--- name: zephyr-coverage-analysis-a56744e1b25f description: "Analyze Zephyr test case coverage for a Jira user story and produce a QE management report with metrics, risk scoring, and prioritized recommendations." metadata: internal: true triggers: keywords: - zephyr coverage analysis - workflow --- # Zephyr Coverage Analysis Skill > [!IMPORTANT] > Analyze Zephyr test case coverage for a Jira user story and produce a QE management report with metrics, risk scoring, and prioritized recommendations. Optional args: slug=, ticket=, mode=interactive|autonomous|channel, channel=, auto_continue=true|false, profile=business|hybrid|technical. ## Instructions When the user asks to perform this workflow, execute the following steps: # Read-only audit — produces coverage_analysis_report.md, does NOT create or modify Zephyr test cases // turbo-all ## 1. Gather Input If the Jira Issue Key is not provided, ask for it. Optional: **Scope** (single ticket default, or comma-separated epic/sprint keys) | **Depth** (`quick` = coverage table only, `full` = default with risk scoring + QE observations). ## 2. Fetch Jira + Discover Zephyr TCs (Parallel Sub-Agents) Launch **two sub-agents in parallel** to keep raw API data out of main context: ### Sub-Agent A: Jira Analyst > Use `Agent` tool with `subagent_type="jira-analyst"`. > Prompt: "Fetch JIRA ticket {ISSUE_KEY} with `?expand=renderedFields`. Extract: Summary, Status, Components, Labels, Market, and the full AC table. For each AC row, read the Platform column from rendered HTML — color codes are authoritative: `#00B8D9` = Web, `#36B37E` = Mobile, `#FF991F` = Web+Mobile. Do NOT use ticket-level Platform — always read per-row from renderedFields. Return structured briefing." ### Sub-Agent B: Zephyr Scanner > Use `Agent` tool with `subagent_type="zephyr-scanner"`. > Prompt: "Find all Zephyr test cases related to {ISSUE_KEY}. Use direct issue link lookup first, then supplemental search only if needed. Return the filtered TC table with key, name, platform, labels, objective summary." Both agents run concurrently. Wait for both to complete before proceeding. ### Merge Results From Sub-Agent A: extract ACs with platform tags. From Sub-Agent B: extract the discovered TC list. ## 3. Map ACs to Coverage Status Per platform slot (Web and Mobile are independent — Mobile covered ≠ Web covered): | Status | Criteria | | --------------- | ----------------------------------------------------------------------------------------------------- | | **Covered** | TC directly references this AC (label, objective, or step) and scope matches (platform, role, market) | | **Partial** | TC exists but has generic objective, wrong role scope, different platform, or missing key assertions | | **Not Covered** | No TC found | Risk-score each gap: **HIGH** = transaction/financial/order completion | **MEDIUM** = feature behavior/conditional display | **LOW** = UI/visual/cosmetic ## 4. Build the Coverage Analysis Report Delete any existing `coverage_analysis_report.md` before creating a new one. Load the **Report Template** from the coverage analysis skill's `references/coverage_report_template.md` and use it to produce `coverage_analysis_report.md` with: Executive Dashboard, AC Heatmap, Quality Observations on existing TCs, QE Debt backlog, Prioritized Action Plan (P1/P2/P3), QE Manager Recommendations. Quality issues to flag on existing TCs: traceability mismatches (objective references wrong ticket), generic objectives (no business-logic assertions), combined TCs masking independent failures, missing data-correctness assertions. QE Debt to surface beyond AC gaps: data correctness (values validated against DB?), negative flows (backend unavailable?), role differentiation, boundary conditions, regression risk on shared screens. ## 5. Present Report & Offer Next Steps After writing the report, offer: 1. Proceed to create missing TCs (invoke zephyr-from-jira workflow for P1 gaps) 2. Export to Confluence or Jira comment 3. Run coverage analysis on a sibling ticket 4. Done Only if the user selects option 1, invoke TC creation. --- Source attribution: HoangNguyen0403 Original project: https://github.com/HoangNguyen0403/agent-skills-standard License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 Gabriel Moreira Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: HoangNguyen0403 Original project: https://github.com/HoangNguyen0403/agent-skills-standard Attribution basis: Upstream repository explicitly recorded in package mirror path Fields of work: Development & Engineering, Product Management, Project & Program Management Roles: Product Manager, Project Manager, QA / Test Engineer Specialties: API Tools: Jira Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 819ef92466ad9d87c65c276df7cc3c08d953e61240c88bce305f35f74fbada53