docs(registry): seed 10 examples + 5 skills (LI SDD M3.1 + M3.3)
對應 .agents/specs/llm-interface/ Milestone 3.1 + 3.3。
registry/examples/ — 10 個可直接 push 的 workflow 範本:
starter: webhook-to-http
common: cron-watcher, llm-classify, rag-search-answer, daily-digest
external: email-summary (gmail+claude+telegram), pdf-to-blocks,
github-issue-bot
advanced: parallel-fanout (trigger_workflow fan-out),
error-retry (try_catch+wait pattern)
每個含:workflow.yaml(可直接 push)+ description.md(解決什麼問題 /
改成你自己的 / 學到什麼)+ tags.json(搜尋用)
registry/skills/ — 5 個 AI playbook(markdown):
build_watcher_workflow — cron + filter + trigger 模式
debug_paused_workflow — claude_api callback paused 怎麼追
migrate_http_to_trigger_workflow — 從 self-fetch 換 trigger_workflow
rag_with_arcrun — KBDB + claude_api 組裝 RAG
add_new_wasm_component — TinyGo 寫 + 部署全流程
兩者差異:
examples = 可直接拿來改的 YAML
skills = 面對 X 問題該怎麼想 + 該用哪個 example
兩者後續:CI 自動同步進 KBDB(type=workflow-example / type=agent-skill),
MCP arcrun_search_examples / arcrun_list_skills 走 KBDB semantic search。
(CI sync 是 M3.4 工作)
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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name: cron_watcher_example
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description: 每 5 分鐘掃 KBDB 未處理的 note → 對每筆觸發子 workflow
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flow:
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- "watch_cron >> ON_SUCCESS >> list_unprocessed"
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- "list_unprocessed >> ON_SUCCESS >> filter_new"
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- "filter_new >> 對每個 item >> trigger_processor"
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config:
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watch_cron:
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component: cron
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cron_expr: "*/5 * * * *"
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description: "每 5 分鐘掃一次"
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list_unprocessed:
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component: kbdb_get
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api_key: "{{api_key}}"
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type: "note"
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source: "user-input"
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limit: 20
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filter_new:
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component: filter
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items: "{{list_unprocessed.blocks}}"
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condition:
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key: "tags_json"
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op: "eq"
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value: "[]"
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# trigger_workflow 是內建 orchestration 零件,in-process call 另一個 workflow
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# **千萬不要用 http_request 自打 cypher-executor 自己的 webhook** — 會撞 CF self-fetch 死鎖
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trigger_processor:
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component: trigger_workflow
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workflow_name: "your_processor_workflow" # ← 改成你的處理 workflow 名
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api_key: "{{api_key}}"
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input:
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api_key: "{{api_key}}"
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block_id: "{{item.id}}"
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