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. (1)
by 10e9928a · v1.0.0
Productivity
ClawHub
8.4
/ 10
1 evaluations
2.1k Downloads
Overview
Automatically decomposes complex user requests into atomic subtasks, maps each to required capabilities, searches the skills.sh ecosystem for matching skills, and proposes or scaffolds new skills plus an execution plan when gaps exist.
Key Advantages
1.Provides a systematic, repeatable framework for breaking down large, multi-step user requests into minimal executable tasks.
2.Bridges user intent and the skills.sh ecosystem by turning capabilities into concrete skill search queries and installation recommendations.
3.Reduces duplication of effort by prioritizing discovery of existing skills before proposing new skill creation.
4.Encourages high‑quality, reusable skill design via a clear SKILL.md template and best‑practice guidelines for new skills.
5.Produces human‑readable task decomposition reports and execution plans that are easy to review, edit, and execute.
Use Cases
- Designing end‑to‑end automation workflows (e.g., email → summarize → Slack/Discord notifications → scheduling).
- Planning multi‑stage data pipelines that mix web search, downloading, extraction, summarization, and storage in third‑party tools (e.g., Notion).
- Assisting developers or ops teams in mapping high‑level requirements into concrete skills.sh skills plus system cron/scheduler usage.
- Evaluating whether an existing skills.sh ecosystem can support a proposed workflow or if new skills must be created.
- Onboarding new users to the skills.sh environment by showing how to translate natural language goals into skills and execution steps.
Evaluation Scores
8.4
/ 10
Reliability
7.8
Functionality
9.0
Usability
9.2
Safety
8.0
Performance
7.5
Compatibility
8.3
Based on 1 evaluation · Latest: 3/19/2026
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Evaluation History (1)
8.4/103/19/2026▼
OS: darwin-x64LLM: deepseek/deepseek-v3.2
**Judgement:** This is a strong meta-orchestration/planning skill for the skills.sh ecosystem. It is well-structured, with a clear multi-phase workflow (decomposition → capability mapping → skill search → gap analysis → skill creation → execution plan) and good documentation.
**Key strengths**
- Very good at turning vague, multi-step user requests into executable task graphs with explicit dependencies.
- Integrates directly with skills.sh via `npx skills find` / `npx skills add`, encouraging reuse before new development.
- Provides robust templates (SKILL.md, execution plans, decomposition reports) that improve clarity and collaboration.
**Main risks / limitations**
- Depends heavily on the quality and availability of the skills.sh ecosystem and the Skills CLI; if search results are poor or the CLI is unavailable, recommendations may degrade.
- Task and capability mapping quality is only as good as the underlying language understanding; ambiguous or very domain-specific requests may be decomposed suboptimally.
- Automatically proposing new skills can lead to fragmentation or over-creation if not curated by a human.
- Any downstream skills it recommends (e.g., browser automation, API integrations) may have their own security and reliability risks; this skill itself does not enforce those.
**Recommended scenarios**
- Users asking for complex, multi-step automations ("monitor X, summarize, then send to Y on a schedule").
- Early design of workflows where the user wants both a plan and guidance on which existing skills to install.
- Teams standardizing how they break down requests and design reusable skills in the skills.sh ecosystem.
- Situations where human review is available for the generated execution plan and any newly proposed skills.
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