Transform overwhelming development tasks into manageable units by respecting cognitive limits, creating clear boundaries, and enabling parallel work. Tasks properly decomposed achieve 3x higher completion rates and 60% fewer defects.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiontask-decompositionExecute the skills CLI command in your project's root directory to begin installation:
Fetches task-decomposition from jwynia/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 task-decomposition. Access via /task-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.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Transform overwhelming development tasks into manageable units by respecting cognitive limits, creating clear boundaries, and enabling parallel work. Tasks properly decomposed achieve 3x higher completion rates and 60% fewer defects.
Use this skill when:
Do NOT use this skill when:
The goal isn't more tasks—it's the right tasks. Tasks small enough to understand completely, large enough to deliver value, independent enough to avoid blocking.
| Limit | Threshold | Implication |
|---|---|---|
| Working memory | 7±2 items | Max concepts per task |
| Context switch recovery | 23 minutes | Minimize task switching |
| Files examined | 15-20 max | Bound task scope |
| Days before completion drops | 2-3 days | Keep tasks under this |
| Duration | Completion Rate |
|---|---|
| < 2 hours | 95% |
| 2-4 hours | 90% |
| 4-8 hours (1 day) | 80% |
| 2-3 days | 60% |
| 1 week | 35% |
| > 2 weeks | <10% |
Symptoms: Estimates range wildly, can't hold all requirements in mind, more than 7 concepts to track
Interventions:
Symptoms: Multiple valid starting points, paralysis, everything seems connected
Interventions:
Symptoms: "Blocked on X", diamond dependencies, coordination overhead
Interventions:
Symptoms: "Almost done" forever, no way to verify completion
Interventions:
Symptoms: Task keeps growing, "while we're here" additions
Interventions:
Symptoms: Estimate variance > 4x, new technology, multiple approaches
Interventions:
Feature: User Profile Management
Slice 1: View basic profile (4h)
- UI: Profile display
- API: GET /profile
- DB: Read profile
Slice 2: Edit profile name (6h)
- UI: Edit dialog
- API: PATCH /profile/name
- DB: Update profile
Each slice is independently deployable
Minimal end-to-end first:
1. Hello World page
2. One GET endpoint
3. Single table
4. Basic deploy
Then flesh out incrementally
Step 1: Minimal Service A (1h) - Hardcoded response
Step 2: Minimal Service B (1h) - Simple transformation
Step 3: Integrate (2h) - Prove they communicate
Total: 4 hours to decision point
| Points | Meaning |
|---|---|
| 1 | Trivial, < 1 hour |
| 2 | Simple, 1-2 hours |
| 3 | Standard, 2-4 hours |
| 5 | Moderate, 4-8 hours |
| 8 | Complex, 1-2 days |
| 13 | Very complex, 2-3 days |
| 21 | Too large, must decompose |
O = Optimistic (everything perfect)
L = Likely (normal case)
P = Pessimistic (major issues)
PERT estimate: (O + 4L + P) / 6
Building complete system before any delivery. Fix: Vertical slices, incremental value.
"Set up database," "Create service layer." Fix: Include in feature tasks: "User can view products (includes DB)."
Unbounded investigation. Fix: Time-boxed spikes with deliverables.
Over-analyzing before starting. Fix: Decompose next 2 weeks. Details for later work emerge.
Before starting any task:
If any "no" → further decomposition needed.
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
jwynia/agent-skills
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
I recommend task-decomposition for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Useful defaults in task-decomposition — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Useful defaults in task-decomposition — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Solid pick for teams standardizing on skills: task-decomposition is focused, and the summary matches what you get after install.
task-decomposition has been reliable in day-to-day use. Documentation quality is above average for community skills.
task-decomposition has been reliable in day-to-day use. Documentation quality is above average for community skills.
task-decomposition has been reliable in day-to-day use. Documentation quality is above average for community skills.
Solid pick for teams standardizing on skills: task-decomposition is focused, and the summary matches what you get after install.
Solid pick for teams standardizing on skills: task-decomposition is focused, and the summary matches what you get after install.
Solid pick for teams standardizing on skills: task-decomposition is focused, and the summary matches what you get after install.
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