Wanwu is an enterprise platform for building AI agents, workflows, retrieval-augmented applications, and managing models in multi-tenant environments. It is designed for developers and enterprise teams delivering AI applications and integrations. The catalogue entries provide skills and agents for using the platform.
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 UnicomAI/wanwu --skill bkn-modeling-advisorgit clone --depth 1 https://github.com/UnicomAI/wanwuWrote 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/unicomai/wanwu/bkn-modeling-advisor)<a href="https://agentmods.dev/skills/unicomai/wanwu/bkn-modeling-advisor"><img src="https://agentmods.dev/badge/skills/unicomai/wanwu/bkn-modeling-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/unicomai/wanwu/bkn-modeling-advisor"><img src="https://agentmods.dev/badge/skills/unicomai/wanwu/bkn-modeling-advisor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00093 | $0.01723 |
| Opus 5 | $0.00046 | $0.00861 |
| Sonnet 5 | $0.00019 | $0.00345 |
| Haiku 4.5 | $0.00009 | $0.00172 |
Grade A, and why
bkn-modeling-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 6d 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 — 213 lines — stays where its author put it; the contents beside it link to each section on GitHub.
BKN Modeling Advisor
适用场景
当用户出现以下意图时使用本技能：
- 从零设计一个 BKN
- 扩展/重构已有 BKN
- 从 PRD、流程文档提取本体初稿
- 评审 BKN 结构是否合理
- 讨论对象、关系、Action、风险与治理建模
工作模式
先识别模式，再进入流程：
new：无现成 BKN，从业务场景开始建模import：已有 BKN，做增量设计或评审from_doc：用户提供文档，先抽取初稿再补齐
交互原则
- 每次只问一个关键问题，等待回答后推进
- 优先使用业务语言，不要求用户理解 JSON 细节
- 每个阶段结束先复述（Model Narration）再确认
- 对不确定术语先标记，再请求澄清
- 出现高风险变更时，强制补齐审批与回滚描述
建模流程
按以下阶段推进，import/from_doc 可从中间阶段切入。
Phase 1：领域锚定
收集并确认：
network.idnetwork.namenetwork.descriptionbusiness_domain（如采购、库存、计划、质量）
Phase 2：场景走查
让用户描述典型流程，提取候选项：
- 反复出现的业务名词 -> 候选
object_type - 状态变化与动作 -> 候选
action_type - 对象之间的关联 -> 候选
relation_type
Phase 3：对象类型确认
对每个候选对象检查：
- 是否有独立生命周期
- 是否有稳定唯一标识（字符串主键）
- 是否至少有 3 个可追踪属性
- 是否被多个流程或角色使用
对象输出至少包含：
id,name,descriptiondata_properties[]keys.primary_keys[],keys.display_key
Phase 4：关系类型确认
逐对对象确认：
- 业务含义是否清晰
- 基数（1:1 / 1:N / N:1 / N:M）
- 关系实现类型
direct：字段直连，需mapping_rulesdata_view：需中间视图，需映射定义
如果“关系本身有属性”（如金额、状态、生效日期），优先升级为独立对象，而非直接建 link。
Phase 5：操作类型确认
为每个操作明确：
- 谁触发（角色）
- 改变哪些对象和字段
- 前置条件（
pre_conditions） - 参数来源（
property/input/const） - 风险等级（
low/medium/high） - 是否必须审批（
requires_approval）
bound_object.action_type 仅可取：
addmodifydeletequery
Phase 6：可选治理补充
按需补充：
risk_type：高风险动作的控制策略concept_group：对象分组（按业务域/职责）
Phase 7：完整性审查与导出
导出前逐项检查：
- 必填字段齐全
- ID 命名合法（建议仅用
[a-z0-9_-]） - 引用对象存在且可达
- 主键/显示键指向有效字段
- Action 参数绑定完整
快速建模准则
对象 vs 属性
- 简单特征值（状态、等级、颜色） -> 属性
- 独立业务实体（可被引用、有生命周期） -> 对象
关系 vs 对象
- 仅表示连接 -> 关系
- 连接本身有业务属性 -> 升级为对象
Action vs 关系
- 描述“结构关联” -> 关系
- 描述“状态改变或业务动作” -> Action
主键设计
- 必须为字符串
- 必须稳定且可复算
- 优先业务单号/编码，避免随机 UUID
输出格式
默认输出两段内容：
- 业务复述版（给业务方确认）
- BKN 结构草案（JSON 片段，按节点类型分组）
如用户确认“导出完整文件”，再输出完整 BKN JSON。
与 bkn-creator 对接契约（MUST）
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.
- 6d ago First seen · 213 lines · 93 tokens per session scan A 42a7884ec597
bkn-modeling-advisor is a skill published in the GitHub repository UnicomAI/wanwu (2,461 stars, last pushed 5d ago), licensed Apache-2.0. It adds 93 tokens to every session and 1,723 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 2026-09-03.
Other skills, from other repositories
dify
Use when building LLM applications with visual workflow — RAG knowledge bases, AI agents, chatbots with drag-and-drop orchestration. Dify: open-source LLM app platform supporting 30+ models (OpenAI, Claude, DeepSeek, Ollama, Qwen, GLM) with Docker deployment.
ai-skills
Use when building LLM applications, RAG knowledge bases, AI agents, terminal coding agents, multi-model orchestration, plugin-based agent harnesses, or file translation. Index of 10 skills: Dify, Hermes Agent, OpenClaw, OpenCode, Pi, DocuTranslate, Oh-My-OpenAgent, Superpowers-zh, DeepSeek Harness, My OpenCode…
Web2Skill
Convert one public website URL or an explicit batch of public URLs into a reusable skill zip backed by rendered HTML snapshots and a bounded JSONL retrieval index. Use to discover a documentation directory from one URL, crawl a supplied URL set sequentially, generate a source profile, or package indexed web content as…
Book2Skill
Convert one or more TXT, Markdown, DOCX, or PDF documents into a reusable skill zip backed by normalized Markdown, extracted images, and a grounded JSONL knowledge index. Invoke this skill before inspecting task files, then execute its workflow directly without listing directories.
agent-orchestration-multi-agent-optimize
Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability.
ai-agents-architect
Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration.