task-decomposition

jwynia/agent-skills · updated Apr 8, 2026

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$npx skills add https://github.com/jwynia/agent-skills --skill task-decomposition
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summary

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.

skill.md

Task Decomposition Diagnostic

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.

When to Use This Skill

Use this skill when:

  • A task feels too big to estimate
  • Unsure where to start
  • Blocked by dependencies
  • Task keeps growing (scope creep)
  • Need to break down an epic or feature

Do NOT use this skill when:

  • Task is already small and clear
  • Doing implementation work
  • Architecture decisions needed (use system-design)

Core Principle

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.

Quick Reference: Cognitive Limits

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

Task Duration Success Rates

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%

Diagnostic States

TD1: Too Big to Understand

Symptoms: Estimates range wildly, can't hold all requirements in mind, more than 7 concepts to track

Interventions:

  • Apply INVEST criteria: Independent, Negotiable, Valuable, Estimable, Small, Testable
  • Use vertical slicing (each slice is independently deployable)
  • Apply walking skeleton (minimal end-to-end first)

TD2: No Clear Entry Point

Symptoms: Multiple valid starting points, paralysis, everything seems connected

Interventions:

  • Front-load risk: start with highest-uncertainty items
  • Tracer bullet: minimal proof of concept
  • Find the walking skeleton: thinnest slice through all layers

TD3: Dependency Problems

Symptoms: "Blocked on X", diamond dependencies, coordination overhead

Interventions:

  • Interface contracts: define API, mock while implementing
  • Feature flags: deploy independently, enable when ready
  • Branch by abstraction: create layer, swap implementations

TD4: No Clear Done Criteria

Symptoms: "Almost done" forever, no way to verify completion

Interventions:

  • Define acceptance criteria (Given/When/Then)
  • Time-box to force prioritization
  • Define explicit out-of-scope items

TD5: Scope Creep

Symptoms: Task keeps growing, "while we're here" additions

Interventions:

  • Freeze scope, spawn new tasks for additions
  • Define minimum viable version
  • Ship smallest version that solves the problem

TD6: Need Spike First

Symptoms: Estimate variance > 4x, new technology, multiple approaches

Interventions:

  • Time-boxed spike (8 hours max)
  • Deliverables: options, POC, trade-offs, revised estimate
  • Spike then implement pattern

Decomposition Patterns

Vertical Slicing (Preferred for Features)

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

Walking Skeleton (For New Systems)

Minimal end-to-end first:
1. Hello World page
2. One GET endpoint
3. Single table
4. Basic deploy

Then flesh out incrementally

Tracer Bullet (Validate Architecture)

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

Estimation Techniques

Complexity Sizing (Fibonacci)

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

Three-Point Estimation

O = Optimistic (everything perfect)
L = Likely (normal case)
P = Pessimistic (major issues)

PERT estimate: (O + 4L + P) / 6

Anti-Patterns

Big Bang Delivery

Building complete system before any delivery. Fix: Vertical slices, incremental value.

Technical Tasks Without Value

"Set up database," "Create service layer." Fix: Include in feature tasks: "User can view products (includes DB)."

Research Forever

Unbounded investigation. Fix: Time-boxed spikes with deliverables.

Perfect Decomposition

Over-analyzing before starting. Fix: Decompose next 2 weeks. Details for later work emerge.

Decomposition Checklist

Before starting any task:

  • Can hold all requirements in working memory?
  • Duration under 2-3 days?
  • Clear acceptance criteria exist?
  • Dependencies identified and broken where possible?
  • Can be completed independently?
  • Delivers verifiable value?
  • Estimate confidence is high?

If any "no" → further decomposition needed.

Related Skills

  • github-agile - Track decomposed work as issues
  • system-design - Understand architectural boundaries
  • requirements-analysis - Clarify unclear requirements
  • code-review - Review after implementation
how to use task-decomposition

How to use task-decomposition on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add task-decomposition
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/jwynia/agent-skills --skill task-decomposition

The skills CLI fetches task-decomposition from GitHub repository jwynia/agent-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/task-decomposition

Reload or restart Cursor to activate task-decomposition. Access the skill through slash commands (e.g., /task-decomposition) or your agent's skill management interface.

Security & Verification Notice

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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

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Use Cases

User Story & Requirements Generation

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

Competitive Analysis

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

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

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

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ 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.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.533 reviews
  • Aditi Farah· Dec 28, 2024

    I recommend task-decomposition for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Pratham Ware· Dec 24, 2024

    Useful defaults in task-decomposition — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Tariq Ramirez· Dec 20, 2024

    Useful defaults in task-decomposition — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Sofia Anderson· Nov 19, 2024

    Solid pick for teams standardizing on skills: task-decomposition is focused, and the summary matches what you get after install.

  • Sakshi Patil· Nov 15, 2024

    task-decomposition has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Aisha Lopez· Nov 11, 2024

    task-decomposition has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Olivia Taylor· Oct 10, 2024

    task-decomposition has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Chaitanya Patil· Oct 6, 2024

    Solid pick for teams standardizing on skills: task-decomposition is focused, and the summary matches what you get after install.

  • Hassan Liu· Oct 2, 2024

    Solid pick for teams standardizing on skills: task-decomposition is focused, and the summary matches what you get after install.

  • Olivia Anderson· Sep 25, 2024

    Solid pick for teams standardizing on skills: task-decomposition is focused, and the summary matches what you get after install.

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