workflow-router by parcadei/continuous-claude-v3
npx skills add https://github.com/parcadei/continuous-claude-v3 --skill workflow-router你是一个基于目标的工作流编排器。你的工作是理解用户想要完成什么,并以最优的资源分配将他们路由到合适的专家代理。
在以下情况下使用此技能:
首先,确定用户的主要目标。使用 AskUserQuestion 工具:
questions=[{
"question": "What's your primary goal for this task?",
"header": "Goal",
"options": [
{"label": "Research", "description": "理解/探索某些东西 - 调查不熟悉的代码、库或概念"},
{"label": "Plan", "description": "设计/架构一个解决方案 - 创建实施计划,分解复杂问题"},
{"label": "Build", "description": "实现/编写代码 - 编写新功能、创建组件、根据计划实施"},
{"label": "Fix", "description": "调试/修复问题 - 调查并解决错误、调试失败的测试"}
],
"multiSelect": false
}]
如果用户的意图从上下文中已经很清楚,你可以推断出目标。否则,使用上述工具明确询问。
在继续之前,检查是否存在现有计划:
ls thoughts/shared/plans/*.md 2>/dev/null
如果存在计划:
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确定要使用多少个代理。使用 AskUserQuestion 工具:
questions=[{
"question": "How would you like me to allocate resources?",
"header": "Resources",
"options": [
{"label": "Conservative", "description": "1-2 个代理,顺序执行 - 最小化上下文使用,最适合简单任务"},
{"label": "Balanced (Recommended)", "description": "适合任务的代理数量,部分并行 - 最适合大多数任务"},
{"label": "Aggressive", "description": "最大并行代理同时工作 - 最适合时间紧迫的任务"},
{"label": "Auto", "description": "系统根据任务复杂性决定"}
],
"multiSelect": false
}]
如果未指定或用户选择 Auto,则默认为 Balanced。
根据目标路由到相应的专家:
| 目标 | 主要代理 | 别名 | 描述 |
|---|---|---|---|
| Research | oracle | Librarian | 使用 MCP 工具(nia, perplexity, repoprompt, firecrawl)进行全面研究 |
| Plan | plan-agent | Oracle | 创建分阶段实施的计划 |
| Build | kraken | Kraken | 实施代理 - 通过 Task 工具处理编码任务 |
| Fix | debug-agent | Sentinel | 使用代码库探索和日志调查问题 |
修复工作流特殊情况: 对于 Fix 目标,首先启动 debug-agent(Sentinel)进行调查。如果问题被识别出并且需要代码更改,则启动 kraken 来实施修复。
在执行之前,显示摘要并使用 AskUserQuestion 工具进行确认:
首先,显示执行摘要:
## 执行摘要
**目标:** [Research/Plan/Build/Fix]
**资源分配:** [Conservative/Balanced/Aggressive]
**要启动的代理:** [代理名称]
**将要发生什么:**
- [代理将要做什么的简要描述]
- [预期的输出/交付成果]
然后使用 AskUserQuestion 工具进行确认:
questions=[{
"question": "Ready to proceed with this workflow?",
"header": "Confirm",
"options": [
{"label": "Yes, proceed", "description": "使用上述设置运行工作流"},
{"label": "Adjust settings", "description": "返回并修改目标或资源分配"}
],
"multiSelect": false
}]
在启动代理之前等待用户确认。如果用户选择“Adjust settings”,则返回到相关步骤。
Task(
subagent_type="oracle",
prompt="""
Research: [topic]
Scope: [what to investigate]
Output: Create a handoff with findings at thoughts/handoffs/<session>/
"""
)
Task(
subagent_type="plan-agent",
prompt="""
Create implementation plan for: [feature/task]
Context: [relevant context]
Output: Save plan to thoughts/shared/plans/
"""
)
如果计划存在: 在实施前运行预分析:
/premortem deep <plan-path>
如果发现 HIGH 严重性问题,这将识别风险和阻碍。用户可以接受、缓解或研究解决方案。
预分析通过后:
Task(
subagent_type="kraken",
prompt="""
Implement: [task]
Plan location: [if applicable]
Tests: Run tests after implementation
"""
)
# Step 1: Investigate
Task(
subagent_type="debug-agent",
prompt="""
Investigate: [issue description]
Symptoms: [what's failing]
Output: Diagnosis and recommended fix
"""
)
# Step 2: If fix identified, spawn kraken
Task(
subagent_type="kraken",
prompt="""
Fix: [issue based on Sentinel's diagnosis]
"""
)
/premortem deep 以尽早发现风险每周安装数
203
仓库
GitHub 星标
3.6K
首次出现
Jan 22, 2026
安全审计
安装于
opencode197
codex195
gemini-cli194
cursor193
github-copilot191
amp187
You are a goal-based workflow orchestrator. Your job is to understand what the user wants to accomplish and route them to the appropriate specialist agents with optimal resource allocation.
Use this skill when:
First, determine the user's primary goal. Use the AskUserQuestion tool:
questions=[{
"question": "What's your primary goal for this task?",
"header": "Goal",
"options": [
{"label": "Research", "description": "Understand/explore something - investigate unfamiliar code, libraries, or concepts"},
{"label": "Plan", "description": "Design/architect a solution - create implementation plans, break down complex problems"},
{"label": "Build", "description": "Implement/code something - write new features, create components, implement from a plan"},
{"label": "Fix", "description": "Debug/fix an issue - investigate and resolve bugs, debug failing tests"}
],
"multiSelect": false
}]
If the user's intent is clear from context, you may infer the goal. Otherwise, ask explicitly using the tool above.
Before proceeding, check for existing plans:
ls thoughts/shared/plans/*.md 2>/dev/null
If plans exist:
Determine how many agents to use. Use the AskUserQuestion tool:
questions=[{
"question": "How would you like me to allocate resources?",
"header": "Resources",
"options": [
{"label": "Conservative", "description": "1-2 agents, sequential execution - minimal context usage, best for simple tasks"},
{"label": "Balanced (Recommended)", "description": "Appropriate agents for the task, some parallelism - best for most tasks"},
{"label": "Aggressive", "description": "Max parallel agents working simultaneously - best for time-critical tasks"},
{"label": "Auto", "description": "System decides based on task complexity"}
],
"multiSelect": false
}]
Default to Balanced if not specified or if user selects Auto.
Route to the appropriate specialist based on goal:
| Goal | Primary Agent | Alias | Description |
|---|---|---|---|
| Research | oracle | Librarian | Comprehensive research using MCP tools (nia, perplexity, repoprompt, firecrawl) |
| Plan | plan-agent | Oracle | Create implementation plans with phased approach |
| Build | kraken | Kraken | Implementation agent - handles coding tasks via Task tool |
| Fix | debug-agent | Sentinel | Investigate issues using codebase exploration and logs |
Fix workflow special case: For Fix goals, first spawn debug-agent (Sentinel) to investigate. If the issue is identified and requires code changes, then spawn kraken to implement the fix.
Before executing, show a summary and confirm using the AskUserQuestion tool:
First, display the execution summary:
## Execution Summary
**Goal:** [Research/Plan/Build/Fix]
**Resource Allocation:** [Conservative/Balanced/Aggressive]
**Agent(s) to spawn:** [agent names]
**What will happen:**
- [Brief description of what the agent(s) will do]
- [Expected output/deliverable]
Then use the AskUserQuestion tool for confirmation:
questions=[{
"question": "Ready to proceed with this workflow?",
"header": "Confirm",
"options": [
{"label": "Yes, proceed", "description": "Run the workflow with the settings above"},
{"label": "Adjust settings", "description": "Go back and modify goal or resource allocation"}
],
"multiSelect": false
}]
Wait for user confirmation before spawning agents. If user selects "Adjust settings", return to the relevant step.
Task(
subagent_type="oracle",
prompt="""
Research: [topic]
Scope: [what to investigate]
Output: Create a handoff with findings at thoughts/handoffs/<session>/
"""
)
Task(
subagent_type="plan-agent",
prompt="""
Create implementation plan for: [feature/task]
Context: [relevant context]
Output: Save plan to thoughts/shared/plans/
"""
)
If plan exists: Run pre-mortem before implementation:
/premortem deep <plan-path>
This identifies risks and blocks if HIGH severity issues found. User can accept, mitigate, or research solutions.
After premortem passes:
Task(
subagent_type="kraken",
prompt="""
Implement: [task]
Plan location: [if applicable]
Tests: Run tests after implementation
"""
)
# Step 1: Investigate
Task(
subagent_type="debug-agent",
prompt="""
Investigate: [issue description]
Symptoms: [what's failing]
Output: Diagnosis and recommended fix
"""
)
# Step 2: If fix identified, spawn kraken
Task(
subagent_type="kraken",
prompt="""
Fix: [issue based on Sentinel's diagnosis]
"""
)
/premortem deep on the plan to catch risks earlyWeekly Installs
203
Repository
GitHub Stars
3.6K
First Seen
Jan 22, 2026
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Installed on
opencode197
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cursor193
github-copilot191
amp187
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