skills/ultrawork/SKILL.md
Parallel execution engine for high-throughput task completion
npx skillsauth add OliverOuyang/shuhe-work-skills ultraworkInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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<Use_When>
<Do_Not_Use_When>
ralph instead (ralph includes ultrawork)autopilot instead (autopilot includes ralph which includes ultrawork)ralph which adds persistence on top of ultrawork
</Do_Not_Use_When><Why_This_Exists> Sequential task execution wastes time when tasks are independent. Ultrawork enables firing multiple agents simultaneously and routing each to the right model tier, reducing total execution time while controlling token costs. It is designed as a composable component that ralph and autopilot layer on top of. </Why_This_Exists>
<Execution_Policy>
model parameter explicitly when delegatingdocs/shared/agent-tiers.md before first delegation for agent selection guidancerun_in_background: true for operations over ~30 seconds (installs, builds, tests)<Tool_Usage>
Task(subagent_type="oh-my-claudecode:executor", model="haiku", ...) for simple changesTask(subagent_type="oh-my-claudecode:executor", model="sonnet", ...) for standard workTask(subagent_type="oh-my-claudecode:executor", model="opus", ...) for complex workrun_in_background: true for package installs, builds, and test suites<Escalation_And_Stop_Conditions>
ralph mode<Final_Checklist>
ralph (persistence wrapper)
\-- includes: ultrawork (this skill)
\-- provides: parallel execution only
autopilot (autonomous execution)
\-- includes: ralph
\-- includes: ultrawork (this skill)
Ultrawork is the parallelism layer. Ralph adds persistence and verification. Autopilot adds the full lifecycle pipeline. </Advanced>
tools
SQL 分段验证、自我修复、结果导出与智能分析。流程:解析SQL → Dataphin MCP 验证元数据 → 自动修复 → 分段执行验证 → 导出 CSV → 智能分析(漏斗解读、异常识别、预判用户问题)。适用场景:"跑一下这个SQL"、"验证这个查询"、"帮我执行并导出"、"分析一下结果"等。
testing
Security-first vetting for OpenClaw skills. Use before installing any skill from ClawHub, GitHub, or other sources. Checks for red flags, permission scope, and suspicious patterns.
development
A universal self-improving agent that learns from ALL skill experiences. Uses multi-memory architecture (semantic + episodic + working) to continuously evolve the codebase. Auto-triggers on skill completion/error with hooks-based self-correction.
data-ai
Standardize Jupyter notebooks (.ipynb) for interactive data analysis workflows. Enforces a mandatory cell manifest (M1-M8 + archetype chapters) with tags ([CONFIG]/[SETUP]/[FUNC]/[RUN]/[VIZ]/[EXPORT]), structured markdown sections, and output prefixes ([OK]/[WARN]/[SKIP]). Use when the user wants to standardize, clean up, or create a notebook from scratch. Two archetypes: problem-driven (question-answer analysis) and monitoring (dimension-based periodic reporting).