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Decomposes complex user requests into executable subtasks, identifies required capabilities, searches for existing skills at skills.sh, and creates new skills when no solution exists. This skill should be used when the user submits a complex multi-step request, wants to automate workflows, or needs help breaking down large tasks into manageable pieces.

Decomposes complex user requests into executable subtasks, identifies required capabilities, searches for existing skills at skills.sh, and creates new skills when no solution exists. This skill should be used when the user submits a complex multi-step request, wants to automate workflows, or needs help breaking down large tasks into manageable pieces.

by 10e9928a · v1.0.0

Productivity
ClawHub
8.1
/ 10
1 evaluations
4.8k Downloads

Overview

Breaks complex user requests into atomic, executable subtasks, maps them to capability types, searches the skills.sh ecosystem for matching skills, and proposes new skills plus an execution plan when gaps exist.

Key Advantages

1.Provides a clear, repeatable multi-phase framework for decomposing complex workflows into atomic tasks with explicit inputs/outputs and dependencies.
2.Uses a capability taxonomy to systematically map subtasks to search queries against skills.sh, reducing ad-hoc guessing.
3.Performs structured gap analysis to distinguish between built-in LLM/code capabilities and cases where external skills are required.
4.Generates detailed execution plans including prerequisites, skills to install/create, and verification steps, aiding implementation and debugging.
5.Promotes reusability and single-responsibility design for newly created skills via a well-defined SKILL.md template.

Use Cases

  • Designing and planning complex, multi-step automations that span APIs, browsers, messaging platforms, and schedulers.
  • Breaking down high-level user goals (e.g., automated reporting, monitoring, data pipelines) into concrete, implementable subtasks.
  • Surveying the skills.sh ecosystem to locate suitable existing skills before building custom ones.
  • Performing gap analysis on an existing workflow to see which steps can be handled natively vs. require new skills.
  • Scaffolding new general-purpose skills (with SKILL.md and structure) when no adequate skill exists for a required capability.

Evaluation Scores

8.1
/ 10
Reliability
7.0
Functionality
8.5
Usability
8.5
Safety
8.0
Performance
7.5
Compatibility
9.0

Based on 1 evaluation · Latest: 3/19/2026

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Evaluation History (1)

8.1/103/19/2026
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OS: win32-x64LLM: openai/gpt-5-nano
**Judgement:** A strong, general-purpose task-decomposition and skill-orchestration skill that is well-aligned with the skills.sh ecosystem and well-suited for complex workflow design and automation planning. **What it does well:** - Systematically decomposes complex requests into atomic, verifiable subtasks with dependencies. - Maps tasks to a clear capability taxonomy, then formulates concrete `npx skills find` queries. - Distinguishes built-in capabilities (LLM, simple code, scheduling) from steps that truly require external skills. - Produces a structured “Task Decomposition Report” and an actionable execution plan, including skills to install and skills to create. - Provides a solid template and process for creating new, reusable skills when gaps are identified. **Main risks / limitations:** - Quality of decomposition and capability mapping is LLM-driven, so subtasks may be over/under-granular or misclassified, impacting reliability. - Skill search may miss relevant skills due to imperfect keyword selection or ambiguous task descriptions. - Encourages installation of third-party skills (`npx skills add`) which may introduce security or quality risks if not reviewed by the user. - Non-expert users may find the multi-phase process and CLI-oriented workflow somewhat complex. **Recommended scenarios:** - Users with complex, multi-step goals who want help turning them into concrete, automatable workflows. - Developers or power users planning new automations and wanting to leverage existing skills before building custom ones. - Situations where clear execution plans (with prerequisites, verification steps, and skill mapping) are needed before implementation. - Early design of new skills that should be reusable and well-documented within the skills.sh ecosystem.

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