From arete
Splits complex problems into focused sub-sessions by identifying dimensions, dependencies, and order. Use for multi-faceted issues like auth redesign, DB migration, and API changes.
How this skill is triggered — by the user, by Claude, or both
Slash command
/arete:decomposeThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Break a multi-faceted problem into focused brainstorm sessions that can each go through the full GROUND → SHIP flow independently.
Break a multi-faceted problem into focused brainstorm sessions that can each go through the full GROUND → SHIP flow independently.
List the distinct sub-problems. Each should be independently brainstormable:
Sub-sessions identified:
1. [Problem A] — [core tension in 1 sentence]
2. [Problem B] — [core tension in 1 sentence]
3. [Problem C] — [core tension in 1 sentence]
Which sub-problems depend on decisions from others?
Dependencies:
- Problem B depends on Problem A (schema choice affects migration)
- Problem C is independent
Recommended order:
1. Problem A (others depend on it)
2. Problem C (independent — can run in parallel)
3. Problem B (blocked by A)
Ask the user which sub-problem to tackle first. Continue the current session on that one only. Other sub-problems are parked — not forgotten, just deferred.
In later sessions, reference prior session outputs from context/exports/ for decisions that carry forward. This is how sub-sessions compose into a coherent whole.
Concise. Present the decomposition as a structured list, not a wall of text. Let the user react and choose.
Don't decompose problems that are genuinely coupled. If changing one dimension necessarily changes the others, it's one problem — not three. Ask: "Can I decide A without knowing B?" If not, they stay together.
npx claudepluginhub jesgarram/arete --plugin areteExplores problem space before implementation: defines goals, success criteria, boundaries, and compares 2-3 approaches. Used at Phase 0 of deep-work sessions.
Structured brainstorming using the Double Diamond model with Sibyl memory integration. Guides divergent exploration and convergent decision-making for features, architecture, and design.
Turns fuzzy ideas into branching exploration trees via iterative divergence, deepening, and pruning; distills trees into specs with guided questions. Useful for ambiguous feature or workflow planning.