# s4b7-ai-skills - Doramagic AI Context Pack

> 定位：安装前体验与判断资产。它帮助宿主 AI 有一个好的开始，但不代表已经安装、执行或验证目标项目。

## 充分原则

- **充分原则，不是压缩原则**：AI Context Pack 应该充分到让宿主 AI 在开工前理解项目价值、能力边界、使用入口、风险和证据来源；它可以分层组织，但不以最短摘要为目标。
- **压缩策略**：只压缩噪声和重复内容，不压缩会影响判断和开工质量的上下文。

## 给宿主 AI 的使用方式

你正在读取 Doramagic 为 s4b7-ai-skills 编译的 AI Context Pack。请把它当作开工前上下文：帮助用户理解适合谁、能做什么、如何开始、哪些必须安装后验证、风险在哪里。不要声称你已经安装、运行或执行了目标项目。

## Claim 消费规则

- **事实来源**：Repo Evidence + Claim/Evidence Graph；Human Wiki 只提供显著性、术语和叙事结构。
- **事实最低状态**：`supported`
- `supported`：可以作为项目事实使用，但回答中必须引用 claim_id 和证据路径。
- `weak`：只能作为低置信度线索，必须要求用户继续核实。
- `inferred`：只能用于风险提示或待确认问题，不能包装成项目事实。
- `unverified`：不得作为事实使用，应明确说证据不足。
- `contradicted`：必须展示冲突来源，不得替用户强行选择一个版本。

## 它最适合谁

- **AI 研究者或研究型 Agent 构建者**：README 明确围绕研究、实验或论文工作流展开。 证据：`README.md` Claim：`clm_0004` supported 0.86
- **正在使用 Claude/Codex/Cursor/Gemini 等宿主 AI 的开发者**：README 或插件配置提到多个宿主 AI。 证据：`README.md` Claim：`clm_0005` supported 0.86
- **希望把专业流程带进宿主 AI 的用户**：仓库包含 Skill 文档。 证据：`skills/a2a-orchestrator/SKILL.md`, `skills/aacp/SKILL.md`, `skills/acp-delegate-auto/SKILL.md`, `skills/agent-cryst/SKILL.md` 等 Claim：`clm_0006` supported 0.86

## 它能做什么

- **AI Skill / Agent 指令资产库**（可做安装前预览）：项目包含可被宿主 AI 读取的 Skill 或 Agent 指令文件，可用于把专业流程带入 Claude、Codex、Cursor 等宿主。 证据：`skills/a2a-orchestrator/SKILL.md`, `skills/aacp/SKILL.md`, `skills/acp-delegate-auto/SKILL.md`, `skills/agent-cryst/SKILL.md` 等 Claim：`clm_0001` supported 0.86
- **多宿主安装与分发**（需要安装后验证）：项目包含插件或 marketplace 配置，说明它面向一个或多个 AI 宿主的安装和分发。 证据：`.claude-plugin/marketplace.json` Claim：`clm_0002` supported 0.86
- **命令行启动或安装流程**（需要安装后验证）：项目文档中存在可执行命令，真实使用需要在本地或宿主环境中运行这些命令。 证据：`README.md`, `skills/ai-sdk-core/references/providers-quickstart.md` Claim：`clm_0003` supported 0.86

## 怎么开始

- `npx skills add https://github.com/s4b7-ai/s4b7-ai-skills` 证据：`README.md` Claim：`clm_0007` supported 0.86
- `npm install @ai-sdk/openai` 证据：`skills/ai-sdk-core/references/providers-quickstart.md` Claim：`clm_0008` supported 0.86
- `npm install @ai-sdk/anthropic` 证据：`skills/ai-sdk-core/references/providers-quickstart.md` Claim：`clm_0009` supported 0.86
- `npm install @ai-sdk/google` 证据：`skills/ai-sdk-core/references/providers-quickstart.md` Claim：`clm_0010` supported 0.86
- `npm install workers-ai-provider` 证据：`skills/ai-sdk-core/references/providers-quickstart.md` Claim：`clm_0011` supported 0.86

## 继续前判断卡

- **当前建议**：需要管理员/安全审批
- **为什么**：继续前可能涉及密钥、账号、外部服务或敏感上下文，建议先经过管理员或安全审批。

### 30 秒判断

- **现在怎么做**：需要管理员/安全审批
- **最小安全下一步**：先跑 Prompt Preview；若涉及凭证或企业环境，先审批再试装
- **先别相信**：这套流程是否适合你的工作方式不能直接相信。
- **继续会触碰**：宿主行为改变、命令执行、宿主 AI 配置

### 现在可以相信

- **适合人群线索：AI 研究者或研究型 Agent 构建者**（supported）：有 supported claim 或项目证据支撑，但仍不等于真实安装效果。 证据：`README.md` Claim：`clm_0004` supported 0.86
- **适合人群线索：正在使用 Claude/Codex/Cursor/Gemini 等宿主 AI 的开发者**（supported）：有 supported claim 或项目证据支撑，但仍不等于真实安装效果。 证据：`README.md` Claim：`clm_0005` supported 0.86
- **适合人群线索：希望把专业流程带进宿主 AI 的用户**（supported）：有 supported claim 或项目证据支撑，但仍不等于真实安装效果。 证据：`skills/a2a-orchestrator/SKILL.md`, `skills/aacp/SKILL.md`, `skills/acp-delegate-auto/SKILL.md`, `skills/agent-cryst/SKILL.md` 等 Claim：`clm_0006` supported 0.86
- **能力存在：AI Skill / Agent 指令资产库**（supported）：可以相信项目包含这类能力线索；是否适合你的具体任务仍要试用或安装后验证。 证据：`skills/a2a-orchestrator/SKILL.md`, `skills/aacp/SKILL.md`, `skills/acp-delegate-auto/SKILL.md`, `skills/agent-cryst/SKILL.md` 等 Claim：`clm_0001` supported 0.86
- **能力存在：多宿主安装与分发**（supported）：可以相信项目包含这类能力线索；是否适合你的具体任务仍要试用或安装后验证。 证据：`.claude-plugin/marketplace.json` Claim：`clm_0002` supported 0.86
- **能力存在：命令行启动或安装流程**（supported）：可以相信项目包含这类能力线索；是否适合你的具体任务仍要试用或安装后验证。 证据：`README.md`, `skills/ai-sdk-core/references/providers-quickstart.md` Claim：`clm_0003` supported 0.86

### 现在还不能相信

- **这套流程是否适合你的工作方式不能直接相信。**（unverified）：流程型 Skill 可能强约束 AI 行为；它能提升纪律，也可能拖慢你当前任务节奏。 证据：`.claude-plugin/marketplace.json`, `skills/a2a-orchestrator/SKILL.md`, `skills/aacp/SKILL.md`, `skills/acp-delegate-auto/SKILL.md` 等
- **不会和你已有 Claude/Cursor/Codex 规则冲突，不能直接相信。**（inferred）：开发流程 Skill 会改变澄清、计划、测试、验证等默认行为，必须在临时宿主里试。 证据：`.claude-plugin/marketplace.json`, `skills/a2a-orchestrator/SKILL.md`, `skills/aacp/SKILL.md`, `skills/acp-delegate-auto/SKILL.md` 等
- **真实输出质量不能在安装前相信。**（unverified）：Prompt Preview 只能展示引导方式，不能证明真实项目中的结果质量。
- **宿主 AI 版本兼容性不能在安装前相信。**（unverified）：Claude、Cursor、Codex、Gemini 等宿主加载规则和版本差异必须在真实环境验证。
- **不会污染现有宿主 AI 行为，不能直接相信。**（inferred）：Skill、plugin、AGENTS/CLAUDE/GEMINI 指令可能改变宿主 AI 的默认行为。 证据：`.claude-plugin/marketplace.json`, `skills/a2a-orchestrator/SKILL.md`, `skills/aacp/SKILL.md`, `skills/acp-delegate-auto/SKILL.md` 等
- **可安全回滚不能默认相信。**（unverified）：除非项目明确提供卸载和恢复说明，否则必须先在隔离环境验证。
- **真实安装后是否与用户当前宿主 AI 版本兼容？**（unverified）：兼容性只能通过实际宿主环境验证。 证据：`.claude-plugin/marketplace.json`
- **项目输出质量是否满足用户具体任务？**（unverified）：安装前预览只能展示流程和边界，不能替代真实评测。

### 继续会触碰什么

- **宿主行为改变**：澄清、计划、TDD、验证、收尾等默认开发节奏。 原因：这类 Skill 的价值和风险都来自强约束流程；必须先确认你愿意被它改变工作方式。 证据：`.claude-plugin/marketplace.json`, `skills/a2a-orchestrator/SKILL.md`, `skills/aacp/SKILL.md`, `skills/acp-delegate-auto/SKILL.md` 等
- **命令执行**：包管理器、网络下载、本地插件目录、项目配置或用户主目录。 原因：运行第一条命令就可能产生环境改动；必须先判断是否值得跑。 证据：`README.md`, `skills/ai-sdk-core/references/providers-quickstart.md`
- **宿主 AI 配置**：Claude/Codex/Cursor/Gemini/OpenCode 等宿主的 plugin、Skill 或规则加载配置。 原因：宿主配置会改变 AI 后续工作方式，可能和用户已有规则冲突。 证据：`.claude-plugin/marketplace.json`, `skills/a2a-orchestrator/SKILL.md`, `skills/aacp/SKILL.md`, `skills/acp-delegate-auto/SKILL.md` 等
- **本地环境或项目文件**：安装结果、插件缓存、项目配置或本地依赖目录。 原因：安装前无法证明写入范围和回滚方式，需要隔离验证。 证据：`.claude-plugin/marketplace.json`, `README.md`, `skills/ai-sdk-core/references/providers-quickstart.md`
- **环境变量 / API Key**：项目入口文档明确出现 API key、token、secret 或账号凭证配置。 原因：如果真实安装需要凭证，应先使用测试凭证并经过权限/合规判断。 证据：`skills/ai-sdk-core/references/production-patterns.md`, `skills/ai-sdk-core/references/providers-quickstart.md`, `skills/ai-sdk-core/references/top-errors.md`, `skills/codex-latex/SKILL.md` 等
- **宿主 AI 上下文**：AI Context Pack、Prompt Preview、Skill 路由、风险规则和项目事实。 原因：导入上下文会影响宿主 AI 后续判断，必须避免把未验证项包装成事实。

### 最小安全下一步

- **先跑 Prompt Preview**：先感受它会怎样改变 AI 的开发节奏，再决定是否让它进入真实宿主。（适用：任何项目都适用，尤其是输出质量未知时。）
- **只在隔离目录或测试账号试装**：避免安装命令污染主力宿主 AI、真实项目或用户主目录。（适用：存在命令执行、插件配置或本地写入线索时。）
- **先备份宿主 AI 配置**：Skill、plugin、规则文件可能改变 Claude/Cursor/Codex 的默认行为。（适用：存在插件 manifest、Skill 或宿主规则入口时。）
- **不要使用真实生产凭证**：环境变量/API key 一旦进入宿主或工具链，可能产生账号和合规风险。（适用：出现 API、TOKEN、KEY、SECRET 等环境线索时。）
- **安装后只验证一个最小任务**：先验证加载、兼容、输出质量和回滚，再决定是否深用。（适用：准备从试用进入真实工作流时。）

### 退出方式

- **保留安装前状态**：记录原始宿主配置和项目状态，后续才能判断是否可恢复。
- **准备移除宿主 plugin / Skill / 规则入口**：如果试装后行为异常，可以把宿主 AI 恢复到试装前状态。
- **记录安装命令和写入路径**：没有明确卸载说明时，至少要知道哪些目录或配置需要手动清理。
- **准备撤销测试 API key 或 token**：测试凭证泄露或误用时，可以快速止损。
- **如果没有回滚路径，不进入主力环境**：不可回滚是继续前阻断项，不应靠信任或运气继续。

## 哪些只能预览

- 解释项目适合谁和能做什么
- 基于项目文档演示典型对话流程
- 帮助用户判断是否值得安装或继续研究

## 哪些必须安装后验证

- 真实安装 Skill、插件或 CLI
- 执行脚本、修改本地文件或访问外部服务
- 验证真实输出质量、性能和兼容性

## 边界与风险判断卡

- **把安装前预览误认为真实运行**：用户可能高估项目已经完成的配置、权限和兼容性验证。 处理方式：明确区分 prompt_preview_can_do 与 runtime_required。 Claim：`clm_0012` inferred 0.45
- **宿主 AI 插件或 Skill 规则冲突**：新规则可能改变用户现有宿主 AI 的工作方式。 处理方式：安装前先检查插件 manifest 和 Skill 文件，必要时隔离测试。 证据：`.claude-plugin/marketplace.json` Claim：`clm_0013` supported 0.86
- **命令执行会修改本地环境**：安装命令可能写入用户主目录、宿主插件目录或项目配置。 处理方式：先在隔离环境或测试账号中运行。 证据：`README.md`, `skills/ai-sdk-core/references/providers-quickstart.md` Claim：`clm_0014` supported 0.86
- **待确认**：真实安装后是否与用户当前宿主 AI 版本兼容？。原因：兼容性只能通过实际宿主环境验证。
- **待确认**：项目输出质量是否满足用户具体任务？。原因：安装前预览只能展示流程和边界，不能替代真实评测。
- **待确认**：安装命令是否需要网络、权限或全局写入？。原因：这影响企业环境和个人环境的安装风险。

## 开工前工作上下文

### 加载顺序

- 先读取 how_to_use.host_ai_instruction，建立安装前判断资产的边界。
- 读取 claim_graph_summary，确认事实来自 Claim/Evidence Graph，而不是 Human Wiki 叙事。
- 再读取 intended_users、capabilities 和 quick_start_candidates，判断用户是否匹配。
- 需要执行具体任务时，优先查 role_skill_index，再查 evidence_index。
- 遇到真实安装、文件修改、网络访问、性能或兼容性问题时，转入 risk_card 和 boundaries.runtime_required。

### 任务路由

- **AI Skill / Agent 指令资产库**：先基于 role_skill_index / evidence_index 帮用户挑选可用角色、Skill 或工作流。 边界：可做安装前 Prompt 体验。 证据：`skills/a2a-orchestrator/SKILL.md`, `skills/aacp/SKILL.md`, `skills/acp-delegate-auto/SKILL.md`, `skills/agent-cryst/SKILL.md` 等 Claim：`clm_0001` supported 0.86
- **多宿主安装与分发**：先说明这是安装后验证能力，再给出安装前检查清单。 边界：必须真实安装或运行后验证。 证据：`.claude-plugin/marketplace.json` Claim：`clm_0002` supported 0.86
- **命令行启动或安装流程**：先说明这是安装后验证能力，再给出安装前检查清单。 边界：必须真实安装或运行后验证。 证据：`README.md`, `skills/ai-sdk-core/references/providers-quickstart.md` Claim：`clm_0003` supported 0.86

### 上下文规模

- 文件总数：77
- 重要文件覆盖：40/77
- 证据索引条目：47
- 角色 / Skill 条目：34

### 证据不足时的处理

- **missing_evidence**：说明证据不足，要求用户提供目标文件、README 段落或安装后验证记录；不要补全事实。
- **out_of_scope_request**：说明该任务超出当前 AI Context Pack 证据范围，并建议用户先查看 Human Manual 或真实安装后验证。
- **runtime_request**：给出安装前检查清单和命令来源，但不要替用户执行命令或声称已执行。
- **source_conflict**：同时展示冲突来源，标记为待核实，不要强行选择一个版本。

## Prompt Recipes

### 适配判断

- 目标：判断这个项目是否适合用户当前任务。
- 预期输出：适配结论、关键理由、证据引用、安装前可预览内容、必须安装后验证内容、下一步建议。

```text
请基于 s4b7-ai-skills 的 AI Context Pack，先问我 3 个必要问题，然后判断它是否适合我的任务。回答必须包含：适合谁、能做什么、不能做什么、是否值得安装、证据来自哪里。所有项目事实必须引用 evidence_refs、source_paths 或 claim_id。
```

### 安装前体验

- 目标：让用户在安装前感受核心工作流，同时避免把预览包装成真实能力或营销承诺。
- 预期输出：一段带边界标签的体验剧本、安装后验证清单和谨慎建议；不含真实运行承诺或强营销表述。

```text
请把 s4b7-ai-skills 当作安装前体验资产，而不是已安装工具或真实运行环境。

请严格输出四段：
1. 先问我 3 个必要问题。
2. 给出一段“体验剧本”：用 [安装前可预览]、[必须安装后验证]、[证据不足] 三种标签展示它可能如何引导工作流。
3. 给出安装后验证清单：列出哪些能力只有真实安装、真实宿主加载、真实项目运行后才能确认。
4. 给出谨慎建议：只能说“值得继续研究/试装”“先补充信息后再判断”或“不建议继续”，不得替项目背书。

硬性边界：
- 不要声称已经安装、运行、执行测试、修改文件或产生真实结果。
- 不要写“自动适配”“确保通过”“完美适配”“强烈建议安装”等承诺性表达。
- 如果描述安装后的工作方式，必须使用“如果安装成功且宿主正确加载 Skill，它可能会……”这种条件句。
- 体验剧本只能写成“示例台词/假设流程”：使用“可能会询问/可能会建议/可能会展示”，不要写“已写入、已生成、已通过、正在运行、正在生成”。
- Prompt Preview 不负责给安装命令；如用户准备试装，只能提示先阅读 Quick Start 和 Risk Card，并在隔离环境验证。
- 所有项目事实必须来自 supported claim、evidence_refs 或 source_paths；inferred/unverified 只能作风险或待确认项。

```

### 角色 / Skill 选择

- 目标：从项目里的角色或 Skill 中挑选最匹配的资产。
- 预期输出：候选角色或 Skill 列表，每项包含适用场景、证据路径、风险边界和是否需要安装后验证。

```text
请读取 role_skill_index，根据我的目标任务推荐 3-5 个最相关的角色或 Skill。每个推荐都要说明适用场景、可能输出、风险边界和 evidence_refs。
```

### 风险预检

- 目标：安装或引入前识别环境、权限、规则冲突和质量风险。
- 预期输出：环境、权限、依赖、许可、宿主冲突、质量风险和未知项的检查清单。

```text
请基于 risk_card、boundaries 和 quick_start_candidates，给我一份安装前风险预检清单。不要替我执行命令，只说明我应该检查什么、为什么检查、失败会有什么影响。
```

### 宿主 AI 开工指令

- 目标：把项目上下文转成一次对话开始前的宿主 AI 指令。
- 预期输出：一段边界明确、证据引用明确、适合复制给宿主 AI 的开工前指令。

```text
请基于 s4b7-ai-skills 的 AI Context Pack，生成一段我可以粘贴给宿主 AI 的开工前指令。这段指令必须遵守 not_runtime=true，不能声称项目已经安装、运行或产生真实结果。
```


## 角色 / Skill 索引

- 共索引 34 个角色 / Skill / 项目文档条目。

- **A2A Orchestrator**（skill）：Use when the operator wants to orchestrate multiple OpenCode sessions from a single orchestrator session. Trigger on "orchestrator", "grid", "split sessions", "control other sessions", "dispatch to sessions", "2x2 grid", "agent to agent", "A2A", "spawn workers", "worker sessions". 激活提示：当用户任务与“A2A Orchestrator”描述的流程高度相关时，先用它做安装前体验，再决定是否安装。 证据：`skills/a2a-orchestrator/SKILL.md`
- **Aacp**（skill）：Use when the user needs: Agent Access Control Plane — manage agent resource access via Google Sheets approval workflow. Request, approve, deny, revoke, audit grants. Trigger on "access control", "grant access", "revoke access", "approval workflow", "AACP", "agent permission", "access request". 激活提示：当用户任务与“Aacp”描述的流程高度相关时，先用它做安装前体验，再决定是否安装。 证据：`skills/aacp/SKILL.md`
- **ACP Delegate Auto**（skill）：Use when the user needs: Auto-route a task to the optimal ACP provider based on task type, mesh availability, and cost. Use when you want Shadow to pick the best agent automatically — code → codex, research → gemini, local/private → ollama, complex reasoning → Codex. Trigger on "delegate auto", "best agent for", "route this to", "/acp-delegate-auto", or any task where provider selection should be automatic. 激活提示：当用户任务与“ACP Delegate Auto”描述的流程高度相关时，先用它做安装前体验，再决定是否安装。 证据：`skills/acp-delegate-auto/SKILL.md`
- **Agent Cryst**（skill）：Use when the user needs: Global always-on crystallization protocol. Use when the operator asks to crystallize, make learnings reusable, create a crystallizing agent, or when a session produces a reusable pattern that should propagate beyond the current chat. 激活提示：当用户任务与“Agent Cryst”描述的流程高度相关时，先用它做安装前体验，再决定是否安装。 证据：`skills/agent-cryst/SKILL.md`
- **AI SDK Core**（skill）：Use when building backend AI with Vercel AI SDK v5/v6. Covers v6 beta Agent abstraction, tool approval, reranking , v4→v5 migration breaking changes , latest models GPT-5/5.1, Claude 4.x, Gemini 2.5 , Workers startup fix, and 12 error solutions AI APICallError, AI NoObjectGeneratedError, streamText silent errors . Use when: implementing AI SDK v5/v6, migrating v4→v5, troubleshooting errors, fixing Workers startup is… 激活提示：当用户任务与“AI SDK Core”描述的流程高度相关时，先用它做安装前体验，再决定是否安装。 证据：`skills/ai-sdk-core/SKILL.md`
- **Codex Context Bridge**（skill）：Unified Codex CLI + Claude Code + Hermes + Gemini agent context for Craft Agents. Brings all agent memories, rules, prompts, patterns, and operational context from across the fleet into a single skill. 激活提示：当用户任务与“Codex Context Bridge”描述的流程高度相关时，先用它做安装前体验，再决定是否安装。 证据：`skills/codex-context/SKILL.md`
- **Codex Latex**（skill）：Use when generating laTeX scientific papers and technical documents using AI models. OpenAI Codex integration for structured academic writing, IEEE/ACM templates, patent specifications. Trigger on "LaTeX", "scientific paper", "research paper", "Codex", "academic writing", "IEEE template", "technical paper". 激活提示：当用户任务与“Codex Latex”描述的流程高度相关时，先用它做安装前体验，再决定是否安装。 证据：`skills/codex-latex/SKILL.md`
- **Codex Policy Steering**（skill）：Use when a Codex request hits cyber policy, Trusted Access, safety classifier, false-positive, blocked prompt, or when the user asks to analyze why a prompt was flagged and how to rephrase legitimate work safely. 激活提示：当用户任务与“Codex Policy Steering”描述的流程高度相关时，先用它做安装前体验，再决定是否安装。 证据：`skills/codex-policy-steering/SKILL.md`
- **Command Center**（skill）：Use when the user needs: Agent Command Center — orchestrate the Upscaler engine, mesh nodes, signals, SCARs, PPAP, and multi-agent dispatch from a single interface. Use when managing engineering programs, checking system health, dispatching work, or coordinating across JARVIS/FRIDAY/ULTRON/AURION. 激活提示：当用户任务与“Command Center”描述的流程高度相关时，先用它做安装前体验，再决定是否安装。 证据：`skills/command-center/SKILL.md`
- **Crystallize**（skill）：Use when completing a novel multi-step task involving significant reasoning, research, or pattern discovery. Trigger on "crystallize", "capture this as a skill", "make this reusable", "create a skill from this", or any deep work session where the agent solved something non-trivial that could recur. 激活提示：当用户任务与“Crystallize”描述的流程高度相关时，先用它做安装前体验，再决定是否安装。 证据：`skills/crystallize/SKILL.md`
- **Generative Latent Intelligence**（skill）：Use when generating skills, commands, scripts, or code using multi-model inference. Trigger on "generate skill", "gli", "create skill from", "generative", "skill factory", "upgrade latent", "multi-model", "use all models". 激活提示：当用户任务与“Generative Latent Intelligence”描述的流程高度相关时，先用它做安装前体验，再决定是否安装。 证据：`skills/gli/SKILL.md`
- **goal-loop**（skill）：Persistent objective completion loop for enterprise tasks. Set a goal, loop with completion audit until verified done or budget exhausted. Adapted from Codex /goal v0.128.0 for M3 Engineering Stack workflows: meeting prep, report quality, tracker updates, and multi-step enterprise deliverables. 激活提示：当用户任务与“goal-loop”描述的流程高度相关时，先用它做安装前体验，再决定是否安装。 证据：`skills/goal-loop/SKILL.md`
- **LangGraph Agent**（skill）：Use when building stateful AI agent workflows using LangGraph and LangChain. Use this skill whenever the user mentions LangGraph, LangChain, agent graphs, stateful agents, tool-calling loops, multi-agent orchestration, agentic workflows, conditional routing, graph-based AI, ReAct agents, or wants to build an AI agent with Python that has tools, memory, or multi-step reasoning. Also trigger when you see imports from… 激活提示：当用户任务与“LangGraph Agent”描述的流程高度相关时，先用它做安装前体验，再决定是否安装。 证据：`skills/langgraph-agent/SKILL.md`
- **Model Change Detector**（skill）：Use when detecting actual Codex model changes and trigger follow-up updates only when the model changed. 激活提示：当用户任务与“Model Change Detector”描述的流程高度相关时，先用它做安装前体验，再决定是否安装。 证据：`skills/model-change-detector/SKILL.md`
- **Model Identity**（skill）：Use when detecting current Codex model from local config and cache without API calls. 激活提示：当用户任务与“Model Identity”描述的流程高度相关时，先用它做安装前体验，再决定是否安装。 证据：`skills/model-identity/SKILL.md`
- **Multi Model Query**（skill）：Use when querying multiple AI models for creative synthesis, design perspectives, and multi-viewpoint analysis. Uses Ollama local models, cloud APIs when keys available , and browser-based free models. Use when the user wants perspectives from ALL models or multi-model synthesis. 激活提示：当用户任务与“Multi Model Query”描述的流程高度相关时，先用它做安装前体验，再决定是否安装。 证据：`skills/multi-model-query/SKILL.md`
- **OpenCode Runtime Identity**（skill）：Use when the user needs: Runtime identity reference for this OpenCode instance on JARVIS. Use when agent behavior depends on the local OpenCode binary, model catalog, plugins, or actual runtime constraints. 激活提示：当用户任务与“OpenCode Runtime Identity”描述的流程高度相关时，先用它做安装前体验，再决定是否安装。 证据：`skills/opencode-runtime-identity/SKILL.md`
- **Patent Queue**（skill）：Use when managing the Shadow Lab patent portfolio — SP-001/002/003 status, draft patents, track filing, review candidates from GLM mining agents. Trigger on "patent", "SP-001", "SP-002", "SP-003", "patent status", "IP portfolio", "filing", "patent review". 激活提示：当用户任务与“Patent Queue”描述的流程高度相关时，先用它做安装前体验，再决定是否安装。 证据：`skills/patent-queue/SKILL.md`
- **Persona Engine**（skill）：Use when managing personas, checking persona stats, switching identity routing, or understanding persona-aware model selection. Trigger on "persona", "switch persona", "who am I", "sabarish persona", "ishuru persona", "leo persona", "sabbu persona", "persona stats", "identity routing", "zone routing". 激活提示：当用户任务与“Persona Engine”描述的流程高度相关时，先用它做安装前体验，再决定是否安装。 证据：`skills/persona-engine/SKILL.md`
- **QA**（skill）：Use when the user needs: Interactive QA session where user reports bugs or issues conversationally, and the agent files GitHub issues. Explores the codebase in the background for context and domain language. Use when user wants to report bugs, do QA, file issues conversationally, or mentions "QA session". 激活提示：当用户任务与“QA”描述的流程高度相关时，先用它做安装前体验，再决定是否安装。 证据：`skills/qa/SKILL.md`
- **QMD Memory**（skill）：Use when the user needs: Supermemory-powered memory layer for QMD -- store, recall, extract, relate, and profile memories with graph relations, temporal reasoning, and auto-extraction. 激活提示：当用户任务与“QMD Memory”描述的流程高度相关时，先用它做安装前体验，再决定是否安装。 证据：`skills/qmd-memory/SKILL.md`
- **Re-Skill Loop**（skill）：Use when the operator says re-skill, re-skills, shadow-extract, recent/reuse/reduce/recycle/redo/revision/rebirth, or wants a workflow improved while resolving a recent item from YouTube, GitHub, X, Reddit, browser history, or another service. 激活提示：当用户任务与“Re-Skill Loop”描述的流程高度相关时，先用它做安装前体验，再决定是否安装。 证据：`skills/re-skill-loop/SKILL.md`
- **Self Observation Loop**（skill）：Use when the user needs: Spawn and observe a parallel OpenCode session through pane mirroring, logs, and vision-assisted monitoring to create a crude but real self-observation loop. 激活提示：当用户任务与“Self Observation Loop”描述的流程高度相关时，先用它做安装前体验，再决定是否安装。 证据：`skills/self-observation-loop/SKILL.md`
- **SHADOW Anchor**（skill）：Use when the user needs: Auto-save session as a temporal anchor — summarize deliverables, decisions, queued items, generate kernel training pairs, compute delta from previous anchor, and feed the shadow-research pipeline. Implements SP-004. Trigger on "anchor session", "save session", "session summary", "what did we do", end of session, before /clear. 激活提示：当用户任务与“SHADOW Anchor”描述的流程高度相关时，先用它做安装前体验，再决定是否安装。 证据：`skills/shadow-anchor/SKILL.md`
- **SHADOW Code Identity**（skill）：Use when detecting active Shadow context zone and enforce behavioral boundaries across personal, enterprise, and lab work. 激活提示：当用户任务与“SHADOW Code Identity”描述的流程高度相关时，先用它做安装前体验，再决定是否安装。 证据：`skills/shadow-code-identity/SKILL.md`
- **SHADOW Continuity**（skill）：Use when exploratory research, architecture work, or multi-source synthesis should be continuously externalized into repo artifacts instead of staying in chat, with later promotion into runtime, skills, wrappers, or project packs. 激活提示：当用户任务与“SHADOW Continuity”描述的流程高度相关时，先用它做安装前体验，再决定是否安装。 证据：`skills/shadow-continuity/SKILL.md`
- **SHADOW Engg**（skill）：Use when the user needs: Meta-exploration engine for the shadow-verse — discovers patterns, uncovers gaps, synthesizes tools, spawns sub-agents, maintains context across all shadow spaces. Also retains original Caresoft/iceberg engineering intelligence. Trigger on "shadow-engg", "explore", "discover", "what''s missing", "scan skills", "find patterns", "synthesize", "gap analysis", "verse report", "iceberg", "teardow… 激活提示：当用户任务与“SHADOW Engg”描述的流程高度相关时，先用它做安装前体验，再决定是否安装。 证据：`skills/shadow-engg/SKILL.md`
- **SHADOW MCP Gadgets**（skill）：Use when interacting with shadow-mcp tools — arc memory , esp context , ant mesh , tap browser , owl ambient , orb voice , ink writing , den physical , factory patents . Trigger on "shadow-mcp", "gadget", "arc.store", "esp.assemble", "ant.mesh", "tap.tabs", "owl.brief", "orb.say", "den.environment", "MCP tools", "16 tools". 激活提示：当用户任务与“SHADOW MCP Gadgets”描述的流程高度相关时，先用它做安装前体验，再决定是否安装。 证据：`skills/shadow-mcp-gadgets/SKILL.md`
- **SHADOW Mind**（skill）：Use when the user needs: Context assembly, state computation, context health, experience capture, latent pattern detection. Use on "what am I working on", "context", "catch me up", "system state", "context health", "memory stats", "renew context", "what focus mode", "operator presence". 激活提示：当用户任务与“SHADOW Mind”描述的流程高度相关时，先用它做安装前体验，再决定是否安装。 证据：`skills/shadow-mind/SKILL.md`
- **SHADOW Patent Factory**（skill）：Use when generating provisional patent applications from invention descriptions. Auto-researches prior art, drafts specification with claims, produces filing guide, and creates technical drawings. Full USPTO-ready patent package in one invocation. 激活提示：当用户任务与“SHADOW Patent Factory”描述的流程高度相关时，先用它做安装前体验，再决定是否安装。 证据：`skills/shadow-patent-factory/SKILL.md`
- **SHADOW Pi**（skill）：Use when the user needs: Pi monorepo integration — coding agent, unified AI API, TUI library, GPU pods, Slack bot, DOE harness. Located on AURION at /data/ShadowArchive/10-projects/pi-mono-doe/. Trigger on "pi", "coding agent", "pi-mono", "pi-ai", "pods", "GPU", "vLLM", "DOE", "harness", "pi TUI". 激活提示：当用户任务与“SHADOW Pi”描述的流程高度相关时，先用它做安装前体验，再决定是否安装。 证据：`skills/shadow-pi/SKILL.md`
- **SHADOW Refactor**（skill）：Use when the user needs: Autonomous code improvement engine — extracts patterns from the codebase, runs TDD loops with local compute Ollama , and self-fixes code in the background. Use when the user says "refactor", "improve code", "clean up", "make it better", "optimize", "self-fix", "shadow refactor", "run in background and fix", "extract patterns", "TDD loop", or when you notice code that could be improved after… 激活提示：当用户任务与“SHADOW Refactor”描述的流程高度相关时，先用它做安装前体验，再决定是否安装。 证据：`skills/shadow-refactor/SKILL.md`
- **Skill Surgery R&D**（skill）：Audit, repair, merge, and research agent skills as a governed R&D loop. Use when the operator asks to find skill gaps, do skill surgery, improve skill triggers, merge duplicate/deprecated skills, research new skills, or develop reusable agent capabilities. 激活提示：当用户任务与“Skill Surgery R&D”描述的流程高度相关时，先用它做安装前体验，再决定是否安装。 证据：`skills/skill-surgery-rd/SKILL.md`
- **Write A Skill**（skill）：Use when creating new agent skills with proper structure, progressive disclosure, and bundled resources. Use when user wants to create, write, or build a new skill. 激活提示：当用户任务与“Write A Skill”描述的流程高度相关时，先用它做安装前体验，再决定是否安装。 证据：`skills/write-a-skill/SKILL.md`

## 证据索引

- 共索引 47 条证据。

- **s4b7-ai-skills**（documentation）：Agent skills for AI/ML workflows — model orchestration, multi-model inference, agent building, policy steering, and self-improvement loops. 证据：`README.md`
- **Package**（package_manifest）：{ "name": "s4b7-ai-skills", "version": "2.0.0", "description": "34 AI/ML agent skills for model orchestration, multi-model inference, and self-improvement.", "files": "skills/", ".claude-plugin/", "guides/", "README.md" , "scripts": { "postinstall": "node scripts/install.js" }, "keywords": "agent-skills", "ai", "langgraph", "multi-model", "codex", "craft-agent" , "author": "s4b7-ai", "license": "MIT", "repository": { "type": "git", "url": "https://github.com/s4b7-ai/s4b7-ai-skills.git" } } 证据：`package.json`
- **A2A Orchestrator — CLI-to-CLI Multi-Session Control**（skill_instruction）：A2A Orchestrator — CLI-to-CLI Multi-Session Control 证据：`skills/a2a-orchestrator/SKILL.md`
- **AACP — Agent Access Control Plane**（skill_instruction）：Manage agent access to resources via Google Sheets approval workflow. 证据：`skills/aacp/SKILL.md`
- **ACP Delegate Auto — Intelligent Task Routing**（skill_instruction）：ACP Delegate Auto — Intelligent Task Routing 证据：`skills/acp-delegate-auto/SKILL.md`
- **Agent-Cryst**（skill_instruction）：Agent-Cryst turns discoveries into durable active truth. 证据：`skills/agent-cryst/SKILL.md`
- **AI SDK Core**（skill_instruction）：Backend AI implementation guide for Vercel AI SDK v5/v6 work. Keep this file trigger-fast; load REFERENCE.md only for detailed migrations, model/version notes, and error recipes. 证据：`skills/ai-sdk-core/SKILL.md`
- **Codex Context Bridge — Unified Agent Fleet Context**（skill_instruction）：Codex Context Bridge — Unified Agent Fleet Context 证据：`skills/codex-context/SKILL.md`
- **Codex LaTeX — Scientific Paper Generator**（skill_instruction）：Codex LaTeX — Scientific Paper Generator 证据：`skills/codex-latex/SKILL.md`
- **Codex Policy Steering**（skill_instruction）：Default to policy/error forensics and safe intent steering , not bypass. Separate evidence from inference, make benign scope explicit, and rewrite ambiguous requests before building automation. 证据：`skills/codex-policy-steering/SKILL.md`
- **Agent Command Center**（skill_instruction）：Unified interface for orchestrating the Upscaler temporal accumulation engine and the OpenClaw mesh network. Inspired by Karpathy's "agent command center" concept — the developer as orchestrator, not executor. 证据：`skills/command-center/SKILL.md`
- **Crystallize**（skill_instruction）：Self-introspection to skill crystallization. After novel deep work, extract reusable patterns into formal skills so future sessions don't redo the same reasoning. 证据：`skills/crystallize/SKILL.md`
- **GLI — Generative Line Interface**（skill_instruction）：Multi-model skill factory adapted for iTerm2. The active complement to SP-006's passive observation. 证据：`skills/gli/SKILL.md`
- **Goal Loop — Persistent Objective Completion**（skill_instruction）：Goal Loop — Persistent Objective Completion 证据：`skills/goal-loop/SKILL.md`
- **LangGraph Agent Development**（skill_instruction）：Build production-grade AI agent workflows using LangGraph graph orchestration + LangChain LLM abstraction . 证据：`skills/langgraph-agent/SKILL.md`
- **Model Change Detector**（skill_instruction）：Trigger: ONLY when model actually changes, not every session 证据：`skills/model-change-detector/SKILL.md`
- **Model Identity**（skill_instruction）：Trigger: Session start, "which model?", model change detection 证据：`skills/model-identity/SKILL.md`
- **Multi-Model Query — Cross-Model Synthesis**（skill_instruction）：Multi-Model Query — Cross-Model Synthesis 证据：`skills/multi-model-query/SKILL.md`
- **OpenCode Runtime Identity**（skill_instruction）：I run as OpenCode v1.4.0 on JARVIS. This is my runtime identity — know it cold. 证据：`skills/opencode-runtime-identity/SKILL.md`
- **Patent Queue — Shadow Lab IP Portfolio**（skill_instruction）：Patent Queue — Shadow Lab IP Portfolio 证据：`skills/patent-queue/SKILL.md`
- **Persona Engine — Identity-Aware Model Routing**（skill_instruction）：Persona Engine — Identity-Aware Model Routing 证据：`skills/persona-engine/SKILL.md`
- **QA Session**（skill_instruction）：Run an interactive QA session. The user describes problems they're encountering. You clarify, explore the codebase for context, and file GitHub issues that are durable, user-focused, and use the project's domain language. 证据：`skills/qa/SKILL.md`
- **QMD-Memory**（skill_instruction）：Supermemory-style memory management built on QMD's local-first search engine. Adds memory graph, temporal reasoning, user profiling, and automatic extraction. 证据：`skills/qmd-memory/SKILL.md`
- **Re-Skill Loop**（skill_instruction）：A re-skill is a repeatable extractor/workflow that learns while it runs: resolve the operator's current/recent source, do the requested action, then reduce the pattern into a reusable skill patch. 证据：`skills/re-skill-loop/SKILL.md`
- **Self-Observation Loop**（skill_instruction）：Spawn a clone opencode session in an adjacent pane, capture its output, and read it back — creating a self-referential feedback loop. 证据：`skills/self-observation-loop/SKILL.md`
- **shadow-anchor — Temporal Session Anchoring**（skill_instruction）：shadow-anchor — Temporal Session Anchoring 证据：`skills/shadow-anchor/SKILL.md`
- **Shadow Code Identity**（skill_instruction）：Trigger: Session start, context switches, enterprise/personal boundary crossing 证据：`skills/shadow-code-identity/SKILL.md`
- **shadow-continuity**（skill_instruction）：Continuous research-to-repo harness for shadow-ent , company wrappers, and project packs. 证据：`skills/shadow-continuity/SKILL.md`
- **shadow-engg — Meta-Exploration Engine**（skill_instruction）：shadow-engg — Meta-Exploration Engine 证据：`skills/shadow-engg/SKILL.md`
- **shadow-mcp — 8 Gadgets, 16 Tools**（skill_instruction）：Federation MCP server at /Volumes/SHADOW/shadow-lab/apps/shadow-mcp/ . Registered in /Volumes/SHADOW/.mcp.json . Runs via bun . 证据：`skills/shadow-mcp-gadgets/SKILL.md`
- **Shadow Mind**（skill_instruction）：Op How ---- ----- Context Assembly esp.assemble — git, mesh, memory, tabs, Bee, cloud State Focus detection, synthesized sensors, operator presence, mesh health Health Memory count, skill inventory, stale detection, disk per category Experience Write discoveries + patterns to memory for future sessions Latent Patterns SP-006 PostToolUse hook monitors tool sequences, flags for crystallization 证据：`skills/shadow-mind/SKILL.md`
- **Shadow Patent Factory**（skill_instruction）：Generate a complete USPTO provisional patent application from an invention description. 证据：`skills/shadow-patent-factory/SKILL.md`
- **shadow-pi — Pi Monorepo Integration**（skill_instruction）：shadow-pi — Pi Monorepo Integration 证据：`skills/shadow-pi/SKILL.md`
- **shadow-refactor — The Autonomous Code Shadow**（skill_instruction）：shadow-refactor — The Autonomous Code Shadow 证据：`skills/shadow-refactor/SKILL.md`
- **Skill Surgery R&D**（skill_instruction）：Governed loop for turning a large skill garden into a smaller, sharper capability system. 证据：`skills/skill-surgery-rd/SKILL.md`
- **Writing Skills**（skill_instruction）：1. Gather requirements - ask user about: - What task/domain does the skill cover? - What specific use cases should it handle? - Does it need executable scripts or just instructions? - Any reference materials to include? 证据：`skills/write-a-skill/SKILL.md`
- **Marketplace**（structured_config）：{ "name": "s4b7-ai-skills", "description": "", "version": "2.0.0", "skills": "./skills/a2a-orchestrator", "./skills/aacp", "./skills/acp-delegate-auto", "./skills/agent-cryst", "./skills/ai-sdk-core", "./skills/codex-context", "./skills/codex-latex", "./skills/codex-policy-steering", "./skills/command-center", "./skills/crystallize", "./skills/gli", "./skills/goal-loop", "./skills/langgraph-agent", "./skills/model-change-detector", "./skills/model-identity", "./skills/multi-model-query", "./skills/opencode-runtime-identity", "./skills/patent-queue", "./skills/persona-engine", "./skills/qa", "./skills/qmd-memory", "./skills/re-skill-loop", "./skills/self-observation-loop", "./skills/shadow-a… 证据：`.claude-plugin/marketplace.json`
- **s4b7-ai-skills Changelog**（documentation）：Added - Initial release with 16 AI/ML agent skills - Agent building: langgraph-agent, ai-sdk-core, agent-cryst, self-observation-loop - Skill engineering: write-a-skill, crystallize, skill-surgery-rd, re-skill-loop - Model & identity: model-identity, model-change-detector, persona-engine, multi-model-query, gli - Codex & policy: codex-context, codex-policy-steering, codex-latex 证据：`CHANGELOG.md`
- **AI SDK Core - Official Documentation Links**（documentation）：AI SDK Core - Official Documentation Links 证据：`skills/ai-sdk-core/references/links-to-official-docs.md`
- **AI SDK Core - Production Patterns**（documentation）：Best practices for deploying AI SDK Core in production environments. 证据：`skills/ai-sdk-core/references/production-patterns.md`
- **AI SDK Core - Providers Quick Start**（documentation）：AI SDK Core - Providers Quick Start 证据：`skills/ai-sdk-core/references/providers-quickstart.md`
- **AI SDK Core - Top 12 Errors & Solutions**（documentation）：AI SDK Core - Top 12 Errors & Solutions 证据：`skills/ai-sdk-core/references/top-errors.md`
- **AI SDK v4 → v5 Migration Guide**（documentation）：Complete guide to breaking changes from AI SDK v4 to v5. 证据：`skills/ai-sdk-core/references/v5-breaking-changes.md`
- **LangGraph Advanced Patterns**（documentation）：Supervisor Pattern A supervisor agent routes tasks to specialized worker agents: 证据：`skills/langgraph-agent/references/advanced-patterns.md`
- **Distribution**（structured_config）：{ "registry": "https://github.com/ishuru/ishuru-skill-distribution", "install": "curl -sL https://raw.githubusercontent.com/ishuru/ishuru-skill-distribution/main/install.sh bash", "accounts": 7, "repos": 11, "total skills": 396 } 证据：`distribution.json`
- **.gitignore**（source_file）：node modules/ .env .DS Store 证据：`.gitignore`
- **!/usr/bin/env node**（source_file）：!/usr/bin/env node console.log '' ; console.log '====================================' ; console.log ' s4b7-ai-skills v1.0.0' ; console.log ' 16 AI/ML agent skills' ; console.log '====================================' ; console.log '' ; console.log '✓ Installed!' ; console.log '' ; console.log 'Skills: langgraph-agent, ai-sdk-core, agent-cryst,' ; console.log ' self-observation-loop, write-a-skill, crystallize,' ; console.log ' skill-surgery-rd, re-skill-loop, model-identity,' ; console.log ' model-change-detector, persona-engine, multi-model-query,' ; console.log ' gli, codex-context, codex-policy-steering, codex-latex' ; console.log '' ; console.log 'https://github.com/s4b7-ai/s4b7-ai-ski… 证据：`scripts/install.js`

## 宿主 AI 必须遵守的规则

- **把本资产当作开工前上下文，而不是运行环境。**：AI Context Pack 只包含证据化项目理解，不包含目标项目的可执行状态。 证据：`README.md`, `package.json`, `skills/a2a-orchestrator/SKILL.md`
- **回答用户时区分可预览内容与必须安装后才能验证的内容。**：安装前体验的消费者价值来自降低误装和误判，而不是伪装成真实运行。 证据：`README.md`, `package.json`, `skills/a2a-orchestrator/SKILL.md`

## 用户开工前应该回答的问题

- 你准备在哪个宿主 AI 或本地环境中使用它？
- 你只是想先体验工作流，还是准备真实安装？
- 你最在意的是安装成本、输出质量、还是和现有规则的冲突？

## 验收标准

- 所有能力声明都能回指到 evidence_refs 中的文件路径。
- AI_CONTEXT_PACK.md 没有把预览包装成真实运行。
- 用户能在 3 分钟内看懂适合谁、能做什么、如何开始和风险边界。

---

## Doramagic Context Augmentation

下面内容用于强化 Repomix/AI Context Pack 主体。Human Manual 只提供阅读骨架；踩坑日志会被转成宿主 AI 必须遵守的工作约束。

## Human Manual 骨架

使用规则：这里只是项目阅读路线和显著性信号，不是事实权威。具体事实仍必须回到 repo evidence / Claim Graph。

宿主 AI 硬性规则：
- 不得把页标题、章节顺序、摘要或 importance 当作项目事实证据。
- 解释 Human Manual 骨架时，必须明确说它只是阅读路线/显著性信号。
- 能力、安装、兼容性、运行状态和风险判断必须引用 repo evidence、source path 或 Claim Graph。

- **项目概览**：importance `high`
  - source_paths: README.md, package.json, CHANGELOG.md
- **安装指南**：importance `high`
  - source_paths: package.json, distribution.json, scripts/install.js
- **技能文件结构**：importance `high`
  - source_paths: skills/write-a-skill/SKILL.md, skills/crystallize/SKILL.md
- **技能分类体系**：importance `high`
  - source_paths: README.md
- **LangGraph Agent技能**：importance `high`
  - source_paths: skills/langgraph-agent/SKILL.md, skills/langgraph-agent/references/advanced-patterns.md
- **AI SDK Core技能**：importance `high`
  - source_paths: skills/ai-sdk-core/SKILL.md, skills/ai-sdk-core/references/top-errors.md, skills/ai-sdk-core/references/v5-breaking-changes.md, skills/ai-sdk-core/references/links-to-official-docs.md
- **Agent-Cryst技能**：importance `medium`
  - source_paths: skills/agent-cryst/SKILL.md
- **编写技能指南**：importance `high`
  - source_paths: skills/write-a-skill/SKILL.md

## Repo Inspection Evidence / 源码检查证据

- repo_clone_verified: true
- repo_inspection_verified: true
- repo_commit: `6199a4881da01834ef6e6134db05f7d43c5fe424`
- inspected_files: `package.json`, `README.md`

宿主 AI 硬性规则：
- 没有 repo_clone_verified=true 时，不得声称已经读过源码。
- 没有 repo_inspection_verified=true 时，不得把 README/docs/package 文件判断写成事实。
- 没有 quick_start_verified=true 时，不得声称 Quick Start 已跑通。

## Doramagic Pitfall Constraints / 踩坑约束

这些规则来自 Doramagic 发现、验证或编译过程中的项目专属坑点。宿主 AI 必须把它们当作工作约束，而不是普通说明文字。

### Constraint 1: 仓库名和安装名不一致

- Trigger: 仓库名 `s4b7-ai-skills` 与安装入口 `skills` 不完全一致。
- Host AI rule: 在 npm/PyPI/GitHub 上确认包名映射和官方 README 说明。
- Why it matters: 用户照着仓库名搜索包或照着包名找仓库时容易走错入口。
- Evidence: identity.distribution | github_repo:1238156052 | https://github.com/s4b7-ai/s4b7-ai-skills | repo=s4b7-ai-skills; install=skills
- Hard boundary: 不要把这个坑点包装成已解决、已验证或可忽略，除非后续验证证据明确证明它已经关闭。

### Constraint 2: 能力判断依赖假设

- Trigger: README/documentation is current enough for a first validation pass.
- Host AI rule: 将假设转成下游验证清单。
- Why it matters: 假设不成立时，用户拿不到承诺的能力。
- Evidence: capability.assumptions | github_repo:1238156052 | https://github.com/s4b7-ai/s4b7-ai-skills | README/documentation is current enough for a first validation pass.
- Hard boundary: 不要把这个坑点包装成已解决、已验证或可忽略，除非后续验证证据明确证明它已经关闭。

### Constraint 3: 维护活跃度未知

- Trigger: 未记录 last_activity_observed。
- Host AI rule: 补 GitHub 最近 commit、release、issue/PR 响应信号。
- Why it matters: 新项目、停更项目和活跃项目会被混在一起，推荐信任度下降。
- Evidence: evidence.maintainer_signals | github_repo:1238156052 | https://github.com/s4b7-ai/s4b7-ai-skills | last_activity_observed missing
- Hard boundary: 不要把这个坑点包装成已解决、已验证或可忽略，除非后续验证证据明确证明它已经关闭。

### Constraint 4: 下游验证发现风险项

- Trigger: no_demo
- Host AI rule: 进入安全/权限治理复核队列。
- Why it matters: 下游已经要求复核，不能在页面中弱化。
- Evidence: downstream_validation.risk_items | github_repo:1238156052 | https://github.com/s4b7-ai/s4b7-ai-skills | no_demo; severity=medium
- Hard boundary: 不要把这个坑点包装成已解决、已验证或可忽略，除非后续验证证据明确证明它已经关闭。

### Constraint 5: 存在评分风险

- Trigger: no_demo
- Host AI rule: 把风险写入边界卡，并确认是否需要人工复核。
- Why it matters: 风险会影响是否适合普通用户安装。
- Evidence: risks.scoring_risks | github_repo:1238156052 | https://github.com/s4b7-ai/s4b7-ai-skills | no_demo; severity=medium
- Hard boundary: 不要把这个坑点包装成已解决、已验证或可忽略，除非后续验证证据明确证明它已经关闭。

### Constraint 6: issue/PR 响应质量未知

- Trigger: issue_or_pr_quality=unknown。
- Host AI rule: 抽样最近 issue/PR，判断是否长期无人处理。
- Why it matters: 用户无法判断遇到问题后是否有人维护。
- Evidence: evidence.maintainer_signals | github_repo:1238156052 | https://github.com/s4b7-ai/s4b7-ai-skills | issue_or_pr_quality=unknown
- Hard boundary: 不要把这个坑点包装成已解决、已验证或可忽略，除非后续验证证据明确证明它已经关闭。

### Constraint 7: 发布节奏不明确

- Trigger: release_recency=unknown。
- Host AI rule: 确认最近 release/tag 和 README 安装命令是否一致。
- Why it matters: 安装命令和文档可能落后于代码，用户踩坑概率升高。
- Evidence: evidence.maintainer_signals | github_repo:1238156052 | https://github.com/s4b7-ai/s4b7-ai-skills | release_recency=unknown
- Hard boundary: 不要把这个坑点包装成已解决、已验证或可忽略，除非后续验证证据明确证明它已经关闭。