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Yeachan-Heo/ultrawork

Yeachan-Heo

ultrawork

Parallel execution engine for high-throughput task completion

global
New~1.4k
v1.1Saved May 11, 2026

<Use_When>

  • Multiple independent tasks can run simultaneously
  • User says "ulw", "ultrawork", or wants parallel execution
  • You need to delegate work to multiple agents at once
  • Task benefits from concurrent execution but the user will manage completion themselves </Use_When>

<Do_Not_Use_When>

  • Task requires guaranteed completion with verification -- use ralph instead (ralph includes ultrawork)
  • Task requires a full autonomous pipeline -- use autopilot instead (autopilot includes ralph which includes ultrawork)
  • There is only one sequential task with no parallelism opportunity -- delegate directly to an executor agent
  • User needs session persistence for resume -- use 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>

  • Fire all independent agent calls simultaneously -- never serialize independent work
  • Always pass the model parameter explicitly when delegating
  • Read docs/shared/agent-tiers.md before first delegation for agent selection guidance
  • Use run_in_background: true for operations over ~30 seconds (installs, builds, tests)
  • Run quick commands (git status, file reads, simple checks) in the foreground </Execution_Policy>

<Tool_Usage>

  • Use Task(subagent_type="oh-my-claudecode:executor", model="haiku", ...) for simple changes
  • Use Task(subagent_type="oh-my-claudecode:executor", model="sonnet", ...) for standard work
  • Use Task(subagent_type="oh-my-claudecode:executor", model="opus", ...) for complex work
  • Use run_in_background: true for package installs, builds, and test suites
  • Use foreground execution for quick status checks and file operations </Tool_Usage>

<Escalation_And_Stop_Conditions>

  • When ultrawork is invoked directly (not via ralph), apply lightweight verification only -- build passes, tests pass, no new errors
  • For full persistence and comprehensive architect verification, recommend switching to ralph mode
  • If a task fails repeatedly across retries, report the issue rather than retrying indefinitely
  • Escalate to the user when tasks have unclear dependencies or conflicting requirements </Escalation_And_Stop_Conditions>

<Final_Checklist>

  • All parallel tasks completed
  • Build/typecheck passes
  • Affected tests pass
  • No new errors introduced </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.

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Overall Score

82/100

Grade

B

Good

Safety

85

Quality

83

Clarity

88

Completeness

75

Summary

Ultrawork is a parallel execution engine that enables simultaneous multi-agent task dispatch for independent work items. It routes tasks to appropriate model tiers (Haiku/Sonnet/Opus), manages foreground vs background execution, and applies lightweight post-completion verification. The skill is designed as a composable component that ralph and autopilot layer on top of, providing parallelism without persistence or comprehensive state management.

Detected Capabilities

multi-agent dispatchconcurrent task executionmodel selection and routingbackground operation managementtask dependency analysislightweight verification (build/test checks)

Trigger Keywords

Phrases that MCP clients use to match this skill to user intent.

parallel executionultraworkconcurrent agentsmultiple tasks simultaneouslyulwsmart model routingindependent delegation

Use Cases

  • Dispatch multiple independent code changes simultaneously (type exports, endpoint implementations, test suites) to reduce total execution time
  • Route simple lookups to cost-effective Haiku models while routing complex refactoring to Opus, optimizing token spend across heterogeneous task complexity
  • Run long-running operations (npm installs, builds, test suites) in the background while short tasks complete in the foreground
  • Identify task dependencies and serialize only dependent work while parallelizing independent tasks to maximize concurrent throughput
  • Decide when to escalate from ultrawork to ralph (for persistence) or autopilot (for full autonomous pipeline) based on completion guarantees and verification requirements

Quality Notes

  • Excellent structural clarity: purpose, use cases, escalation policies, and checklist are all explicit and well-separated
  • Strong decision boundaries documented: clear guidance on when NOT to use ultrawork (single task, needs persistence, needs full pipeline) and what to use instead (ralph, autopilot)
  • Good pedagogical examples: contrasts good vs bad patterns with explicit explanations ('Why good', 'Why bad')
  • Tier selection guidance is actionable with specific model recommendations (Haiku for simple, Sonnet for standard, Opus for complex)
  • Relationship to other modes (ralph, autopilot) is documented in Advanced section, helping users understand composition
  • Practical execution policy includes specific thresholds (~30 seconds for background execution)
  • Lightweight verification approach is appropriate for the scope — does not overreach into ralph or autopilot's domain
  • No supporting files like 'docs/shared/agent-tiers.md' are included, but the skill explicitly documents this dependency and its purpose
Model: claude-haiku-4-5-20251001Analyzed: May 11, 2026

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Version History

v1.1

Content updated

2026-04-20

Latest
v1.0

No changelog

2026-04-12

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