Transforms metrics and findings into meaningful narratives — insight extraction, metrics-to-meaning conversion, comparison framing, and magnitude communication. [EXPLICIT] Use when presenting scoring matrices, coverage metrics, performance data, cost estimates, or any quantitative finding that needs interpretation and context. [EXPLICIT]
From jm-adknpx claudepluginhub javimontano/jm-adk-alfaThis skill is limited to using the following tools:
agents/chart-selector.mdagents/insight-extractor.mdagents/narrative-builder.mdagents/visualization-designer.mdevals/evals.jsonexamples/README.mdexamples/sample-output.htmlexamples/sample-output.mdprompts/metaprompts.mdprompts/use-case-prompts.mdreferences/body-of-knowledge.mdreferences/knowledge-graph.mmdreferences/state-of-the-art.mdTransforms raw metrics, scores, and quantitative findings into meaningful narratives that drive understanding and action. Owns insight extraction, comparison framing, magnitude communication, and the bridge between numbers and decisions. [EXPLICIT]
A number without context is noise. A number with context, comparison, and consequence is an insight. 92% test coverage means nothing until we know that the uncovered 8% concentrates the payment modules — exactly where risk is highest. Data metodologia-storytelling turns metrics into comprehension.
$1 — Data context: metrics, scoring, financial, performance, coverage (default: metrics)$2 — Audience: executive, technical, mixed (default: mixed)Parse from $ARGUMENTS. [EXPLICIT]
Raw metric → Context → Comparison → Insight → Implication → Action
Example:
Raw: "Deployment frequency: 1/month"
Context: "El equipo despliega una vez al mes"
Comparison: "vs. benchmark DORA de equipos elite: múltiples por día"
Insight: "La brecha de 30x indica proceso manual o miedo al cambio"
Implication: "Cada feature espera en promedio 15 días de cola antes de llegar a producción"
Action: "Pipeline CI/CD automatizado puede cerrar la brecha a 1/semana en 3 sprints"
Data point → Pattern → Anomaly → Significance → Recommendation
Steps:
1. Observe the data point: "8 de 12 módulos tienen cobertura >90%"
2. Detect the pattern: "Los módulos con alta cobertura comparten equipo senior"
3. Identify the anomaly: "Los 4 módulos sin cobertura son todos del equipo junior"
4. Interpret the significance: "No es un problema de herramientas, es de capacitación"
5. Recommend: "Pair programming cross-team + coverage gates en CI"
| Frame Type | When | Example |
|---|---|---|
| Before/After | Projected improvement | "De 12 semanas a 4 semanas de time-to-market" |
| Peer Benchmark | Industry comparison | "vs. mediana del sector: 3 deploys/semana" |
| Industry Standard | Reference frameworks | "DORA elite: <1 hora lead time" |
| Internal Baseline | Historical comparison | "vs. Q1: incidentes reducidos 40%" |
| Target Gap | Distance to objective | "A 15 puntos del objetivo de disponibilidad 99.9%" |
| Cost Equivalence | Making FTE tangible | "Equivalente a 3 desarrolladores senior durante 6 meses" |
Abstract → Concrete → Impactful
"40 FTE-meses"
→ "Equivalente a un equipo de 8 personas durante 5 meses"
→ "Es decir, todo el equipo backend dedicado exclusivamente
desde enero hasta mayo, sin poder hacer nada más"
"99.5% disponibilidad"
→ "43 horas de downtime al año"
→ "Equivalente a casi 2 días completos sin servicio,
probablemente concentrados en momentos de alta demanda"
"$2M de deuda técnica" → NEVER. Use FTE-month equivalents. [EXPLICIT]
When presenting scoring tables:
1. Lead with the pattern, not individual scores:
"De las 6 dimensiones evaluadas, 2 están en rojo y comparten causa raíz:
acoplamiento entre el módulo de autenticación y el core de negocio."
2. Highlight the anomalies:
"La dimensión de seguridad sorprende en verde dado que el equipo
no tiene un rol dedicado — evidencia de buenas prácticas orgánicas."
3. Connect to action:
"Los 2 rojos se resuelven con el escenario B en Fase 1 (Q2);
los 3 amarillos mejoran orgánicamente con la nueva arquitectura."
For multi-chart metodologia-storytelling (presentations, executive summaries):
Chart 1: The headline
"Aquí estamos" — current state summary metric
Chart 2: The context
"Así llegamos aquí" — trend or historical view
Chart 3: The comparison
"Así estamos vs. donde deberíamos estar" — benchmark gap
Chart 4: The path
"Así cerramos la brecha" — roadmap or scenario projection
Each chart builds on the previous. No standalone charts. [EXPLICIT]
| Type | Guideline |
|---|---|
| Table footnotes | Explain methodology, not data (data goes in cells) |
| Semaphore criteria | Define thresholds: >80%, 50-80%, <50% |
| Cross-references | "→ See 03_AS-IS § Cobertura for methodology" |
| Source attribution | Evidence tag inline: "92% cobertura [CÓDIGO]" |
| Criterion | Check |
|---|---|
| Every metric has context | Not just the number — the story around it |
| Every metric has comparison | vs. baseline, benchmark, target, or prior period |
| Insights are actionable | "So what?" answered for every data point |
| Magnitudes are tangible | FTE-months translated to team-equivalents |
| Scoring patterns highlighted | Not just individual scores — the story across dimensions |
| No naked numbers | Zero metrics without interpretation |
| Edge Case | Handling Strategy |
|---|---|
| No industry benchmarks available | Use internal baseline (prior quarter, other team, other project). Declare explicitly: "No industry benchmark available; internal Q1 baseline used as reference [SUPUESTO]". If no internal baseline either, use standard frameworks (DORA, SRE). |
| Contradictory metrics | Present the contradiction as a finding itself. "High coverage (92%) contradicts incident rate (8/month), suggesting tests that do not cover real scenarios [INFERENCIA]". The contradiction IS the story. |
| Scarce data (<10 data points) | Acknowledge limitation explicitly: "With [N] data points, the trend is indicative, not conclusive". Use confidence intervals. Recommend collection period before definitive conclusions. |
| Metrics that favor inaction (everything green) | Look for the story beneath the surface: trends, degradation velocity, opportunity cost. "Everything is green today, but the trend over the last 3 quarters shows..." |
| Decision | Justification | Discarded Alternative |
|---|---|---|
| Context before number as rule | A number without reference is noise. The reader cannot evaluate "92% coverage" without knowing the target, baseline, or benchmark. | Number first: the reader forms premature judgment before having a reference frame. |
| Mandatory comparison in every metric | Every metric needs at least one reference: vs baseline, vs industry, vs target, vs prior quarter. Without comparison there is no insight. | Isolated metric: informative but not actionable; the reader does not know if it is good or bad. |
| Tangible magnitudes over abstract | "40 FTE-months" means nothing to a CEO. "The entire backend team dedicated from January to May doing nothing else" generates visceral understanding. | Abstract magnitudes: precise but not communicative for executive audience. |
| Narrative sequence in dashboards (4 charts) | Each chart builds on the previous: status -> trend -> benchmark -> path. Without sequence, charts are isolated data. | Independent charts: flexible but do not build cumulative argument. |
graph TD
subgraph Core["Core: Data Storytelling"]
M2M[Metrics-to-Meaning]
INSIGHT[Insight Extraction]
COMPARE[Comparison Framing]
MAGNITUDE[Magnitude Communication]
end
subgraph Inputs["Inputs"]
METRICS[Metricas Crudas]
SCORES[Scoring Matrices]
BENCHMARKS[Benchmarks]
CONTEXT[Contexto de Negocio]
end
subgraph Outputs["Outputs"]
NARRATIVE[Metric Narratives]
DASHBOARD[Dashboard Sequences]
SCORING_N[Scoring Narratives]
PROJECTION[Projections]
end
subgraph Related["Related Skills"]
STORY[storytelling]
COPY[copywriting]
DATAVIZ[data-viz-storytelling]
TECHWRITE[technical-writing]
end
METRICS --> M2M
SCORES --> INSIGHT
BENCHMARKS --> COMPARE
CONTEXT --> MAGNITUDE
M2M --> NARRATIVE
INSIGHT --> SCORING_N
COMPARE --> DASHBOARD
MAGNITUDE --> PROJECTION
STORY --> Core
COPY --> NARRATIVE
Core --> DATAVIZ
TECHWRITE --> METRICS
Filename: Data_Narrative_{project}_{dimension}_{WIP|Aprobado}.md
# Narrativa de Datos: {project} - {dimension}
## Headline
{Una metrica clave con contexto y comparacion en una linea}
## Estado Actual
| Metrica | Valor | Baseline | Benchmark | Gap | Tendencia |
|---|---|---|---|---|---|
## Interpretacion
{Parrafo denso: patron detectado + anomalia + significancia}
## Implicacion
{So what? Que significa para el negocio en terminos tangibles}
## Recomendacion
{Accion concreta que cierra el gap, con timeline estimado}
## Fuentes
| Dato | Tag de Evidencia | Confianza |
|---|---|---|
Data_Narrative_{project}_{dimension}_{WIP}.html{fase}_{entregable}_{cliente}_{WIP}.docx{fase}_{entregable}_{cliente}_{WIP}.xlsx{fase}_{entregable}_{cliente}_{WIP}.pptxFilename: Scoring_Narrative_{project}_{WIP|Aprobado}.md
# Scoring Narrative: {project}
## Patron General
{De las N dimensiones evaluadas, X estan en rojo y comparten causa raiz: ...}
## Scoring Matrix
| Dimension | Score | Semaforo | Evidencia Clave | Causa Raiz |
|---|---|---|---|---|
## Anomalias
{Dimensiones que sorprenden -- positiva o negativamente -- con explicacion}
## Conexion a Accion
{Los rojos se resuelven con [escenario] en [fase]; los amarillos mejoran con...}
## Proyeccion
{Si no se actua: tendencia de scores en N trimestres}
| Dimension | Peso | Criterio |
|---|---|---|
| Trigger Accuracy | 10% | Se activa ante metricas, scores, datos cuantitativos que requieren interpretacion y contexto |
| Completeness | 25% | Toda metrica tiene contexto, comparacion, interpretacion, implicacion, y recomendacion |
| Clarity | 20% | Magnitudes tangibles; secuencia logica dato -> insight -> accion; cero numeros desnudos |
| Robustness | 20% | Produce narrativas utiles sin benchmarks, con datos escasos, con metricas contradictorias |
| Efficiency | 10% | Genera narrativa completa por metrica sin requerir multiples iteraciones |
| Value Density | 15% | Cada metrica interpretada genera insight accionable; ratio signal-to-noise alto |
Umbral minimo: 7/10
metodologia-storytelling — Arco narrativo general que consume las narrativas de datosmetodologia-copywriting — Prosa persuasiva que envuelve los insights de datosmetodologia-data-viz-storytelling — Visualizaciones que representan las narrativas de datosmetodologia-technical-writing — Precision documental de las metricas fuenteExample invocations:
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