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
This commit is contained in:
Claude
2026-04-16 04:06:25 +00:00
commit 2707fca32b
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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}}"
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module component
go 1.21
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// wait — 等待指定毫秒數後繼續(最多 30 秒)
// 注意:TinyGo/WASM 環境中 time.Sleep 可能不可用,改用 busy-wait 模擬
package main
import (
"encoding/json"
"io"
"os"
"time"
)
type Input struct {
Ms int `json:"ms"`
Context map[string]interface{} `json:"context"`
}
func main() {
raw, err := io.ReadAll(os.Stdin)
if err != nil {
writeError("failed to read stdin: " + err.Error())
return
}
var input Input
if err := json.Unmarshal(raw, &input); err != nil {
writeError("invalid input JSON: " + err.Error())
return
}
if input.Ms <= 0 {
writeError("ms 必須大於 0")
return
}
ms := input.Ms
if ms > 30000 {
ms = 30000
}
time.Sleep(time.Duration(ms) * time.Millisecond)
result := make(map[string]interface{})
for k, v := range input.Context {
result[k] = v
}
result["waited_ms"] = ms
out, _ := json.Marshal(map[string]interface{}{"success": true, "data": result})
os.Stdout.Write(out)
}
func writeError(msg string) {
out, _ := json.Marshal(map[string]interface{}{"success": false, "error": msg})
os.Stdout.Write(out)
}