Dashboard Tooling & Data Feeds

TL;DR: Configures project management dashboard tooling including data feed setup, visualization component selection, refresh cadence, and alert configuration. Ensures dashboards are automatically updated with live project data rather than requiring manual input.

Principio Rector

Un dashboard que requiere actualización manual no es un dashboard — es una presentación recurrente. La automatización de data feeds es lo que transforma reportes en monitoreo en tiempo real. El valor del dashboard es inversamente proporcional al esfuerzo de mantenerlo actualizado.

Assumptions & Limits

• Assumes PM tools have APIs or export capabilities for data extraction [SUPUESTO]
• Assumes dashboard platform is selected and accessible [PLAN]
• Breaks when data sources are manual spreadsheets with no API — design data pipeline first
• Does not design dashboard layout — that is the output of executive-dashboard or similar
• Data feed reliability depends on source system availability; design fallback for outages
• Alert fatigue occurs when thresholds are too sensitive — calibrate over ≥2 reporting cycles [METRIC]

Usage

# Configure data feeds for project dashboard
/pm:dashboard-tooling $PROJECT --type=feeds --sources="jira,ado"

# Set up alert rules for project metrics
/pm:dashboard-tooling $PROJECT --type=alerts --metrics="cpi,spi,velocity"

# Configure dashboard refresh cadence
/pm:dashboard-tooling $PROJECT --type=refresh --cadence="hourly"

Parameters:

Parameter	Required	Description
$PROJECT	Yes	Project identifier
--type	Yes	feeds, alerts, refresh, access-control, maintenance
--sources	No	Data sources (jira, ado, monday, sheets)
--metrics	No	Metrics for alert configuration
--cadence	No	Refresh frequency (real-time, hourly, daily)

Service Type Routing

{TIPO_PROYECTO}: All project types need dashboard tooling. Agile uses velocity and burndown widgets; Waterfall uses EVM and milestone widgets; Portfolio uses heatmap and aggregate views.

Before Configuring

1. Read the dashboard design to understand which metrics and visualizations need data feeds
2. Read the PM tool API documentation to assess data extraction capabilities
3. Glob skills/dashboard-tooling/references/*.md for data feed configuration patterns
4. Grep for existing dashboard configurations or data integration scripts

Entrada (Input Requirements)

• Dashboard design specifications
• Data sources (Jira, Azure DevOps, spreadsheets, APIs)
• Refresh cadence requirements
• Alert thresholds
• User access requirements

Proceso (Protocol)

1. Data source mapping — Identify all data sources for dashboard metrics
2. Feed configuration — Set up data extraction from each source
3. Transformation rules — Define data transformation and calculation rules
4. Widget selection — Choose visualization widgets per metric type
5. Layout implementation — Build dashboard layout per design
6. Refresh scheduling — Configure automatic refresh cadence
7. Alert configuration — Set up threshold-based alerts and notifications
8. Access control — Configure user access and permission levels
9. Testing — Verify data accuracy and refresh reliability
10. Maintenance plan — Document dashboard maintenance procedures

Edge Cases

1. Data source API unavailable: Design manual data upload pipeline with scheduled CSV exports. Document API requirements for future tool upgrade. [SUPUESTO]
2. Dashboard performance degrades with data volume: Implement data aggregation at source. Limit historical data to rolling window. Archive detailed data separately. [METRIC]
3. Alert fatigue from too many notifications: Calibrate thresholds using 2-cycle historical data. Implement tiered alerts (info, warning, critical). Allow user-level alert preferences. [PLAN]
4. Data accuracy discrepancy between dashboard and source: Implement reconciliation check. Display "last verified" timestamp. Design data validation rules at ingestion. [METRIC]

Example: Good vs Bad

Good Dashboard Tooling:

Attribute	Value
Data feeds configured	All metrics from automated sources
Refresh cadence	Configured per metric type (hourly/daily)
Alert rules	Tiered with calibrated thresholds
Access control	Role-based with 3 permission levels
Testing	Data accuracy verified against source
Maintenance plan	Documented with ownership

Bad Dashboard Tooling: A dashboard that requires someone to manually copy data from Jira into a spreadsheet every Friday. No automated feeds, no alerts, no refresh cadence. Fails because manual dashboards are always stale, error-prone, and abandoned within weeks when the person responsible gets busy.

Validation Gate

• Every dashboard metric has an identified data source with extraction method documented
• Automated data feeds configured for ≥80% of dashboard metrics
• Refresh cadence configured per metric type with documented schedule
• Alert thresholds calibrated using ≥1 cycle of historical data
• Access control defined with role-based permissions
• Data accuracy verified by comparing dashboard values against source system
• Maintenance plan documented with ownership and escalation for feed failures
• No manual data entry required for core dashboard metrics
• Users get timely, accurate data without manual effort [STAKEHOLDER]
• Tooling supports methodology-specific metrics and views [PLAN]

Escalation Triggers

• Data feed failures causing stale dashboards
• Dashboard performance degradation
• Data accuracy discrepancies detected
• Tool licensing issues affecting availability

Additional Resources

Resource	When to read	Location
Body of Knowledge	Before configuring to understand data integration patterns	references/body-of-knowledge.md
State of the Art	When evaluating dashboard platforms	references/state-of-the-art.md
Knowledge Graph	To link tooling to dashboard design and metrics	references/knowledge-graph.mmd
Use Case Prompts	When scoping tooling requirements	prompts/use-case-prompts.md
Metaprompts	To generate data feed configuration templates	prompts/metaprompts.md
Sample Output	To calibrate expected tooling documentation	examples/sample-output.md

Output Configuration

• Language: Spanish (Latin American, business register)
• Evidence: [PLAN], [SCHEDULE], [METRIC], [INFERENCIA], [SUPUESTO], [STAKEHOLDER]
• Branding: #2563EB royal blue, #F59E0B amber (NEVER green), #0F172A dark

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Sub-Agents

Data Source Connector

Data Source Connector Agent

Core Responsibility

Designs data source connections for dashboard feeds. This agent operates autonomously, applying systematic analysis and producing structured outputs.

Process

1. Gather Inputs. Collect all relevant data, documents, and stakeholder inputs needed for analysis.
2. Analyze Context. Assess the project context, methodology, phase, and constraints.
3. Apply Framework. Apply the appropriate analytical framework or model.
4. Generate Findings. Produce detailed findings with evidence tags and quantified impacts.
5. Validate Results. Cross-check findings against related artifacts for consistency.
6. Formulate Recommendations. Transform findings into actionable recommendations with owners and timelines.
7. Deliver Output. Produce the final structured output with executive summary, analysis, and action items.

Output Format

• Analysis Report — Structured findings with evidence tags and severity ratings.
• Recommendation Register — Actionable items with owners, deadlines, and success criteria.
• Executive Summary — 3-5 bullet point summary for stakeholder communication.

Refresh Automation Planner

Refresh Automation Planner Agent

Core Responsibility

Plans automated dashboard refresh and distribution. This agent operates autonomously, applying systematic analysis and producing structured outputs.

Process

1. Gather Inputs. Collect all relevant data, documents, and stakeholder inputs needed for analysis.
2. Analyze Context. Assess the project context, methodology, phase, and constraints.
3. Apply Framework. Apply the appropriate analytical framework or model.
4. Generate Findings. Produce detailed findings with evidence tags and quantified impacts.
5. Validate Results. Cross-check findings against related artifacts for consistency.
6. Formulate Recommendations. Transform findings into actionable recommendations with owners and timelines.
7. Deliver Output. Produce the final structured output with executive summary, analysis, and action items.

Output Format

• Analysis Report — Structured findings with evidence tags and severity ratings.
• Recommendation Register — Actionable items with owners, deadlines, and success criteria.
• Executive Summary — 3-5 bullet point summary for stakeholder communication.

Tool Evaluator

Tool Evaluator Agent

Core Responsibility

Evaluates dashboard tools against project requirements. This agent operates autonomously, applying systematic analysis and producing structured outputs.

Process

1. Gather Inputs. Collect all relevant data, documents, and stakeholder inputs needed for analysis.
2. Analyze Context. Assess the project context, methodology, phase, and constraints.
3. Apply Framework. Apply the appropriate analytical framework or model.
4. Generate Findings. Produce detailed findings with evidence tags and quantified impacts.
5. Validate Results. Cross-check findings against related artifacts for consistency.
6. Formulate Recommendations. Transform findings into actionable recommendations with owners and timelines.
7. Deliver Output. Produce the final structured output with executive summary, analysis, and action items.

Output Format

• Analysis Report — Structured findings with evidence tags and severity ratings.
• Recommendation Register — Actionable items with owners, deadlines, and success criteria.
• Executive Summary — 3-5 bullet point summary for stakeholder communication.

Visualization Designer

Visualization Designer Agent

Core Responsibility

Designs dashboard visualizations for different audiences. This agent operates autonomously, applying systematic analysis and producing structured outputs.

Process

1. Gather Inputs. Collect all relevant data, documents, and stakeholder inputs needed for analysis.
2. Analyze Context. Assess the project context, methodology, phase, and constraints.
3. Apply Framework. Apply the appropriate analytical framework or model.
4. Generate Findings. Produce detailed findings with evidence tags and quantified impacts.
5. Validate Results. Cross-check findings against related artifacts for consistency.
6. Formulate Recommendations. Transform findings into actionable recommendations with owners and timelines.
7. Deliver Output. Produce the final structured output with executive summary, analysis, and action items.

Output Format

• Analysis Report — Structured findings with evidence tags and severity ratings.
• Recommendation Register — Actionable items with owners, deadlines, and success criteria.
• Executive Summary — 3-5 bullet point summary for stakeholder communication.