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
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canonical_id: "wait"
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display_name: "等待延遲"
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category: "logic"
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version: "v1"
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wasi_target: "preview1"
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stability: "floating"
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runtime_compat:
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- "cf-workers"
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- "workerd"
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- "wazero"
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constraints:
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max_size_kb: 2048
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max_cold_start_ms: 50
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no_network_syscall: true
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no_filesystem_syscall: true
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io_model: "stdin_stdout_json"
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input_schema:
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type: object
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required: [ms]
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properties:
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ms:
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type: integer
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description: 等待毫秒數,最大 30000(30 秒)
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context:
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type: object
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description: 透傳到下一個節點的上下文資料
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output_schema:
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type: object
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properties:
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success:
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type: boolean
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data:
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type: object
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description: 透傳的 context 加上 waited_ms 欄位
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properties:
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waited_ms:
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type: integer
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gherkin_tests:
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- scenario: "等待 100ms"
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given: '{"ms":100}'
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then_contains: '"waited_ms":100'
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- scenario: "超過上限自動截斷為 30000ms"
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given: '{"ms":99999}'
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then_contains: '"waited_ms":30000'
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- scenario: "ms 為 0 時失敗"
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given: '{"ms":0}'
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then_contains: '{"success":false'
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tags: [builtin, wait, delay, sleep, timing]
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description: "等待指定毫秒數後繼續,最長 30 秒,並透傳 context 資料。"
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config_example: |
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my_wait: # 節點名稱(可自訂)
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ms: 1000 # 等待毫秒數,最大 30000(必填)
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context: # 透傳到下一個節點的資料(選填)
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payload: "{{upstream.data}}"
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@@ -0,0 +1,3 @@
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module component
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go 1.21
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@@ -0,0 +1,52 @@
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// wait — 等待指定毫秒數後繼續(最多 30 秒)
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// 注意:TinyGo/WASM 環境中 time.Sleep 可能不可用,改用 busy-wait 模擬
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package main
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import (
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"encoding/json"
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"io"
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"os"
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"time"
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)
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type Input struct {
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Ms int `json:"ms"`
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Context map[string]interface{} `json:"context"`
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}
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func main() {
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raw, err := io.ReadAll(os.Stdin)
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if err != nil {
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writeError("failed to read stdin: " + err.Error())
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return
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}
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var input Input
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if err := json.Unmarshal(raw, &input); err != nil {
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writeError("invalid input JSON: " + err.Error())
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return
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}
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if input.Ms <= 0 {
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writeError("ms 必須大於 0")
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return
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}
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ms := input.Ms
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if ms > 30000 {
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ms = 30000
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}
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time.Sleep(time.Duration(ms) * time.Millisecond)
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result := make(map[string]interface{})
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for k, v := range input.Context {
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result[k] = v
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}
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result["waited_ms"] = ms
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out, _ := json.Marshal(map[string]interface{}{"success": true, "data": result})
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os.Stdout.Write(out)
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}
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func writeError(msg string) {
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out, _ := json.Marshal(map[string]interface{}{"success": false, "error": msg})
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os.Stdout.Write(out)
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}
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