estimate-story

name	estimate-story
description	Estimate story points for OLS Jira stories using the calibrated rubric derived from 300 completed stories. Fetches the story, applies the decision tree, sets the SP field, and adds the estimate as a comment. Use for on-demand estimation or after creating a new story.
argument-hint	OLS-1234 [OLS-1235 ...]

Estimate Story Points for OLS Stories

Overview

Estimate story points for one or more OLS Jira stories using the team's calibrated rubric. After estimating, set the story points field and add an estimation comment.

Usage

/estimate-story OLS-1234
/estimate-story OLS-1234 OLS-1235 OLS-1236

Also invoked automatically after creating a new OLS story. Works for Stories, Bugs, Tasks, Weaknesses, and Vulnerabilities.

Rubric Location

Read the full rubric from: story-point-rubric.md (in the workspace root)

You MUST read this file before estimating. It contains:

• Point definitions (0, 0.5, 1, 2, 3, 5) with characteristics and code complexity data
• Decision tree for base estimate
• Bias corrections from blind testing
• Complexity multipliers (up/down factors)
• Component-specific guidance

Workflow

Step 1: Read the rubric

Read story-point-rubric.md

Step 2: Parse story keys from arguments

Extract all OLS-XXXX keys from the skill arguments. If no arguments provided, ask the user for story key(s).

Step 3: For each story

3a. Fetch the story from Jira

Use mcp__plugin_atlassian_atlassian__getJiraIssue with:

• cloudId: redhat.atlassian.net
• issueIdOrKey: the story key
• responseContentFormat: markdown

Extract: summary, description, components, labels, current story points value.

If story points are already set, tell the user and ask whether to re-estimate or skip.

Note: The rubric was built from Stories but applies to all issue types that use story points. For Weaknesses and Vulnerabilities, treat them like Bugs — the complexity is in the investigation + fix, not the issue type label.

3b. Apply the rubric

Using the rubric you read in Step 1:

1. Identify work type — new feature, removal, refactor, test, spike, UI, operator, doc, CI, etc.
2. Count acceptance criteria — more criteria generally means more points
3. Check for cross-cutting concerns — cross-repo, multi-component, external systems
4. Apply the decision tree to get a base estimate
5. Apply bias corrections:
   • Vague/sparse descriptions → bias UP, not down
   • External system integration → add 1 point
   • "Integrate external library" → add 1 point
   • "Investigate and fix" tasks → 2 minimum, 3 if "across" anything
   • "Setup job following existing pattern" → often 2-3, not 1
6. Apply complexity multipliers (up/down factors from rubric)
7. Determine confidence level:
   • High: clear AC, specific file paths, established patterns
   • Medium: has AC but implementation details are ambiguous
   • Low: vague, no AC, spike-like scope

3c. Set story points on the Jira issue

Use mcp__plugin_atlassian_atlassian__editJiraIssue with:

• cloudId: redhat.atlassian.net
• issueIdOrKey: the story key
• fields: {"customfield_10028": <estimated_points>}

Do NOT update the description field. Only set the story points field.

3d. Add estimation comment

Use mcp__plugin_atlassian_atlassian__addCommentToJiraIssue with:

• cloudId: redhat.atlassian.net
• issueIdOrKey: the story key
• contentFormat: markdown
• commentBody: **AI Estimate:** X SP (confidence: high/medium/low)

Step 4: Report to user

For each story, report:

• Story key and summary
• Estimated SP and confidence level

Do NOT list comparable stories from history.

For low-confidence estimates, provide a range and note what would change the estimate.

Jira Field Reference

• Story Points field: customfield_10028 (number, float)
• Cloud ID: redhat.atlassian.net
• Project: OLS
• Fibonacci scale: 0, 0.5, 1, 2, 3, 5 (8+ means split the story)