Quality Dashboard Design

When to Use

• Use this skill when you need evidence-bounded analysis, design, or validation preparation for quality dashboard audiences, decision questions, panels, drill-downs, freshness, and alert boundaries.
• Use it to review a plan, result, or metric and turn the review into executable improvements.
• Use it when input is incomplete but a bounded first pass with assumptions, gaps, and human-decision boundaries is still useful.

Output Format Options

• Default to Markdown organized by domain risk, evidence state, priority, and boundary.
• When the user requests tables, CSV, JSON, or ticket fields, preserve the same finding fields, evidence, and decision boundaries.
• Before machine consumption, confirm the schema, enums, required fields, and evidence sources.

How to Use

1. Read and follow prompts/quality-dashboard-design.md, including its input audit, domain coverage, and output order.
2. Extract scope, environment, version, time window, constraints, success criteria, and available evidence, with attention to audience, decision question, panel, drill-down path, freshness and alerting.
3. Separate confirmed facts, evidence-backed inferences, candidate recommendations, and Human decisions before ranking by risk and evidence strength.
4. Turn high-risk items into preconditions, steps, expected behavior or decision criteria, required evidence, and a validation method.
5. When input is incomplete, deliver a bounded first pass, state unsupported conclusions, and never present static material as execution evidence.

Reference Files

• Always read prompts/quality-dashboard-design.md; it is the complete execution specification for this skill.
• For evaluation, read evals/eval.yaml and the matching cases under evals/cases/.
• Load references/, examples/, scripts/, or output-formats.md only when those directories exist and the task needs them.

Core Constraints

• Keep the analysis focused on quality dashboard audiences, decision questions, panels, drill-downs, freshness, and alert boundaries; do not replace business owners or Human risk acceptance, exception approval, or release decisions.
• Never invent system behavior, fields, metrics, thresholds, data, root causes, execution records, or pass claims.
• Static design, plans, file presence, or a dry run retain their evidence state and cannot become proof of real execution.
• When evidence is insufficient, use pending confirmation, blocked, unassessed, or NOT_SCORED and give the smallest validation method.
• For production, privacy, or security work, use least privilege, masked data, mocks, dry runs, or isolation.

Delivery Checklist

• Covered audience, decision question, panel, drill-down path, freshness and alerting, with source, evidence state, and validation method for each.
• Separated facts, inferences, candidate recommendations, gaps, and Human decisions.
• Gave high-risk items P0/P1/P2/P3 or an equivalent priority, owner role, and close condition.
• Did not turn plans, static checks, or dry runs into test execution, all-passed, or release-approved claims.
• Stated residual risk, stop/escalation conditions, and next actions.

Common Pitfalls

• Listing checks without triggers, expected concerns, owner roles, close conditions, and evidence.
• Treating adjacent metrics or tool names as a complete quality dashboard judgment.
• Using unexplained numbers for false precision or writing correlation as causation.
• Refusing incomplete input, or pretending that incomplete evidence is conclusive.

Best Practices

• Start with paths most likely to cause business loss, quality regression, or decision blockage.
• Use the smallest verifiable experiment to reduce uncertainty and record conditions, versions, sources, and evidence.
• Make the Skill independently installable, executable, and reviewable by another engineer.