Estimate software development tasks using story points, planning poker, and t-shirt sizing.
Works with
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
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiontask-estimationExecute 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.
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-estimation. Access via /task-estimation 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
4
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4
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Run in your terminal
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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
Process:
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
## 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
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
## 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
#estimation #agile #story-points #planning-poker #sprint-planning #project-management
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.
supercent-io/skills-template
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
task-estimation fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
task-estimation has been reliable in day-to-day use. Documentation quality is above average for community skills.
I recommend task-estimation for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Keeps context tight: task-estimation is the kind of skill you can hand to a new teammate without a long onboarding doc.
Solid pick for teams standardizing on skills: task-estimation is focused, and the summary matches what you get after install.
Registry listing for task-estimation matched our evaluation — installs cleanly and behaves as described in the markdown.
We added task-estimation from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
task-estimation reduced setup friction for our internal harness; good balance of opinion and flexibility.
Useful defaults in task-estimation — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
We added task-estimation from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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