skills/ai-documentation/SKILL.md
Auto-generated docs from code, README generation, API doc automation. [EXPLICIT] Trigger: "ai documentation"
npx skillsauth add JaviMontano/jm-adk-alfa ai-documentationInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
"Method over hacks."
Auto-generated docs from code, README generation, API doc automation. [EXPLICIT]
Example invocations:
| Scenario | Handling | |----------|----------| | Empty or minimal input | Request clarification before proceeding | | Conflicting requirements | Flag conflicts explicitly, propose resolution | | Out-of-scope request | Redirect to appropriate skill or escalate |
development
This skill should be used when the user asks to "design analytics models", "set up a dbt project", "plan data transformations", "define data contracts", or "model a star schema", or mentions staging models, marts, incremental strategies, or materializations. It produces analytics pipeline designs with dbt-style transformations, data modeling patterns, testing strategies, and documentation plans. [EXPLICIT] Use this skill whenever the user needs source-to-target mapping, materialization decisions, or transformation framework architecture, even if they don't explicitly ask for "analytics engineering". [EXPLICIT]
testing
Alert fatigue prevention, escalation rules, severity classification. [EXPLICIT] Trigger: "alerting strategy"
tools
LLM-in-the-loop workflows, human-AI handoff, approval gates. [EXPLICIT] Trigger: "ai workflow automation"
tools
Comprehensive testing strategy for AI systems — testing scope matrix (6 types x 6 layers), model prediction testing, data quality testing, compliance and fairness testing, integration approaches, and CI/CD test automation. This skill should be used when the user asks to "define AI testing strategy", "test ML models", "design data quality tests", "plan fairness testing", "test AI pipelines", "design integration tests for ML", or mentions adversarial testing, drift simulation, model regression testing, bias testing, explainability testing, or AI test automation. [EXPLICIT]