Issue Estimate Sp

Estimate

Estimate story points for a Jira ticket via context gathering and a scrum-poker council session. This is a read-only operation. Never update the ticket.

Input

• ticket_key (required) — e.g. ABC-12345

Workflow

Estimate Progress:
- [ ] Step 1: Gather ticket context from Jira
- [ ] Step 2: Gather related context (feature, linked tickets, Confluence)
- [ ] Step 3: Load reference data for calibration
- [ ] Step 4: Run scrum-poker council session
- [ ] Step 5: Present estimate with rationale

Step 1: Gather ticket context

Use the Atlassian MCP (read-only) to collect:

1. Issue details — jira_get_issue with the ticket key. Extract: summary, description, issue type, priority, labels, components, current story points (if any), acceptance criteria.
2. Comments — jira_get_comments for team discussion and context.
3. Changelog — jira_get_changelog to see if story points were previously set, changed, or debated.

Step 2: Gather related context

1. Parent feature/epic — if the issue has a Feature Link or Epic Link, fetch it with jira_get_issue to understand the broader scope.
2. Linked tickets — follow issue links (blocks, relates-to, split-from) to understand dependencies and prior work. Limit to 3-5 most relevant links.
3. Confluence context — if the ticket references Confluence pages or has remote links, fetch up to 2 pages for additional design/requirements context using confluence_get_page.
4. Similar completed tickets — search for recently closed tickets with similar labels (components are rarely populated — only 8% of tickets have them). Use team labels (emerald, pyrite, obsidian) and category labels (POAM, sp-candidate, spTooling, documentation) for matching. Example JQL: project = {PROJECT} AND labels in ({TEAM_LABEL}) AND status in (Done) AND "Story Points" is not EMPTY ORDER BY resolutiondate DESC (limit 5). Also check for keyword-similar tickets using a text search on the summary.

Step 3: Load reference data

Read REFERENCE_DATA.md to calibrate the estimate against historical actuals. This file contains:

• Representative tickets at each Fibonacci level with actual flow times
• Known estimation biases in the project (8 documented biases)
• Summary keyword anchors (e.g. "smoke" = always 1, "STIG" = typically 3-5)
• Team estimation profiles by label
• Guidance on when to adjust estimates up or down

Quick-check the keyword anchors first. If the ticket summary contains a strong keyword signal (e.g. "smoke test", "upgrade", "investigate"), use the keyword table as a starting point before the full council session.

Use this data to ground the council's discussion in empirical reality, not just gut feel.

Step 4: Run scrum-poker council session

Invoke the clarity-council skill with the following configuration:

user_problem: "Estimate story points for {TICKET_KEY}: {SUMMARY}"

context: |
  {Paste the gathered ticket description, acceptance criteria, and key details}
  
  Related context:
  {Feature/epic summary if available}
  {Key linked ticket summaries}
  
  Reference calibration:
  {Summary of relevant reference data from REFERENCE_DATA.md — 
    include the representative tickets at the 2-3 most likely SP levels}

desired_outcome: >
  A Fibonacci story point estimate (1, 2, 3, 5, 8, 13, or 21) with rationale.
  Each persona should independently "play a card" (pick a number),
  then the group synthesizes to a final recommendation.

constraints:
  - 1 story point ≈ 1 business day of flow time (In Progress to Done)
  - Use only Fibonacci values: 1, 2, 3, 5, 8, 13, 21
  - Account for coordination overhead, not just coding effort
  - Investigation/prototype tickets tend to resolve faster than estimated
  - Consider review queue time as part of the estimate
  - This is READ ONLY — do not modify the ticket

selected_personas:
  - scrum-master
  - tech-lead
  - senior-developer
  - qa-engineer

depth: standard

Persona overrides for scrum poker:

• scrum-master: Focus on historical velocity, team capacity, and whether this fits cleanly into a sprint. Compare against reference data for similar tickets. Watch for estimation anti-patterns (artificial splitting, optimism bias).
• tech-lead: Focus on technical complexity, unknowns, dependencies, and coordination tax. Flag if this crosses team boundaries or requires environment access.
• senior-developer: Focus on implementation effort — lines of code, number of files, testing complexity, review burden. Be concrete about what "done" looks like.
• qa-engineer: Focus on test coverage needed, edge cases, regression risk, and validation effort. Flag if testing requires a deployed environment or special access.

Step 5: Present estimate

Format the output as:

## Story Point Estimate: {TICKET_KEY}

**Recommended: {N} story points**

### Scrum Poker Results

| Persona | Card Played | Key Reasoning |
|---------|-------------|---------------|
| Scrum Master | {N} | {one sentence} |
| Tech Lead | {N} | {one sentence} |
| Senior Developer | {N} | {one sentence} |
| QA Engineer | {N} | {one sentence} |

### Why {N} points

{2-3 sentences synthesizing the council's reasoning. Reference the closest
comparable ticket from REFERENCE_DATA.md if one exists.}

### Risk factors

- {bullet list of things that could push the estimate higher}

### Comparable tickets

| Ticket | Summary | Est. SP | Actual Days |
|--------|---------|---------|-------------|
| {key} | {summary} | {sp} | {days} |

> **Note:** This estimate was generated from historical data and persona analysis.
> It has not been written to the ticket. Discuss with your team before committing.