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osdu

GitLab CI/CD test job reliability analysis for OSDU projects. Tracks test job (unit/integration/acceptance) pass/fail status across pipeline runs. Use for test job status, flaky test job detection, test reliability/quality metrics, cloud provider analytics. Wraps osdu-quality CLI.

DeepseekModel Curated skill Quality Good · 48 v1.0.0

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https://deepseekmodel.com/api/download.php?id=danielscholl-agent-skills-plugins-osdu-skills-osdu-skill-md&format=skill
Download .skill Standard format with system_prompt and model_config, ready for any agent framework
The actual content of the system_prompt field in the .skill file.
name osdu description GitLab CI/CD test job reliability analysis for OSDU projects. Tracks test job (unit/integration/acceptance) pass/fail status across pipeline runs. Use for test job status, flaky test job detection, test reliability/quality metrics, cloud provider analytics. Wraps osdu-quality CLI. version 2.0.0 brief_description OSDU GitLab CI/CD test reliability analysis triggers {"keywords":["osdu","gitlab","quality","ci","cd","pipeline","test","job","reliability","flaky","acceptance","integration","unit","azure","aws","gcp","cloud","provider"],"verbs":["analyze","track","monitor","test","check"],"patterns":["test.*(?:reliability|status|job)","pipeline.*(?:analysis|status)","flaky.*test","ci.*cd","gitlab.*(?:pipeline|job)"]} allowed-tools Bash Analyze GitLab CI/CD test job reliability for OSDU platform projects, tracking test job pass/fail status across pipeline runs to identify flaky tests and provide quality metrics. OSDU project test status queries ("how is {project} looking", "partition test quality") Flaky test detection ("are there flaky tests in {project}") Pipeline health monitoring ("recent pipeline failures") Cloud provider comparison ("azure vs aws test reliability") Stage-specific analysis ("unit test status", "integration test failures") <skip-when> <condition>Individual test case tracking (we track job-level, not test-level)</condition> <condition>Non-test jobs (build, deploy, lint, security scans)</condition> <condition>Non-OSDU projects or non-GitLab CI systems</condition> <condition>Real-time monitoring (data is from completed pipelines only)</condition> </skip-when> Pipeline Run → Test Stage (unit/integration/acceptance) → Test Job → Test Suite (many tests) <capabilities> <supported>Test job pass/fail status across multiple pipeline runs</supported> <supported>Flaky test job detection (jobs that intermittently fail)</supported> <supported>Stage-level metrics (unit/integration/acceptance)</supported> <supported>Cloud provider breakdown (azure, aws, gcp, ibm, cimpl)</supported> <unsupported>Individual test results (not tracked)</unsupported> <unsupported>Non-test jobs like build, deploy, lint</unsupported> </capabilities> <example> Pipeline #1: job "unit-tests-azure" → PASS (100/100 tests passed) Pipeline #2: job "unit-tests-azure" → FAIL (99/100 tests passed) Pipeline #3: job "unit-tests-azure" → PASS (100/100 tests passed) Result: This job is FLAKY (unreliable across runs) </example> <progressive-approach mandatory="true"> <step number="1" name="start-light"> <action>Use status.py for quick overview</action> <command>script_run osdu status.py --format json --pipelines 3 --project {name}</command> <rationale>Lightweight, fast, safe token usage</rationale> </step> <step number="2" name="deep-dive" condition="only-if-needed"> <action>Use analyze.py with strict filters</action> <command>script_run osdu analyze.py --format json --pipelines 5 --project {name} --stage unit</command> <rationale>Heavy query, use only when status insufficient</rationale> </step> <step number="3" name="never-query-all"> <action>ALWAYS specify --project to avoid 30-project scan</action> <rationale>Prevents token limit exceeded error</rationale> </step> </progressive-approach> <format-selection> <format type="json"> <use-when>Extracting specific metrics or calculating statistics</use-when> <use-when>Building summaries or comparisons</use-when> <use-when>Parsing structured data programmatically</use-when> <use-when importance="critical">ALWAYS for status.py (lightweight, parseable)</use-when> </format> <format type="markdown"> <use-when>Analyze.py queries (10x smaller than JSON, still readable)</use-when> <use-when>Creating reports for sharing</use-when> <use-when>Need human-readable tables without parsing</use-when> <use-when>Token budget is tight</use-when> </format> <format type="terminal" status="never-use"> <avoid-because>Includes ANSI codes and colors, hard to parse</avoid-because> <avoid-because>Only for direct human terminal viewing</avoid-because> </format> </format-selection> <domain-services> <project name="wellbore-domain-services" description="Wellbore data"/> <project name="well-delivery" description="Well delivery workflows"/> <project name="seismic-store-service" description="Seismic data storage"/> <project name="dataset" description="Dataset management"/> <project name="register" description="Data registration"/> <project name="unit-service" description="Unit conversion"/> </domain-services> <reference-services> <project name="crs-catalog-service" description="Coordinate reference systems"/> <project name="crs-conversion-service" description="CRS conversion"/> </reference-services> <ddms-services> <project name="rafs-ddms-services" description="R&D data management"/> <project name="eds-dms" description="Engineering data management"/> </ddms-services> <workflow-processing> <project name="ingestion-workflow" description="Data ingestion pipelines"/> <project name="indexer-queue" description="Indexing queue management"/> <project name="notification" description="Event notifications"/> <project name="segy-to-mdio-conversion-dag" description="Seismic format conversion"/> </workflow-processing> <infrastructure> <project name="infra-azure-provisioning" description="Azure infra provisioning"/> <project name="os-core-common" description="Shared core libraries"/> <project name="os-core-lib-azure" description="Azure-specific libs"/> </infrastructure> <other-services> <project name="geospatial" description="Geospatial services"/> <project name="policy" description="Policy engine"/> <project name="secret" description="Secret management"/> <project name="open-etp-client" description="ETP protocol client"/> <project name="schema-upgrade" description="Schema migration tools"/> </other-services> <cloud-providers> <provider code="azure" name="Microsoft Azure"/> <provider code="aws" name="Amazon Web Services"/> <provider code="gcp" name="Google Cloud Platform"/> <provider code="ibm" name="IBM Cloud"/> <provider code="cimpl" name="CIMPL (Venus) provider"/> </cloud-providers> osdu-quality CLI installed: uv tool install git+https://community.opengroup.org/danielscholl/osdu-quality.git GitLab authentication (choose one): - GITLAB_TOKEN environment variable, OR - glab CLI authenticated (glab auth login) Access to OSDU GitLab projects Best approach: Start with status.py script_run osdu status.py --format json --pipelines 3 --project partition <pattern name="flaky-test-detection"> <step number="1">Check status</step> <command>script_run osdu status.py --format json --pipelines 5 --project partition</command> <step number="2">If issues found, deep dive with analyze.py</step> <command>script_run osdu analyze.py --format markdown --pipelines 5 --project partition --stage unit</command> </pattern> <pattern name="provider-comparison"> <description>Compare Azure vs AWS for specific project/stage</description> <command>script_run osdu analyze.py --format markdown --pipelines 5 --project storage --stage integration --provider azure</command> <command>script_run osdu analyze.py --format markdown --pipelines 5 --project storage --stage integration --provider aws</command>
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The downloaded .skill package contains the following fields.
Field Description
formatFormat tag (skill/v1)
skill_idUnique skill ID
nameSkill name
versionVersion
descriptionDescription
categoryCategories (array)
trigger_wordsTrigger words
tagsTags
sourceSource
source_urlSource URL (this page)
exported_atExported at (set per download)
system_promptSystem prompt body
model_configModel config: provider / model / temperature / max_tokens / top_p
examplesExamples
install_guideImport guide for Coze / Dify / Claude / custom frameworks
The same skill can be exported in different platform formats.
.skill Standard format with system_prompt and model_config, ready for any agent framework Download
.skillpro Enhanced format with scripts, tools, dependencies and hooks Download
.json Plain JSON export with system_prompt and model parameters only Download
Coze Markdown with frontmatter, for Coze platform import Download
Dify Dify DSL, import directly after creating an app Download

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