estimate
### Estimate
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Install Skill
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What it does
description: "Estimates task effort by analyzing complexity, dependencies, historical velocity, and risk factors. Produces a structured estimate with confidence levels."
argument-hint: "[task-description]"
allowed-tools: Read, Glob, Grep
Installation Guide
How to use estimate 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
estimate
Run the install command
Execute the skills CLI command in your project's root directory to begin installation:
Fetches estimate from Donchitos/Claude-Code-Game-Studios 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 estimate. Access via /estimate 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
| name | estimate |
| description | "Estimates task effort by analyzing complexity, dependencies, historical velocity, and risk factors. Produces a structured estimate with confidence levels." |
| argument-hint | "[task-description]" |
| user-invocable | true |
| allowed-tools | Read, Glob, Grep |
Phase 1: Understand the Task
Read the task description from the argument. If the description is too vague to estimate meaningfully, ask for clarification before proceeding.
Read CLAUDE.md for project context: tech stack, coding standards, architectural patterns, and any estimation guidelines.
Read relevant design documents from design/gdd/ if the task relates to a documented feature or system.
Phase 2: Scan Affected Code
Identify files and modules that would need to change:
- Assess complexity (size, dependency count, cyclomatic complexity)
- Identify integration points with other systems
- Check for existing test coverage in the affected areas
- Read past sprint data from
production/sprints/for similar completed tasks and historical velocity
Phase 3: Analyze Complexity Factors
Code Complexity:
- Lines of code in affected files
- Number of dependencies and coupling level
- Whether this touches core/engine code vs leaf/feature code
- Whether existing patterns can be followed or new patterns are needed
Scope:
- Number of systems touched
- New code vs modification of existing code
- Amount of new test coverage required
- Data migration or configuration changes needed
Risk:
- New technology or unfamiliar libraries
- Unclear or ambiguous requirements
- Dependencies on unfinished work
- Cross-system integration complexity
- Performance sensitivity
Phase 4: Generate the Estimate
## Task Estimate: [Task Name]
Generated: [Date]
### Task Description
[Restate the task clearly in 1-2 sentences]
### Complexity Assessment
| Factor | Assessment | Notes |
|--------|-----------|-------|
| Systems affected | [List] | [Core, gameplay, UI, etc.] |
| Files likely modified | [Count] | [Key files listed below] |
| New code vs modification | [Ratio] | |
| Integration points | [Count] | [Which systems interact] |
| Test coverage needed | [Low / Medium / High] | |
| Existing patterns available | [Yes / Partial / No] | |
**Key files likely affected:**
- `[path/to/file1]` -- [what changes here]
### Effort Estimate
| Scenario | Days | Assumption |
|----------|------|------------|
| Optimistic | [X] | Everything goes right, no surprises |
| Expected | [Y] | Normal pace, minor issues, one round of review |
| Pessimistic | [Z] | Significant unknowns surface, blocked for a day |
**Recommended budget: [Y days]**
### Confidence: [High / Medium / Low]
[Explain which factors drive the confidence level for this specific task.]
### Risk Factors
| Risk | Likelihood | Impact | Mitigation |
|------|-----------|--------|------------|
### Dependencies
| Dependency | Status | Impact if Delayed |
|-----------|--------|-------------------|
### Suggested Breakdown
| # | Sub-task | Estimate | Notes |
|---|----------|----------|-------|
| 1 | [Research / spike] | [X days] | |
| 2 | [Core implementation] | [X days] | |
| 3 | [Testing and validation] | [X days] | |
| | **Total** | **[Y days]** | |
### Notes and Assumptions
- [Key assumption that affects the estimate]
- [Any caveats about scope boundaries]
Output the estimate with a brief summary: recommended budget, confidence level, and the single biggest risk factor.
This skill is read-only — no files are written. Verdict: COMPLETE — estimate generated.
Phase 5: Next Steps
- If confidence is Low: recommend a time-boxed spike (
/prototype) before committing. - If the task is > 10 days: recommend breaking it into smaller stories via
/create-stories. - To schedule the task: run
/sprint-plan updateto add it to the next sprint.
Guidelines
- Always give a range (optimistic / expected / pessimistic), never a single number
- The recommended budget should be the expected estimate, not the optimistic one
- Round to half-day increments — estimating in hours implies false precision for tasks longer than a day
- Do not pad estimates silently — call out risk explicitly so the team can decide
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Steps
- 1Install skill using provided installation command
- 2Test with simple use case relevant to your work
- 3Evaluate output quality and relevance
- 4Iterate on prompts to improve results
- 5Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
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Reviews
- IIsabella Ramirez★★★★★Dec 16, 2024
We added estimate from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- KKaira Srinivasan★★★★★Dec 12, 2024
Solid pick for teams standardizing on skills: estimate is focused, and the summary matches what you get after install.
- NNeel Torres★★★★★Dec 12, 2024
Solid pick for teams standardizing on skills: estimate is focused, and the summary matches what you get after install.
- LLiam Bhatia★★★★★Dec 8, 2024
Registry listing for estimate matched our evaluation — installs cleanly and behaves as described in the markdown.
- DDhruvi Jain★★★★★Dec 4, 2024
estimate has been reliable in day-to-day use. Documentation quality is above average for community skills.
- LLiam Torres★★★★★Nov 27, 2024
estimate reduced setup friction for our internal harness; good balance of opinion and flexibility.
- OOshnikdeep★★★★★Nov 23, 2024
Solid pick for teams standardizing on skills: estimate is focused, and the summary matches what you get after install.
- OOmar Smith★★★★★Nov 7, 2024
Keeps context tight: estimate is the kind of skill you can hand to a new teammate without a long onboarding doc.
- KKaira White★★★★★Nov 3, 2024
estimate has been reliable in day-to-day use. Documentation quality is above average for community skills.
- AArjun Shah★★★★★Nov 3, 2024
estimate has been reliable in day-to-day use. Documentation quality is above average for community skills.
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Discussion
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