agentleFS
katalon-labskatalon-labs/true-skills/.github/skills/release-analyze/SKILL.md

release-analyze

Skill10 starsChanged 5 months agoMITView on GitHub

Analyze Katalon True Platform/TestOps release readiness from testing quality data. Use when you need to use Katalon MCP metrics and results to assess whether a release, sprint, iteration, version, test plan, suite, or repository is ready to ship; summarize requirement coverage, execution health, defect risk, test stability, configuration coverage, release blockers, quality gaps, and produce a Ready / Ready with risk / Not ready recommendation. Written for the test manager who owns the ship call and the test lead who has to defend it.

The file

Show its rules and text

We split this skill into the rules it gives an agent

16 rules. About 0 are common, 9 are distinctive, and 7 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈9

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 7

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Use this skill to assess release readiness from Katalon MCP data.

    line 8
  2. Distinctive:

    Not directly available: create formal Test Plan entities, inspect live AUT UI, guarantee AI execution completion, or decide …

    line 15
  3. Distinctive:

    If MCP tools are unavailable, provide only a framework/template and mark release confidence as Low until data is verified.

    line 16
  4. Very specific:

    Read references/capability-boundaries.md when capability scope is unclear.

    line 18
  5. Distinctive:

    If the user gives only a release name or issue key, use MCP discovery first.

    line 29
  6. Distinctive:

    Always resolve context before analysis:

    line 33
  7. Very specific:

    Test case quality: find_test_cases, find_test_cases_by_requirement, fetch_test_case_data.

    line 43
  8. Distinctive:

    Use the freshest data available for the requested release.

    line 50
  9. Distinctive:

    Test coverage: total tests, automated/manual split, unexecuted, blocked, stale, missing ownership or incomplete cases.

    line 57
  10. Very specific:

    Read references/release-quality-gates.md before making the final readiness decision.

    line 64
  11. Distinctive:

    If thresholds are provided, apply them.

    line 74
  12. Very specific:

    Never mark Ready when:

    line 76
  13. Distinctive:

    Create or link missing tests for uncovered critical requirements.

    line 118
  14. Very specific:

    Use execute-test when execution is needed.

    line 122
  15. Very specific:

    Use create-test-cases when coverage gaps require new manual cases.

    line 123
  16. Very specific:

    Use upload-report when external automation results must be uploaded before assessment.

    line 124

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Katalon Release Analyze

Use this skill to assess release readiness from Katalon MCP data. The output must be a testing-quality decision, not just a metric dump.

Availability Boundary

State the MCP boundary before promising an assessment:

  • Available through Katalon MCP: project/repository discovery, requirements, test cases, test suites, executions/results, quality summaries, defect data, stability data, and configuration coverage when tools are exposed.
  • Not directly available: create formal Test Plan entities, inspect live AUT UI, guarantee AI execution completion, or decide product/business readiness beyond test evidence.
  • If MCP tools are unavailable, provide only a framework/template and mark release confidence as Low until data is verified.

Read references/capability-boundaries.md when capability scope is unclear.

Required Scope

Resolve or ask for the minimum missing scope:

  • Katalon project and repository/Test Project.
  • Release identifier, sprint, iteration, version, test suite, suite collection, execution ID, or date range.
  • Target environment/configuration matrix when readiness depends on browsers, devices, OS, or deployment environments.
  • Quality gate thresholds when the team has defined them.

If the user gives only a release name or issue key, use MCP discovery first. Ask only when multiple equally plausible scopes remain.

MCP Data Collection

Always resolve context before analysis:

  1. Call list_projects.
  2. Call list_repositories.
  3. Resolve the repository/Test Project.
  4. Find the release scope using available requirement, suite, execution, iteration, or result tools.

Collect evidence with the available Katalon MCP tools:

  • Requirement and traceability: find_requirements, read_requirement, fetch_requirement_data.
  • Test case quality: find_test_cases, find_test_cases_by_requirement, fetch_test_case_data.
  • Suites and executable scope: find_test_suites, read_test_suite.
  • Executions and results: read_execution, read_execution_test_results, read_test_result, find_test_results.
  • Defect risk: fetch_defect_data.
  • Stability: fetch_test_stability_data.
  • Configuration coverage: fetch_test_configuration_data.

Use the freshest data available for the requested release. If comparing trend or stability, include the date range used.

Key Metrics

Report the metrics that are available and relevant:

  • Requirement coverage: total, covered, uncovered, linked test cases, requirements without passing evidence.
  • Test coverage: total tests, automated/manual split, unexecuted, blocked, stale, missing ownership or incomplete cases.
  • Execution health: latest executions, pass/fail/blocked/skipped/not-run counts, pass rate, failed P0/P1 tests.
  • Defect risk: open critical/high defects, release blockers, reopened defects, failed tests with linked defects, untriaged failures.
  • Stability: flaky tests, repeated failures, unstable suites/configurations, recent trend.
  • Configuration coverage: environments, browsers, devices, OS, execution profiles, and missing target combinations.
  • Data confidence: missing MCP data, stale executions, ambiguous release scope, or incomplete links.

Read references/release-quality-gates.md before making the final readiness decision.

Readiness Decision

Return one of:

  • Ready: no testing blocker remains; critical requirements have coverage and recent passing evidence; target configurations are sufficiently tested.
  • Ready with risk: no hard blocker remains, but moderate gaps or accepted risks exist.
  • Not ready: release-blocking defects, failed/blocked/unrun critical tests, missing critical coverage, stale evidence, or untested required configurations remain.

If thresholds are provided, apply them. If not, use risk-based judgment and state assumptions.

Never mark Ready when:

  • Critical/high release-blocking defects remain open.
  • P0/P1 tests are failed, blocked, or not run without accepted risk.
  • Critical requirements have no tests or no passing evidence.
  • Required target configurations are untested.
  • MCP data is unavailable or too incomplete to support confidence.

Response Format

Respond with:

Release Readiness: Ready | Ready with risk | Not ready
Confidence: High | Medium | Low

Key Metrics:
- Requirement coverage:
- Test execution:
- Defect risk:
- Stability:
- Configuration coverage:

Blocking Issues:
- ...

Risks / Gaps:
- ...

Recommendation:
- ...

Evidence:
- ...

Evidence should include execution IDs, suite names, requirement keys, defect IDs, or links when returned by the platform.

Follow-Up Actions

Recommend concrete next steps:

  • Create or link missing tests for uncovered critical requirements.
  • Re-run failed, flaky, or stale suites.
  • Resolve or accept specific defects.
  • Expand configuration coverage for missing release targets.
  • Use execute-test when execution is needed.
  • Use create-test-cases when coverage gaps require new manual cases.
  • Use upload-report when external automation results must be uploaded before assessment.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.