JIRA Story Point Estimator Skill

When to Use This Skill

Activate this skill when the user:

• Asks "estimate this ticket" or "how many story points?"
• Asks to "size" or "point" a ticket
• Asks "what should this be pointed at?"
• Wants help with sprint planning estimation
• Asks "is X points right for this ticket?"
• Reviews a ticket that needs story points

Story Point Scale Reference

HyperFleet uses a modified Fibonacci sequence for story points:

Points	Meaning	Typical Scope	Notes
0	Tracking Only	Quick/easy task with stakeholder value	Rarely used. For tasks worth tracking but with negligible effort compared to a 1-pointer
1	Trivial	One-line change, extremely simple task	The smallest issue possible - everything scales from here. No risk, very low effort, very low complexity
3	Straightforward	Time consuming but fairly straightforward work	Doesn't have to be complex, but usually time consuming. Minor risks possible
5	Medium	Requires investigation, design, collaboration	Probably needs discussion with others. Can be quite time consuming or complex. Risks involved
8	Large	Big task requiring investigation and design	Requires collaboration with others. Solution can be quite challenging. Risks are expected. Design doc required
13	Too Large	Should be split into smaller stories	Ideally, this shouldn't be used. If you see an issue this big, it must be broken down

Estimation Methodology

Step 1: Fetch Ticket Details

jira issue view TICKET-KEY --plain 2>/dev/null

Step 2: Analyze Complexity Factors

Scope Indicators (examine description and acceptance criteria):

• Number of acceptance criteria
• Number of components/files likely affected
• Integration points mentioned
• Testing requirements

Complexity Indicators:

• New feature vs. modification vs. bug fix
• Requires new patterns or unfamiliar technology
• External dependencies (APIs, services)
• Database/schema changes
• Security implications
• Documentation requirements

Risk Indicators:

• Ambiguity in requirements
• Dependencies on other tickets
• Time-sensitive (blocks other work)
• Requires coordination with other teams

Step 3: Find Similar Historical Tickets

Search for comparable completed tickets:

jira issue list -q"project = HYPERFLEET AND status = Done AND type = Story" --plain 2>/dev/null | head -20

jira issue list -q"project = HYPERFLEET AND status = Done AND 'Story Points' is not EMPTY AND type = Story" --plain 2>/dev/null | head -20

Step 4: Provide Estimation

Output Format

Estimation for: TICKET-KEY

Summary: [Ticket title] Type: [Story/Task/Bug]

---

Complexity Analysis

Factor	Assessment	Impact
Scope	[Small/Medium/Large]	[Description]
Technical Complexity	[Low/Medium/High]	[Why]
Dependencies	[None/Few/Many]	[List if any]
Testing Effort	[Minimal/Moderate/Extensive]	[Why]
Risk/Uncertainty	[Low/Medium/High]	[Why]

---

Recommendation

Suggested Story Points: X

Confidence Level: [High/Medium/Low]

Reasoning:

• [Primary factor 1]
• [Primary factor 2]
• [Comparison to similar work]

---

Similar Completed Tickets (for reference)

Ticket	Summary	Points	Similarity
TICKET-A	[Summary]	5	High - similar scope
TICKET-B	[Summary]	3	Medium - simpler version

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Considerations

• If higher: [What would make this larger]
• If lower: [What would make this smaller]
• Break-down suggestion: [If 13+ points, suggest how to split]

Setting Story Points

Once agreed, set story points using:

jira issue edit TICKET-KEY --custom story-points=X --no-input

Or for Story Point Estimate field (Next-gen projects):

jira issue edit TICKET-KEY --custom story-point-estimate=X --no-input

Team Calibration Notes

When estimating, consider:

• Team's typical velocity (points completed per sprint)
• Recent similar work and how long it actually took
• Team familiarity with the codebase area
• Current sprint load and focus

Anti-Patterns to Flag

• Anchoring: Don't let existing (wrong) estimates bias you
• Scope Creep: Point what's written, not what might be added
• Hero Estimates: Assume average team member, not expert
• Planning Fallacy: Add buffer for unknowns
• Story Point Inflation: Keep consistent with team baseline