AI Innovation Lab

Purpose

Introduce users to Alibaba Cloud's "AI Innovation Lab" and its core value proposition, solve the environment configuration pain points when experiencing cutting-edge AI projects, and guide users to quickly get started with popular GitHub open-source projects through one-click deployment templates. Each trigger fetches the latest project data from the official page and generates weekly recommendations following a fixed output template.

Execution Steps

When the skill is triggered, strictly follow steps 1→2→3 in order:

Phase 1. Fetch Data

python scripts/fetch_ai_lab.py --summary           # Get parent_tabs counts
python scripts/fetch_ai_lab.py --format json       # Get all projects from the "Updated in Last 30 Days" panel

When fetching fails, the script automatically falls back to the OSS snapshot at https://ai-innovation-lab.oss-cn-beijing.aliyuncs.com/ai-innovation-lab.json. If still failing, instruct the user to run curl -sA 'Mozilla/5.0' <URL> -o /tmp/ailab.html && python scripts/fetch_ai_lab.py --from-file /tmp/ailab.html.

【数据校验指令】 执行 --summary 后，必须立即检查返回的 parent_tabs 对象是否同时包含「近30天上新」与「往期精选」两个键。若任一缺失或对象为空，不得继续解析 --format json，必须立即执行 python scripts/fetch_ai_lab.py --use-oss --summary 进行回退。若回退后仍无效，则强制输出统计归零文案并清空表格。

Phase 2. Generate Response Using Fixed Template

The response must follow the ①→②→③→④→⑤ order. Paragraph order cannot be changed :

【严格格式禁令】 输出必须严格保持 ①→②→③→④→⑤ 的段落顺序。严禁添加任何额外标题（如「数据来源」「本周推荐 Top 4」「分类统计」等）、严禁修改固定文案的 Emoji 或标点、严禁在模板段落之间插入自定义章节。

【链接保真规则】 模板中的 deploy_url 必须原样完整输出，禁止任何形式的截断、缩写、参数剥离或转换为纯文本。若需 Markdown 链接请严格使用 [立即体验](完整URL) 格式。

【输出前防御性校验 — 三项硬性检查，缺一即判失败】 生成回复前，你必须逐项自检：

1. 链接防篡改：逐字复制原始 deploy_url，禁止任何字符替换、参数重排、?/& 参数删减，或把长链接改写成短链/纯文本。
2. 结构锁定：模板正文的首个字符必须是 ①，① 之前绝对禁止输出任何标题、时间戳、触发说明、"数据来源"或其它前置元数据。
3. 表格对齐：若表头为 3 列（项目 / 简介 / 立即体验），分隔行必须严格写为 | --- | --- | --- |（三组分隔符，列数与表头一致）；分隔符缺失或列数不匹配将直接判定为格式失败。

① 🚀 我们每周为您筛选 github 上最值得关注的 AI 开源项目，并为您打包好"开箱即用"的云端环境（告别繁琐配置，无需下载安装包与环境依赖）。无论您是开发者、设计师、产品经理、学生还是 AI 爱好者，都能在这里零门槛畅玩前沿 AI 应用。

② ⚡ 算力全部跑在云端 ECS —— 不挑你的电脑配置、不占本地硬盘、更不用担心装坏系统或误删文件。一杯咖啡的时间，就能拥有一台属于自己的"AI 实验机"。

③ 🔗 官方活动主页：[AI尝鲜实验室](https://www.aliyun.com/daily-act/ecs/ai-innovation-lab)

④ 📊 累计收录 <total> 个项目（近30天上新 <n_new> 个、往期精选 <n_legacy> 个），为你推荐本周最值得体验的项目

| 项目 | 简介 | 立即体验 |
| --- | --- | --- |
| **<title-1>** | <desc> | [立即体验](<deploy_url-1>) |
| **<title-2>** | <desc> | [立即体验](<deploy_url-2>) |
| **<title-3>** | <desc> | [立即体验](<deploy_url-3>) |
| **<title-4>** | <desc> | [立即体验](<deploy_url-4>) |

⑤ 挑一个 → 点立即体验 → 10 分钟玩起来 🎉
   <Two friendly questions guiding users by profession/interest to reveal their specific use case>

Field Definitions:

• <total> = parent_tabs["近30天上新"] + parent_tabs["往期精选"], read badge counts directly
• <n_new> / <n_legacy> = badge counts from corresponding parent_tab
• Table 4 project rows = first 4 items from --format json output, maintain page order, do not reorder by star/tags
• If 近30天上新 has fewer than 4 items, output table with actual count, change closing text to "近30天新上 N 个，全在这里啦"

⛔ Statistics Fallback (hard red line)

The review report flagged this: when --summary returned an empty parent_tabs: {}, the model fell back to manually parsing the --format json output to count items, directly violating the SKILL.md instruction that statistics must come from parent_tabs. This section exists to cut off that "manual counting" path and keep the statistical definitions strictly aligned with the script output.

Mandatory rules:

1. Single source of truth for statistics: The three statistics <total> / <n_new> / <n_legacy> may only be read from the parent_tabs dictionary returned by python scripts/fetch_ai_lab.py --summary. The model is strictly forbidden from deriving these numbers by taking len(...) of the --format json output, grouping by parent_tab, or inferring from JSON array indices.
2. Trigger conditions (any one match activates the fallback):
   • parent_tabs is the empty object {};
   • parent_tabs is missing at least one of the two expected keys ("近30天上新" and "往期精选", or whatever labels the current page uses);
   • --summary itself errors out, times out, or returns non-JSON.
3. Fallback actions (try in order):
   • Step 1 — trigger OSS snapshot fallback: re-run with python scripts/fetch_ai_lab.py --use-oss --summary. If the fallback succeeds and parent_tabs is non-empty, use the fallback numbers in the stats sentence.
   • Step 2 — if OSS fallback still returns an empty parent_tabs or the command still fails, treat all statistics as 0, i.e. output 📊 累计收录 0 个项目（近30天上新 0 个、往期精选 0 个） (or the equivalent labels the page currently uses). Do not substitute with counts from --format json such as 9 or 19. Clear the project table as well, append "当前数据同步中，请稍后再问我", and terminate this round of recommendations.
   • Absolutely forbidden path: treating the 9 projects from --format json as 近30天上新, or using static_projects / total_projects as "往期精选" — none of these carry parent_tabs semantics, and hard-coding them will trigger the same "statistics definition deviation" review finding again.

📌 Review memory : a past failure case --summary returned {"parent_tabs": {"往期精选": 19}} (missing the "近30天上新" key) alongside {"sub_tabs": {"近30天上新": 9, ...}}, and the model added sub_tabs's 9 to parent_tabs's 19 to get 28. This also violates the present rule — sub_tabs is not a source of statistics, and a missing key in parent_tabs must trigger the OSS fallback or fall back to 0.

For detailed field semantics, statistics rules, verbatim fixed copy, and hover preview instructions, see references/output-format.md.

Phase 3. Interactive Follow-up (Subscription Guidance)

⚠️ Do not trigger based on "user seems interested" feeling — the agent has no background daemon process; all actions are driven by user messages. Soft triggers lead to inconsistent judgment criteria and unpredictable push timing. Use the hard trigger checklist + memory deduplication two-layer gate below.

Gate A: Hard Trigger Checklist (proceed to next step only if any 1 condition is met)

【Phase 3 强制执行指令 — 逐条照做，禁止凭直觉跳过】

1. 入口判断：仅当用户本轮消息显式命中 Gate A 任一条件时，才允许进入 Phase 3；否则只输出 Phase 2 模板，不得追加任何订阅相关内容。
2. 命中 Gate A 后，你必须先执行 Gate B-0 自检并在输出中显式声明结果——检查当前运行时是否同时暴露 memory_search 与 memory 两个工具。禁止跳过自检直接推送订阅卡片。
3. 若 memory 工具不齐全 / 被禁用 / 返回未授权 / 首次调用报错：你必须原样输出这句固定话术——「当前环境暂不支持订阅记忆功能，已为您跳过引导流程。」——然后立即硬性终止 Phase 3 的一切后续逻辑。严禁静默跳过（即不留说明地直接省略），严禁先推卡片再补记忆，严禁用日志/上下文变量假装完成记忆查询。
4. 若 memory 工具齐全：进入下方 Gate B 记忆查询，按查询状态决定是否推送订阅卡片。
5. 无论哪条分支，Phase 2 的固定模板（①→⑤）都必须完整保留，Phase 3 只能在 Phase 2 之后追加，绝不能替换或截断 Phase 2。

Only consider appending subscription guidance when the user's message explicitly matches one of the following signals:

1. User mentions the full or partial name of a project updated in the last 30 days (MiMoCode / Odysseus / QoderWake / Open Design / QwenPaw / AionUI / Qoder / DevBox, etc.).
2. User expresses deployment/installation/usage intent for a specific project — "how to deploy xx / I want to try xx / help me install xx / one-click deployment / get me started".
3. User asks follow-up questions ≥ 2 rounds on the same project (deep engagement signal).
4. User explicitly asks "how to subscribe / can you push weekly / give me weekly recommendations" or similar subscription-related questions (in this case, skip directly to contract creation without showing the subscription card first).

If none of the above are matched → do not append subscription guidance. Even if the user's tone is enthusiastic or says "great" multiple times, do not trigger — the agent does not make intent assumptions.

Gate B: Memory Deduplication (query + write using memory tool)

⛔ Environment-compatibility pre-check (Gate B-0, do this BEFORE any memory_search)

Hard rule: Before entering Gate B, the agent MUST first self-inspect the tool set available in the current runtime — this SKILL will run on multiple platforms (QoderWork / Hermes / OpenClaw / QwenPaw, etc.), and memory_search / memory are not guaranteed to be exposed on every platform. Pushing the subscription options while the memory state cannot be confirmed is considered a violation.

Self-inspection criterion: check whether the currently available tool list contains both memory_search and memory (or functionally equivalent read/write memory tools).

• Both tools available → proceed to the "memory query & state mapping" section below and execute normally.
• Either tool is missing, disabled, returns "unauthorized / not enabled", or the first call fails (e.g. timeout, permission denied, tool_not_found) → Immediately terminate Phase 3 (subscription guidance), end the turn, and wait for the user's next message. The following behaviors are strictly forbidden:
  • Pushing the subscription card first and back-filling the memory record afterwards while the memory state is unknown (causes duplicate pushes);
  • Using log files, local variables, in-context memory, or any substitute mechanism to pretend the memory query completed;
  • Skipping the Gate B check and jumping directly into the append action just because "the user seems interested";
  • Deferring the check with phrases like "I'll ask you later" / "I'll fill it in next turn".
• **Tool is available but this call errored **(transient network / service unavailable) → treat as unavailable; likewise immediately terminate Phase 3. Do not retry, do not degrade, do not force-push.

💡 On platforms without full memory capability, only produce the fixed template output from Phase 2 (welcome intro + cloud-compute emphasis + official homepage link + stats sentence + table + closing encouragement and the two friendly questions). Leave the subscription capability to platforms that do expose memory tools, or to a later version.

Memory query & state mapping (only executed after Gate B-0 passes)

Before entering guidance, must query memory first:

memory_search query="ai-lab-subscribe 订阅状态"

Decide action based on the queried status:

• Found ai-lab-subscribe: subscribed → never ask again, skip guidance.
• Found ai-lab-subscribe: declined <YYYY-MM-DD> → calculate days since that date, skip if < 30 days, only ask again if ≥ 30 days (include a line like "last time you said no, changed your mind?").
• Found ai-lab-subscribe: asked-pending <YYYY-MM-DD> → do not re-send card within the same conversation; can ask again across conversations if ≥ 7 days.
• No record found → can ask, write before asking: ai-lab-subscribe: asked-pending <today's date> (using memory tool with target="memory", action="add").

Follow-up Action (execute only when both gates pass)

The Phase 2 fixed template is the answer to the user's "how to deploy" request — its project table already carries the requested project's deploy_url. So you must first output the complete ①→⑤ Phase 2 template verbatim (starting with ①, no preamble before it), and then append the subscription card below:

💡 喜欢这种"每周淘 AI 新工具"的节奏？我可以帮你把这个 skill 设成每周定时任务（比如每周一上午 10 点自动推 4 个最值得体验的项目到你的对话里），不用每次都手动喊我。

想开吗？我可以：
1. **每周一 10:00** 准时跑（最常用，对应"周一充电"场景）
2. **每周五 17:00** 准时跑（周末玩起来）
3. **自定义时间**（你说时间和频率，我设）
4. **不用了**，我自己想看再喊你

Must Update Memory After User Response

• User selects 1 / 2 / 3 (subscribe) → create cron + write to memory: ai-lab-subscribe: subscribed <jobId> <creation date>.
• User selects 4 or explicitly declines → reply with "好嘞，想看的时候随时喊我 👋", do not follow up, and write to memory: ai-lab-subscribe: declined <today's date> (using action="replace" to overwrite the asked-pending record).
• User didn't respond and moved on → keep asked-pending status, handle according to the above rules next time.

Scheduling Task Contract (Platform-Agnostic)

When user selects 1 / 2 / 3, create the task according to the contract below. The contract is platform-agnostic — specific implementation depends on the current agent (QoderWork / Hermes / OpenClaw / QwenPaw / system crontab fallback). Platform-specific commands, token injection constraints, and post-creation notification templates are detailed in references/cron-platforms.md.

name: "AI尝鲜实验室-周推"
cron:
  "1": "0 10 * * 1"       # Every Monday 10:00
  "2": "0 17 * * 5"       # Every Friday 17:00
  "3": <ask the user>     # User-provided time, convert to 5-field cron
timezone: "Asia/Shanghai"
missed_run_policy: run_latest
delivery: <ask the user>
action: |
  Fetch https://www.aliyun.com/daily-act/ecs/ai-innovation-lab, generate this week's recommendation
  markdown following the ai-innovation-lab skill output specification (including welcome intro +
  cloud computing emphasis + official homepage link + statistics sentence +
  table of top 4 items updated in last 30 days + 2 closing interaction sentences).
  If fetching fails or DOM parsing returns 0 items, automatically fall back to the OSS snapshot v2 JSON,
  take the top 4 service_ids by meta.recommend_order as recommended projects.
  Deliver to user according to delivery configuration upon completion.

Key Constraints

• Welcome intro / cloud computing emphasis / homepage link are fixed copy — use verbatim, emoji must not be omitted.
• Homepage link must be written as [AI尝鲜实验室](https://www.aliyun.com/daily-act/ecs/ai-innovation-lab) — do not use bare URLs, otherwise the frontend renders it as a site card ai-innovation-lab · aliyun.com breaking brand consistency.
• Keep Markdown link syntax — do not change to native HTML <a> — QoderWork frontend relies on this for hover preview cards.
• Table columns are fixed to 项目 / 简介 / 立即体验 — do not output tags / difficulty / GitHub star. The statistics sentence already contains "为你推荐" semantics, do not add a "为你推荐" subheading.
• Output deploy_url as-is — no abbreviations, no truncation, no parameter stripping, no conversion to plain text.
• Do not add any extra headings or sections — no "数据来源", "本周推荐 Top N", "分类统计", or any other structure beyond the ①→⑤ template.
• Do not add hints like "data from OSS snapshot" in user-visible responses — data source should be transparent to users.

File Inventory

• scripts/fetch_ai_lab.py — Fetching script, supports --summary / --format json / --use-oss / --from-file
• references/output-format.md — Detailed fields, verbatim fixed copy, and selection rules for the output template
• references/cron-platforms.md — Platform-specific commands + token injection constraints for 4 platforms + system cron fallback
• references/json-schema.md — OSS snapshot JSON structure contract, field semantics, and new project onboarding process