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 SuperChason/ontology-driven-ai-data-management-skills --skill ontology-ai-application-pattern-selectiongit clone --depth 1 https://github.com/SuperChason/ontology-driven-ai-data-management-skillsWrote 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/superchason/ontology-driven-ai-data-management-skills/ontology-ai-application-pattern-selection)<a href="https://agentmods.dev/skills/superchason/ontology-driven-ai-data-management-skills/ontology-ai-application-pattern-selection"><img src="https://agentmods.dev/badge/skills/superchason/ontology-driven-ai-data-management-skills/ontology-ai-application-pattern-selection/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/superchason/ontology-driven-ai-data-management-skills/ontology-ai-application-pattern-selection"><img src="https://agentmods.dev/badge/skills/superchason/ontology-driven-ai-data-management-skills/ontology-ai-application-pattern-selection.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 | $0.00100 | $0.01557 |
| Opus 5 | $0.00050 | $0.00779 |
| Sonnet 5 | $0.00020 | $0.00311 |
| Haiku 4.5 | $0.00010 | $0.00156 |
Grade A, and why
ontology-ai-application-pattern-selection 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 3d 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 — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
本体与 AI 六类应用模式选择
方法骨架
- 在本体路线已经通过适配验证后，从六类应用模式中选择业务形态。
- 流程自动化处理规则明确的动态流程，自主运营处理持续数据—规则闭环。
- 多维决策聚合多源事实和目标，跨领域协同连接多部门、多系统与多Agent。
- 知识解析聚焦单任务深度推理，多场景统筹用PDCA协调持续计划与执行。
- 选择依据包括痛点、闭环跨度、协作复杂度、约束强度和人机责任。
- 输出一个主模式、必要的组合模式及其所需本体和Agent能力。
触发场景
用户会在什么情境下需要这个 Skill
- 场景已确认适合本体，需要选择应用架构
- 问答、审核、协同、决策和PDCA方案混在一起
- 需要明确单Agent、多Agent和流程编排形态
语言信号
- “这个场景属于哪种应用模式”
- “六类模式怎么选”
- “问答审核协同应该怎么组合”
- 英文信号：application pattern, ontology AI patterns, workflow vs PDCA
与相邻 Skill 的区分
- 与
ontology-ai-scenario-fit-and-spike：场景适配先判断本体路线是否成立；本 skill 只在通过后选择应用模式。 - 与
risk-based-agent-action-modes：本 skill 决定整体业务形态；行动模式决定其中具体动作的自动化与人工控制。
执行步骤
按当前任务选择必要步骤；已有可靠成果直接复用：
-
确认适配结论
- 动作：读取场景适配与穿刺证据，确认本体必要性和业务目标。
- 完成标准：明确适配证据和状态；未通过时交付轻量替代方案或候选比较，停止正式本体建设。
- 判停条件：适配未通过时限制建设结论，补足必要评估；不阻止有明确假设的候选模式比较。
-
识别主痛点
- 动作：判断主要矛盾属于流程、运营、决策、跨域协同、知识推理或持续统筹。
- 完成标准：主痛点只有一个，次要需求另列。
-
匹配六类模式
- 动作：比较闭环跨度、事实时效、规则清晰度、协作复杂度、Action和人工责任。
- 完成标准：选择一个主模式，并说明排除其他模式的原因。
-
设计必要组合
- 动作：仅在真实链路需要时加入辅助模式，定义模式间输入输出。
- 完成标准：组合关系有明确接口，没有为展示技术增加多Agent。
-
输出能力清单
- 动作：列出所需事实、事理、Action、Agent、系统、权限、反馈和验收。
- 完成标准：形成模式选择卡和最小实现边界。
固定输出
- 场景适配前提卡：业务目标、本体适配结论、穿刺证据和边界
- 应用模式比较表：模式、主痛点适配、闭环跨度、事实时效、规则强度、协作复杂度、Action 和人工责任
- 主模式选择卡：选定模式、选择依据、排除其他模式的原因和适用边界
- 组合模式协作表：主辅模式、触发、输入、输出、交接和冲突处理
- 能力需求清单：事实、事理、本体资产、Agent、Action、确定性系统、权限、反馈和监控
- 最小实现边界与验收表：首期范围、不做范围、必备数据、用例、效果指标和停止条件
- 未决项、风险和下一阶段输入清单
主模式保持唯一，组合模式只保留业务链路必需部分，每个组合都需要明确交接和验收。
使用边界
不要在以下情况使用
- 还没有证明场景需要本体
- 纯信息问答可由普通检索满足
- 仅凭技术偏好选择多Agent或PDCA
常见失败模式
- 简单任务过度采用多Agent协同：任务分解收益不足以抵消通信、路由、共享状态和冲突处理成本。
- 高时效动态场景硬套本体方案：语义资产更新周期与业务半衰期失配，规则持续过期并带来重复验证成本。
- 对模糊创意和情感任务强制形式化：形式化边界无法覆盖主观语境，固定分类和规则反而压缩生成空间并制造伪精确。
- 为简单低价值任务引入本体工程：新增语义层没有解决关键未知或决策复杂性，却持续产生建模、集成和维护成本。
使用折扣与复核要求
- 六类模式是归纳框架，复杂企业场景可能需要组合，但组合必须由业务链路证明。
- 大模型生成形式结构无法直接证明业务语义正确，生产使用需保留专家确认、工具校验、真实用例和审计记录。
相关 Skills
depends-on→ontology-ai-scenario-fit-and-spike；场景适配先判断本体路线是否成立；本 skill 只在通过后选择应用模式。composes-with→risk-based-agent-action-modes；本 skill 决定整体业务形态；行动模式决定其中具体动作的自动化与人工控制。
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.
- 3d ago Changed · +13 tokens per session e0199da0755b
- 7d ago Changed · +7 lines e2710f0e39f4
- 11d ago First seen · 101 lines · 87 tokens per session scan A c4345bad697c
ontology-ai-application-pattern-selection is a skill published in the GitHub repository SuperChason/ontology-driven-ai-data-management-skills (10 stars, last pushed 4d ago), licensed MIT. It adds 100 tokens to every session and 1,557 once invoked, about $0.0005 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 2026-08-31.
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