.claude-plugin/plugin.json

@@ -1,7 +1,7 @@
 {
   "name": "jsc-cli",
-  "version": "0.0.8",
+  "version": "0.1.0",
-  "description": "CLI 偵測、模型能力標籤與技能庫批次部署",
+  "description": "CLI 偵測、模型能力標籤、子代理派工與技能庫批次部署",
   "skills": "./skills",
   "author": {
     "name": "JSC"

.codex-plugin/plugin.json

@@ -1,6 +1,6 @@
 {
   "name": "jsc-cli",
-  "version": "0.0.8",
+  "version": "0.1.0",
-  "description": "CLI 偵測、模型能力標籤與技能庫批次部署",
+  "description": "CLI 偵測、模型能力標籤、子代理派工與技能庫批次部署",
   "skills": "./skills"
 }

README.md

@@ -38,6 +38,10 @@ Marketplace 統一為 `jsc`（https://gitea.jsc.idv.tw/plugins/meta.git），安
 
 用 `tools/list-models.sh` 讀出各已安裝 CLI 可使用的模型並加上能力標籤（`references/model-tags.md`），用 `tools/model-tags.sh sync` 把標籤表寫進 `$JSC_HOME/model-tags.tsv` 供 sdlc-gate 讀取，列出 SDLC 各階段的必要標籤（plan、analyze 需 `reasoning-max`；implement 需 `coding`；maintain 任意），並用 `tools/model-config.sh` 列出各階段的偏好模型鏈。`jsc-sdlc` 閘門一律以能力標籤判定，偏好鏈只用來建議切換目標。
 
+### `delegate`
+
+把單一明確任務交給另一個已安裝的 AI agent CLI 當作 subagent 執行。可指定目標 CLI，也可依任務需求用能力標籤篩選模型，或強制指定模型。每個目標分開派工，預設只讀，並回傳結構化結果供主 agent 驗證與彙整。此技能不負責模型盤點或 plugin 部署。
+
 ### `deploy`
 
 技能庫批次安裝、更新、解除安裝：偵測 CLI → **先比對各 plugin 的本機與已發佈版本並列表，只要有任一個落後就把「更新」設為推薦選項** → 決策樹選模式 → 每個 CLI 一個 sub agent 呼叫 `tools/deploy.sh` 執行原生 plugin 指令（統一 marketplace `jsc`，token `jsc-{domain}@jsc`）。domain 名單動態取自 `plugins/meta` 的 marketplace.json，不硬編碼。

plugin.json

@@ -1,6 +1,6 @@
 {
   "name": "jsc-cli",
-  "version": "0.0.8",
+  "version": "0.1.0",
-  "description": "CLI 偵測、模型能力標籤與技能庫批次部署",
+  "description": "CLI 偵測、模型能力標籤、子代理派工與技能庫批次部署",
   "skills": "./skills/"
 }

skills/delegate/SKILL.md

@@ -0,0 +1,37 @@
+---
+name: delegate
+description: Delegate a single bounded task to another installed AI agent CLI as a subagent, with an explicit target CLI, required capability tags, or a forced model. Use when another CLI should do the work and return structured results; not for model inventory, plugin deployment, or tasks that must stay in the current agent.
+---
+
+# delegate — hand a task to another CLI
+
+Use this skill when the work should move to another installed AI agent CLI instead of staying in the current agent.
+
+## Steps
+
+1. Split the request into one bounded goal. One goal means one subagent.
+2. Detect the installed AI agent CLIs. Use only a target CLI that is actually present.
+3. Choose the execution model.
+   - If the user names a CLI, keep only that CLI.
+   - If the user gives capability tags, keep only models that satisfy all tags.
+   - If the user forces a model, use that exact model and fail if it does not satisfy the tags.
+4. Build the subagent prompt.
+   - Include the goal, the acceptance criteria, the target CLI, the model choice, and the minimum needed context.
+   - Include `/jsc-shared:spec-output`.
+   - State the write scope clearly. If no write scope is granted, say the subagent is read-only.
+5. Spawn one subagent for the goal. Do not mix unrelated goals in the same subagent.
+6. Verify the result before you report it.
+   - Check the exit status.
+   - Check that the return value is structured.
+   - Check that the result covers the acceptance criteria.
+   - Check that any writes stayed inside the allowed scope.
+7. Report the verified result.
+   - State the CLI, the model, the capability tags, and the outcome.
+   - Separate success, failure, and anything that still needs user input.
+
+## Do not use this skill
+
+- Do not use it for model inventory.
+- Do not use it for plugin deployment.
+- Do not use it for tasks that must stay inside the current agent.
+- Do not use it when the task is not bounded enough to hand off cleanly.