task-estimation
Estimate software development tasks using story points, planning poker, and t-shirt sizing.
Works with
4
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4
this week
88
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Install Skill
Run in your terminal
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
Installation Guide
How to use task-estimation on Cursor
AI-first code editor with Composer
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
Run the install command
Execute the skills CLI command in your project's root directory to begin installation:
Fetches task-estimation from supercent-io/skills-template and configures it for Cursor.
Select Cursor when prompted
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
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:
- Product Owner explains the story
- Team asks questions
- Everyone picks a card (1, 2, 3, 5, 8, 13)
- Reveal simultaneously
- Explain highest/lowest scores
- Re-vote
- 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)
- Relative estimation: Relative complexity instead of absolute time
- Team consensus: Agreement from the whole team, not individuals
- Use historical data: Plan based on velocity
Prohibited (MUST NOT)
- Pressuring individuals: Estimates are not promises
- Overly granular estimation: Split anything 13+ points
- Turning estimates into deadlines: estimate ≠ commitment
Best practices
- Break Down: Split big work into smaller pieces
- Reference Stories: Reference similar past work
- 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
- 1Install product management skill
- 2Start with user story generation for known feature
- 3Progress to competitive analysis: research 2-3 competitors
- 4Use for roadmap prioritization: apply RICE/ICE scoring
- 5Draft stakeholder communications and refine based on feedback
- 6Build template library for recurring PM tasks
- 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
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
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Reviews
- AAmina Taylor★★★★★Dec 20, 2024
task-estimation fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- DDhruvi Jain★★★★★Dec 16, 2024
task-estimation has been reliable in day-to-day use. Documentation quality is above average for community skills.
- TTariq Tandon★★★★★Dec 16, 2024
I recommend task-estimation for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- NNaina Verma★★★★★Dec 8, 2024
Keeps context tight: task-estimation is the kind of skill you can hand to a new teammate without a long onboarding doc.
- RRahul Santra★★★★★Nov 27, 2024
Solid pick for teams standardizing on skills: task-estimation is focused, and the summary matches what you get after install.
- LLucas Johnson★★★★★Nov 27, 2024
Registry listing for task-estimation matched our evaluation — installs cleanly and behaves as described in the markdown.
- LLucas Chen★★★★★Nov 11, 2024
We added task-estimation from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- OOshnikdeep★★★★★Nov 7, 2024
task-estimation reduced setup friction for our internal harness; good balance of opinion and flexibility.
- AAma Sanchez★★★★★Nov 7, 2024
Useful defaults in task-estimation — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- GGanesh Mohane★★★★★Oct 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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