feat(arcrun): implement arcrun MVP — open-source AI workflow engine

Phase 1-5 complete per .agents/specs/u6u-core-mvp/:

**Phase 1 — Cherry-pick & cleanup**
- Create arcrun/ from cypher-executor, credentials, builtins, registry
- Remove 9 InkStone Service Bindings (KBDB, REGISTRY, CLINIC_*, AICEO, MINI_ME)
- Rewrite component-loader: 3-layer (builtin → WASM_BUCKET R2 → error)
- Remove autoPublishMissing.ts, proxy.ts (AICEO), execution-logger.ts (KBDB)
- Clean all KV namespace IDs and InkStone internal URLs from config files

**Phase 2 — contract.yaml completeness**
- Add credentials_required to gmail, google_sheets, telegram, line_notify
- Add config_example to all 21 components with annotated field descriptions

**Phase 3 — Credential injection**
- Add credential-injector.ts: AES-GCM decrypt from CREDENTIALS_KV
- Integrate into GraphExecutor before WASM execution
- Structured errors with repair instructions when credential missing

**Phase 4 — CLI (acr)**
- cli/package.json: arcrun package, bin: acr, deps: commander/js-yaml/chalk/ora
- 8 commands: init, creds push, push, run, validate, parts, list, logs
- Standard mode: writes directly to user's CF KV via CF REST API
- acr init: interactive setup with arcrun.dev API Key registration

**Phase 5 — Open source release prep**
- README.md: 5-minute quickstart, component table, workflow YAML syntax
- CONTRIBUTING.md: TinyGo dev env, component scaffolding, submission flow
- Security audit: no InkStone internal URLs/IDs in committed files
- .gitignore: exclude credentials.yaml, .wrangler, *.wasm

https://claude.ai/code/session_01BnCdSLVH8tUed9VrrPavgT

registry/components/wait/component.contract.yaml

@@ -0,0 +1,54 @@
+canonical_id: "wait"
+display_name: "等待延遲"
+category: "logic"
+version: "v1"
+wasi_target: "preview1"
+stability: "floating"
+runtime_compat:
+  - "cf-workers"
+  - "workerd"
+  - "wazero"
+constraints:
+  max_size_kb: 2048
+  max_cold_start_ms: 50
+  no_network_syscall: true
+  no_filesystem_syscall: true
+  io_model: "stdin_stdout_json"
+input_schema:
+  type: object
+  required: [ms]
+  properties:
+    ms:
+      type: integer
+      description: 等待毫秒數，最大 30000（30 秒）
+    context:
+      type: object
+      description: 透傳到下一個節點的上下文資料
+output_schema:
+  type: object
+  properties:
+    success:
+      type: boolean
+    data:
+      type: object
+      description: 透傳的 context 加上 waited_ms 欄位
+      properties:
+        waited_ms:
+          type: integer
+gherkin_tests:
+  - scenario: "等待 100ms"
+    given: '{"ms":100}'
+    then_contains: '"waited_ms":100'
+  - scenario: "超過上限自動截斷為 30000ms"
+    given: '{"ms":99999}'
+    then_contains: '"waited_ms":30000'
+  - scenario: "ms 為 0 時失敗"
+    given: '{"ms":0}'
+    then_contains: '{"success":false'
+tags: [builtin, wait, delay, sleep, timing]
+description: "等待指定毫秒數後繼續，最長 30 秒，並透傳 context 資料。"
+config_example: |
+  my_wait:                           # 節點名稱（可自訂）
+    ms: 1000                         # 等待毫秒數，最大 30000（必填）
+    context:                         # 透傳到下一個節點的資料（選填）
+      payload: "{{upstream.data}}"