task-estimation

Estimate software development tasks using story points, planning poker, and t-shirt sizing.

supercent-io/skills-templateUpdated Jun 4, 2026

Works with

Claude CodeCursorClineWindsurfCodexGooseGitHub CopilotZed

4

total installs

4

this week

88

GitHub stars

0

upvotes

Install Skill

Run in your terminal

$npx skills add https://github.com/supercent-io/skills-template --skill task-estimation

4

installs

4

this week

88

stars

What it does

  • Provides Fibonacci-based story point scales (1–21+) with time and complexity guidelines for each level

  • Includes planning poker process for team consensus estimation with discussion and re-voting workflows

  • Offers t-shirt sizing (XS–XL) for quick backlog grooming and rough roadmap planning

  • Adjusts estimates for risk and uncertainty using configurable buffers, with examples for medium and high

Category

Productivity

Last updated

Jun 4, 2026

Installation Guide

How to use task-estimation 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 machine
  • Node.js 16+ with npm — verify with node --version
  • Active project directory where you want to add task-estimation
2

Run the install command

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

$npx skills add https://github.com/supercent-io/skills-template --skill task-estimation

Fetches task-estimation from supercent-io/skills-template and configures it for Cursor.

3

Select Cursor when prompted

The CLI shows a list of agents. Use arrow keys and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ────────────────
│ · Cline · Codex · Goose · Windsurf
│ ●Cursor(selected)
│ · Cursor · Aider · Continue
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/task-estimation

Restart Cursor to activate task-estimation. Access via /task-estimation in your agent's command palette.

Security 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 environment. Always review source, verify the publisher, and test in isolation before production.

Documentation

Task Estimation

When to use this skill

  • Sprint Planning: Decide what work to include in the sprint
  • Roadmap creation: Build long-term plans
  • Resource planning: Estimate team size and schedule

Instructions

Step 1: Story Points (relative estimation)

Fibonacci sequence: 1, 2, 3, 5, 8, 13, 21

## Story Point guidelines

### 1 Point (Very Small)
- Example: text change, constant value update
- Time: 1-2 hours
- Complexity: very low
- Risk: none

### 2 Points (Small)
- Example: simple bug fix, add logging
- Time: 2-4 hours
- Complexity: low
- Risk: low

### 3 Points (Medium)
- Example: simple CRUD API endpoint
- Time: 4-8 hours
- Complexity: medium
- Risk: low

### 5 Points (Medium-Large)
- Example: complex form implementation, auth middleware
- Time: 1-2 days
- Complexity: medium
- Risk: medium

### 8 Points (Large)
- Example: new feature (frontend + backend)
- Time: 2-3 days
- Complexity: high
- Risk: medium

### 13 Points (Very Large)
- Example: payment system integration
- Time: 1 week
- Complexity: very high
- Risk: high
- **Recommended**: Split into smaller tasks

### 21+ Points (Epic)
- **Required**: Must be split into smaller stories

Step 2: Planning Poker

Process:

  1. Product Owner explains the story
  2. Team asks questions
  3. Everyone picks a card (1, 2, 3, 5, 8, 13)
  4. Reveal simultaneously
  5. Explain highest/lowest scores
  6. Re-vote
  7. Reach consensus

Example:

Story: "Users can upload a profile photo"

Member A: 3 points (simple frontend)
Member B: 5 points (image resizing needed)
Member C: 8 points (S3 upload, security considerations)

Discussion:
- Use an image processing library
- S3 is already set up
- File size validation needed

Re-vote → consensus on 5 points

Step 3: T-Shirt Sizing (quick estimation)

## T-Shirt sizes

- **XS**: 1-2 Story Points (within 1 hour)
- **S**: 2-3 Story Points (half day)
- **M**: 5 Story Points (1-2 days)
- **L**: 8 Story Points (1 week)
- **XL**: 13+ Story Points (needs splitting)

**When to use**:
- Initial backlog grooming
- Rough roadmap planning
- Quick prioritization

Step 4: Consider risk and uncertainty

Estimation adjustment:

interface TaskEstimate {
  baseEstimate: number;      // base estimate
  risk: 'low' | 'medium' | 'high';
  uncertainty: number;        // 0-1
  finalEstimate: number;      // adjusted estimate
}

function adjustEstimate(estimate: TaskEstimate): number {
  let buffer = 1.0;

  // risk buffer
  if (estimate.risk === 'medium') buffer *= 1.3;
  if (estimate.risk === 'high') buffer *= 1.5;

  // uncertainty buffer
  buffer *= (1 + estimate.uncertainty);

  return Math.ceil(estimate.baseEstimate * buffer);
}

// Example
const task = {
  baseEstimate: 5,
  risk: 'medium',
  uncertainty: 0.2  // 20% uncertainty
};

const final = adjustEstimate(task);  // 5 * 1.3 * 1.2 = 7.8 → 8 points

Output format

Estimation document template

## Task: [Task Name]

### Description
[work description]

### Acceptance Criteria
- [ ] Criterion 1
- [ ] Criterion 2
- [ ] Criterion 3

### Estimation
- **Story Points**: 5
- **T-Shirt Size**: M
- **Estimated Time**: 1-2 days

### Breakdown
- Frontend UI: 2 points
- API Endpoint: 2 points
- Testing: 1 point

### Risks
- Uncertain API response time (medium risk)
- External library dependency (low risk)

### Dependencies
- User authentication must be completed first

### Notes
- Need to discuss design with UX team

Constraints

Required rules (MUST)

  1. Relative estimation: Relative complexity instead of absolute time
  2. Team consensus: Agreement from the whole team, not individuals
  3. Use historical data: Plan based on velocity

Prohibited (MUST NOT)

  1. Pressuring individuals: Estimates are not promises
  2. Overly granular estimation: Split anything 13+ points
  3. Turning estimates into deadlines: estimate ≠ commitment

Best practices

  1. Break Down: Split big work into smaller pieces
  2. Reference Stories: Reference similar past work
  3. Include buffer: Prepare for the unexpected

References

Metadata

Version

  • Current version: 1.0.0
  • Last updated: 2025-01-01
  • Compatible platforms: Claude, ChatGPT, Gemini

Tags

#estimation #agile #story-points #planning-poker #sprint-planning #project-management

Examples

Example 1: Basic usage

Example 2: Advanced usage

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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

Steps

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

Related Skills

Reviews

4.554 reviews
  • A
    Amina TaylorDec 20, 2024

    task-estimation fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • D
    Dhruvi JainDec 16, 2024

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

  • T
    Tariq TandonDec 16, 2024

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

  • N
    Naina VermaDec 8, 2024

    Keeps context tight: task-estimation is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • R
    Rahul SantraNov 27, 2024

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

  • L
    Lucas JohnsonNov 27, 2024

    Registry listing for task-estimation matched our evaluation — installs cleanly and behaves as described in the markdown.

  • L
    Lucas ChenNov 11, 2024

    We added task-estimation from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • O
    OshnikdeepNov 7, 2024

    task-estimation reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • A
    Ama SanchezNov 7, 2024

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

  • G
    Ganesh MohaneOct 26, 2024

    We added task-estimation from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

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