域社群、专家招募/冷启动/分层/激励/转化相关经验帖、教程、文章
- 要把零散文章沉淀到飞书多维表格
- 要给文章做 0-100 分评分、S/A/B/C/D 分层
- 要从文章里抽取多条运营策略
- 要把相似策略去重整合
- 要把高分策略输出成策略笔记、研究报告、操作 SOP
下面这些更轻的任务通常不要触发本 skill，除非用户同时明确要求表格沉淀、评分分层或策略库建设：
- 只总结几篇文章
- 只润色一篇笔记
- 只头脑风暴策略方向
- 只问“专家运营有哪些思路”
Default Deliverables
默认交付物有四层：
内容源采集库专家运营策略拆解库专家运营策略整合库专家运营备选策略笔记-yyyymmdd
如果用户只要其中一层，也可以只执行到对应阶段，不必强行跑完整流水线。
Compatibility
Hard requirements by task type
lark-base：只要任务涉及建 Base、建表、读写记录、字段设计、公式字段，就把它视为硬依赖。web-research或同等联网检索能力：只要任务涉及“近三年”“最新”“全网搜索”“采集文章”，就把联网研究视为硬依赖。lark-doc：只有当用户明确要求发布飞书文档时，才是硬依赖。
Optional but preferred dependencies
firecrawl：当用户要更深的页面抓取、整站抽取或网页交互时优先使用；如果不可用，不要阻塞全流程，改用浏览器检索和页面阅读。assess：优先用来执行 0-100 分评分；如果不可用，仍然按本 skill 的评分口径手动完成评分。research-synthesis：优先用来组织整合策略的主题、洞察和机会；如果不可用，直接按本 skill 里的默认策略笔记结构输出。writing-documentation-with-diataxis：优先用来约束最终文档写成 How-to Guide；如果不可用，仍然按“可执行优先”的原则输出，不要停止任务。
Fallback rules
- 缺少联网能力：明确说明无法验证“近三年 / 最新”范围，只能基于已提供资料或本地上下文给出非联网版本。
- 缺少
lark-base：先输出建议表结构、字段定义和待写入内容，不要假装已经写进 Base。 - 缺少
lark-doc：先输出 Markdown 成品，并明确“尚未发布到飞书文档”。 - 缺少
firecrawl/assess/research-synthesis/writing-documentation-with-diataxis：降级执行，不中断主流程。
Tool Selection
优先使用下面这些能力：
lark-base：建 Base、建表、查字段、写记录、更新字段web-research：做多源检索、归类、研究式汇总firecrawl：需要更强页面抓取、站点级抽取时使用；如果 CLI 或环境不可用，明确说明并回退到浏览器检索assess：按评分口径打 0-100 分并写出评分依据research-synthesis：把整合后的策略聚类成主题、洞察、机会writing-documentation-with-diataxis：把策略整合成可执行的笔记文章，优先写成 How-to Guidelark-doc：把最终 Markdown/结构化内容发布成飞书文档
如果用户是问“有没有更合适的 skill 可以补采集能力/补渠道/补整理能力”，再补用 find-skills。
Working Principles
1. 先结构化，再规模化
先确认 Base、表结构、字段设计和评分口径，再扩大采集量。不要一上来抓很多内容，最后发现字段不够用。
2. 优先采集“可转策略”的内容
What ships with it
2 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.
- 11d ago First seen · 442 lines · 0 tokens per session scan A 73910b3bdb11
expert-ops-strategy-pipeline is a skill published in the GitHub repository AgenticAIPlan/AgenticAISkills (11 stars, last pushed 3mo ago), licensed MIT. It adds 127 tokens to every session and 3,991 once invoked, about $0.0006 per session on Opus 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 2026-08-30.
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