Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Owl-Listener/ai-design-skills --skill task-decompositiongit clone --depth 1 https://github.com/Owl-Listener/ai-design-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/owl-listener/ai-design-skills/task-decomposition)<a href="https://agentmods.dev/skills/owl-listener/ai-design-skills/task-decomposition"><img src="https://agentmods.dev/badge/skills/owl-listener/ai-design-skills/task-decomposition/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/owl-listener/ai-design-skills/task-decomposition"><img src="https://agentmods.dev/badge/skills/owl-listener/ai-design-skills/task-decomposition.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00016 | $0.00491 |
| Opus 5 | $0.00008 | $0.00246 |
| Sonnet 5 | $0.00003 | $0.00098 |
| Haiku 4.5 | $0.00002 | $0.00049 |
Grade A, and why
task-decomposition scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 37 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Task Decomposition
Users come with goals, not subtasks. Task decomposition is how a multi-agent system breaks a complex user goal into pieces that individual agents can handle — and then reassembles the results into something coherent.
Decomposition Strategies
- Sequential decomposition: Break the goal into ordered steps. Step 1 must complete before Step 2 starts.
- Parallel decomposition: Break the goal into independent parts that can be worked on simultaneously.
- Hierarchical decomposition: Break the goal into sub-goals, then break each sub-goal into tasks.
- Conditional decomposition: The next step depends on the result of the current step. Different results lead to different paths.
- Iterative decomposition: Start with a rough version, then refine through multiple passes.
Designing Decomposition Rules
For each type of user goal the system handles:
- What's the entry point? How does the system receive the goal?
- What are the subtasks? List all possible subtasks for this goal type.
- What are the dependencies? Which subtasks depend on others' outputs?
- What's the critical path? Which sequence of subtasks determines the minimum completion time?
- What can be parallelised? Which subtasks can run simultaneously?
- What's the reassembly logic? How do subtask results combine into the final output?
Granularity
How finely to decompose matters:
- Too coarse: Single agents get tasks that are too complex, leading to lower quality
- Too fine: Overhead from handoffs exceeds the benefit of specialisation
- Just right: Each subtask matches one agent's sweet spot in terms of scope and complexity
Handling Ambiguity
User goals are often ambiguous. The system needs to:
- Clarify before decomposing: Ask the user to specify when the goal is unclear
- Decompose tentatively: Start with a plan and adjust as information emerges
- Recompose when needed: If decomposition was wrong, restructure without starting over
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 37 lines · 16 tokens per session scan A dc5a67253136
task-decomposition is a skill published in the GitHub repository Owl-Listener/ai-design-skills (172 stars, last pushed 3mo ago), licensed MIT. It adds 16 tokens to every session and 491 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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