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 bp-arch-advisorgit 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/bp-arch-advisor)<a href="https://agentmods.dev/skills/agenticaiplan/agenticaiskills/bp-arch-advisor"><img src="https://agentmods.dev/badge/skills/agenticaiplan/agenticaiskills/bp-arch-advisor/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/bp-arch-advisor"><img src="https://agentmods.dev/badge/skills/agenticaiplan/agenticaiskills/bp-arch-advisor.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 | $0.00112 | $0.01281 |
| Opus 5 | $0.00056 | $0.00641 |
| Sonnet 5 | $0.00022 | $0.00256 |
| Haiku 4.5 | $0.00011 | $0.00128 |
Grade A, and why
bp-arch-advisor 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.
What it actually says
BP 技术架构分析与文心大模型增强方案
适用场景
当用户提供一份创业项目计划书（BP），且希望完成以下任一目标时，使用本 Skill：
- 从 BP 中提取技术架构关键信息并结构化呈现
- 对现有技术架构进行可扩展性、AI-Ready 程度等维度的诊断评估
- 获得基于百度文心大模型（ERNIE）生态的具体架构改造方案
- 获得改造成本估算和分阶段实施路线图
不适用场景：BP 中无任何技术描述且用户也无法补充技术信息时，应提示用户先完善 BP 技术部分。
输入要求
- BP 文档（必需）：支持 PDF、Markdown 或纯文本格式的创业项目计划书
- 聚焦方向（可选）：
full（完整分析，默认）|architecture（仅架构诊断）|ernie-enhancement（仅文心增强方案）|cost（仅成本估算）
执行步骤
第一步：读取 BP 文档并提取关键信息
- 使用 Read 工具读取用户提供的 BP 文档。如果是 PDF，使用
pages参数分批读取（每次不超过 20 页）。 - 从文档中提取以下 8 个维度的信息，整理为表格。如某项信息在 BP 中未提及，标注为
[BP未提及 - 建议补充]。
提取维度和对应内容参见 references/extraction-dimensions.md。
第二步：技术架构深度诊断
基于第一步的提取结果，完成以下诊断：
架构评分（每项 1-5 星）：
- 可扩展性：能否支撑 10x 用户增长
- AI-Ready 程度：是否为 AI 集成预留了架构空间
- 数据闭环能力：是否具备采集→训练→推理→反馈的闭环
- 成本效率：技术选型的性价比
- 技术壁垒：架构层面的护城河强度
风险识别：列出 Top 3 技术架构风险，每条包含风险描述、影响程度（高/中/低）、发生概率（高/中/低）、建议应对措施。
第三步：文心大模型增强方案
这是本 Skill 的核心输出。基于项目实际情况设计具体方案，包含以下子步骤：
-
模型选型：根据项目场景匹配文心模型组合。选型依据参见
references/ernie-model-matrix.md。对每个场景输出：推荐模型、选型理由、调用方式（API / 私有化 / 精调）。 -
架构改造方案：
- 用 ASCII 图绘制改造后的系统架构，标注文心大模型的接入点
- 对每个改造点输出：当前方案、改造方案、接入方式（API / RAG / 微调 / Agent）、预期收益、实施复杂度（高/中/低）、优先级（P0/P1/P2）
-
数据策略：包括训练/微调数据准备、数据标注方案（如需要）、向量化与知识库构建方案（如使用 RAG）。
-
分阶段路线图：
- MVP 验证（1-2 周）：快速验证核心 AI 场景
- 能力构建（3-6 周）：构建生产级 AI 管线
- 规模化（7-12 周）：全面接入 + 效果优化
-
成本估算：基于百度智能云公开定价，估算 API 调用、向量数据库、GPU 推理等月度成本。
第四步：BP 修改建议
给出 3-5 条针对 BP 文档本身的改进建议，帮助创始人在技术叙事上更具说服力：
- 建议补充的技术细节
- 建议调整的技术选型表述
- 数据/AI 策略的包装建议
输出要求
- 全部使用中文输出
- 关键数据用 加粗 标注
- 架构图使用 ASCII art 绘制
- 每个章节之间用
---分隔 - 如果 BP 中技术细节不足，基于行业常见架构进行合理推断，并标注
[推断] - 方案必须具体可执行，避免空泛的"建议使用 AI"类表述
- 优先推荐文心生态内的解决方案（千帆平台、向量数据库 VDB、AppBuilder 等）
- 如果项目不适合大模型增强，直接说明原因，不强行推荐
- 在输出末尾附加一段不超过 50 字的总结
参考资料
references/extraction-dimensions.md：BP 信息提取的 8 个维度定义与字段说明references/ernie-model-matrix.md：文心大模型产品矩阵、能力定位与适用场景速查表
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 · 88 lines · 112 tokens per session scan A dc95ec764468
bp-arch-advisor is a skill published in the GitHub repository AgenticAIPlan/AgenticAISkills (11 stars, last pushed 3mo ago), licensed MIT. It adds 112 tokens to every session and 1,281 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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