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
2026-04-16 04:06:25 +00:00
commit 2707fca32b
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+/**
+ * acr run <workflow_name> [--input key=value...]
+ * 觸發 cypher-executor 執行指定 workflow
+ */
+import chalk from 'chalk';
+import ora from 'ora';
+import { loadConfig, getCypherExecutorUrl } from '../lib/config.js';
+
+interface RunOptions {
+  input?: string[];
+}
+
+export async function cmdRun(workflowName: string, options: RunOptions): Promise<void> {
+  const config = loadConfig();
+  const executorUrl = getCypherExecutorUrl(config);
+
+  // 解析 --input key=value 為 JSON object
+  const inputContext: Record<string, string> = {};
+  for (const pair of (options.input ?? [])) {
+    const idx = pair.indexOf('=');
+    if (idx < 0) {
+      console.error(chalk.red(`--input 格式錯誤：${pair}（應為 key=value）`));
+      process.exit(1);
+    }
+    inputContext[pair.slice(0, idx)] = pair.slice(idx + 1);
+  }
+
+  const spinner = ora(`執行 workflow "${workflowName}"`).start();
+
+  const headers: Record<string, string> = { 'Content-Type': 'application/json' };
+  if (config.api_key) headers['X-Arcrun-API-Key'] = config.api_key;
+
+  const webhookUrl = `${executorUrl}/webhooks/${workflowName}`;
+
+  try {
+    const res = await fetch(webhookUrl, {
+      method: 'POST',
+      headers,
+      body: JSON.stringify(inputContext),
+    });
+
+    const data = await res.json() as {
+      success: boolean;
+      data?: unknown;
+      error?: string;
+      trace?: Array<{ node: string; status: string; error?: string }>;
+      duration_ms: number;
+      failed_node?: string;
+    };
+
+    if (data.success) {
+      spinner.succeed(chalk.green(`✓ 執行成功（${data.duration_ms}ms）`));
+      console.log('\n  結果：');
+      console.log(JSON.stringify(data.data, null, 2).split('\n').map(l => `  ${l}`).join('\n'));
+    } else {
+      spinner.fail(chalk.red(`✗ 執行失敗（${data.duration_ms}ms）`));
+      if (data.failed_node) {
+        console.log(chalk.red(`\n  失敗節點：${data.failed_node}`));
+      }
+      if (data.error) {
+        console.log(chalk.red(`  錯誤：${data.error}`));
+      }
+      if (data.trace) {
+        console.log('\n  執行追蹤：');
+        for (const step of data.trace) {
+          const icon = step.status === 'failed' ? chalk.red('✗') : chalk.green('✓');
+          console.log(`    ${icon} ${step.node}${step.error ? ` — ${step.error}` : ''}`);
+        }
+      }
+    }
+  } catch (e) {
+    spinner.fail(chalk.red(`網路錯誤：${e instanceof Error ? e.message : e}`));
+    process.exit(1);
+  }
+}