Runs a sequenced monolith-to-modular pipeline that sizes and inventories components, finds shared domain duplication, addresses flattening and hierarchy issues, analyzes coupling, then groups components into candidate domain-aligned units, with optional embedded DDD strategic analysis for bounded contexts. Use when asking how to split a monolith, size components before extraction, find duplicated domain logic, clean up module hierarchy, measure coupling between modules, or group components into services. Do NOT use for phased extraction roadmaps or prioritization without the prior analysis steps (use decomposition-planning-roadmap after this pipeline), end-to-end legacy migration strategy writeups (use legacy-migration-planner), pure infrastructure capacity sizing, or when you only need DDD without the structural pipeline (install domain-analysis standalone).
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AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionmodular-decompositionExecute the skills CLI command in your project's root directory to begin installation:
Fetches modular-decomposition from tech-leads-club/agent-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate modular-decomposition. Access via /modular-decomposition in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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| name | modular-decomposition |
| description | Runs a sequenced monolith-to-modular pipeline that sizes and inventories components, finds shared domain duplication, addresses flattening and hierarchy issues, analyzes coupling, then groups components into candidate domain-aligned units, with optional embedded DDD strategic analysis for bounded contexts. Use when asking how to split a monolith, size components before extraction, find duplicated domain logic, clean up module hierarchy, measure coupling between modules, or group components into services. Do NOT use for phased extraction roadmaps or prioritization without the prior analysis steps (use decomposition-planning-roadmap after this pipeline), end-to-end legacy migration strategy writeups (use legacy-migration-planner), pure infrastructure capacity sizing, or when you only need DDD without the structural pipeline (install domain-analysis standalone). |
This skill runs the Patterns 1–5 analysis pipeline before service extraction. Each pattern is plain markdown under references/; load the file for that step and execute it against the user’s codebase.
If the user only wants extraction order, phases, or migration roadmap after analysis exists, use decomposition-planning-roadmap instead. If they need a full legacy migration plan (strangler fig, research, multi-stack), use legacy-migration-planner as well or instead of this skill when that is the primary ask.
references/pattern-NN-*.md file and follow its instructions. Use the optional *-quick-reference.md for the same number when a short checklist is enough.references/domain-analysis.md before or alongside Pattern 5. Optionally open references/domain-analysis-quick-reference.md or references/domain-analysis-examples.md for condensed rules or illustrations.Example 1 — Full pipeline
User: "We're going to split this monolith—run the full decomposition analysis (Patterns 1–5)."
Agent: Execute patterns 1→5 in order, loading each references/pattern-NN-*.md, preserving outputs between steps, then summarize cross-cutting recommendations.
Example 2 — Coupling after inventory
User: "We already have a rough module list—focus on coupling (Pattern 4) and then domain grouping (Pattern 5)."
Agent: If no prior inventory exists in the thread, either run Pattern 1 briefly or derive an explicit module list from the repo before 4 and 5. State any assumptions.
Example 3 — DDD before grouping
User: "Map bounded contexts and language, then group components into domains."
Agent: Read references/domain-analysis.md (and optional quick reference/examples) in parallel with or immediately before Pattern 5; align Pattern 5 groupings with linguistic boundaries where evidence supports it.
references/domain-analysis.md before or alongside Pattern 5 (see Bounded contexts below).| Step | Pattern | Primary reference |
|---|---|---|
| 1 | Identify and size components | references/pattern-01-identify-and-size.md (optional: pattern-01-identify-and-size-quick-reference.md) |
| 2 | Common domain detection | references/pattern-02-common-domain.md (optional: pattern-02-common-domain-quick-reference.md) |
| 3 | Flattening / hierarchy | references/pattern-03-flattening.md (optional: pattern-03-flattening-quick-reference.md) |
| 4 | Coupling analysis | references/pattern-04-coupling.md |
| 5 | Domain identification and grouping | references/pattern-05-domain-grouping.md (optional: pattern-05-domain-grouping-quick-reference.md) |
Pattern 6 (create domain services / extraction) is not duplicated here. After Pattern 5, switch to decomposition-planning-roadmap for phased extraction order, milestones, and migration-style planning. For full legacy migration strategy (strangler-fig, cross-stack rewrites, research-heavy plans), optionally use legacy-migration-planner in addition.
references/domain-analysis.md, with optional domain-analysis-quick-reference.md and domain-analysis-examples.md. Use it when you need to validate or refine boundaries against business language, not only folder structure.Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
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✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
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Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
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Solid pick for teams standardizing on skills: modular-decomposition is focused, and the summary matches what you get after install.
We added modular-decomposition from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Solid pick for teams standardizing on skills: modular-decomposition is focused, and the summary matches what you get after install.
Registry listing for modular-decomposition matched our evaluation — installs cleanly and behaves as described in the markdown.
modular-decomposition fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
We added modular-decomposition from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
modular-decomposition reduced setup friction for our internal harness; good balance of opinion and flexibility.
modular-decomposition fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
modular-decomposition is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Useful defaults in modular-decomposition — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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