Cf Analytics

Content Analytics Dashboard

Track ContentForge production quality, pipeline timing, brand-specific patterns, and compliance trends over configurable time periods with automated insights and alert flags.

When to Use

Use /contentforge:cf-analytics when you need:

• Quality trend visibility — Are scores improving or declining over time?
• Pipeline performance audit — Which phases are slowest? Where are bottlenecks?
• Brand comparison — Which brands consistently score highest/lowest?
• Content type analysis — Are articles scoring better than whitepapers?
• Compliance monitoring — Citation rates, brand adherence, loop frequency
• Capacity planning — Average throughput for estimating batch timelines

For real-time batch monitoring, use the Progress Tracker (built into /contentforge:batch-process). For individual content production, use /contentforge:create-content.

What This Command Does

Loads historical production data from the brand's configured tracking backend (Google Sheets, Airtable, or local — see tracking.backend in the brand profile), calculates aggregate metrics across configurable dimensions, identifies statistical outliers and concerning trends, generates an ASCII dashboard with actionable recommendations, and flags alerts when performance degrades.

Process Flow:

1. Load Data — Read tracking records from the brand's tracking backend (Google Sheets / Airtable / local JSON)
2. Filter & Parse — Apply time period, brand, content type, and metric focus filters
3. Calculate Aggregates — Average scores, trends, percentiles, phase timing breakdowns
4. Detect Outliers — Flag data points beyond 2.0 standard deviations from mean
5. Generate Insights — Identify patterns, correlations, and improvement opportunities
6. Present Dashboard — Render ASCII analytics display with charts and recommendations
7. Alert Check — Evaluate alert rules and surface any triggered flags

Required Inputs

Optional (all have defaults):

• Time Period — 7 | 30 | 90 days (default: 30)
• Brand Filter — Filter to specific brand (default: all brands)
• Content Type Filter — article | blog | whitepaper | faq | research_paper | video_script | case_study | newsletter (default: all types)
• Metric Focus — quality | timing | compliance | citations (default: quality)

How to Use

Default Dashboard (Last 30 Days, All Brands)

/contentforge:cf-analytics

Specific Time Period

/contentforge:cf-analytics --period=90

Brand-Specific Analysis

/contentforge:cf-analytics --brand=AcmeMed --period=30

Content Type Focus

/contentforge:cf-analytics --type=whitepaper --period=90

Metric-Specific Deep Dive

/contentforge:cf-analytics --focus=timing --period=30

Combined Filters

/contentforge:cf-analytics --brand=AcmeMed --type=article --focus=quality --period=90

Data Sources

Data source: the brand's tracking backend

ContentForge's Output Manager (Phase 8) logs every completed piece to the backend configured in the brand profile (tracking.backend):

• google_sheets — rows in the configured Google Sheet (read via scripts/sheets-tracker.py)
• airtable — records in the configured Airtable base (read via scripts/airtable-tracker.py)
• local — tracking.json under ~/.claude-marketing/{brand-slug}/tracking/ (read via scripts/local-tracker.py)

All three backends share the same record schema:

Default when no cloud backend is configured

The local backend is the default: tracking data lives at

~/.claude-marketing/{brand-slug}/tracking/tracking.json

Switch backends anytime with /contentforge:cf-switch-backend (migration is additive and idempotent).

What Happens

Step 1: Data Loading (5-10 seconds)

Loading analytics data...
Source: <brand's tracking backend, e.g. Airtable base appXXXX / Google Sheet / local tracking.json>
Records found: 147 total
After filters: 42 records (last 30 days, all brands)
Date range: 2026-01-26 to 2026-02-25

Pipeline telemetry (v4.0). When a brand filter is active, also load the cross-run telemetry so the dashboard can show where the pipeline itself works hard for this brand:

python ${CLAUDE_PLUGIN_ROOT}/scripts/telemetry.py loops --brand <slug>
python ${CLAUDE_PLUGIN_ROOT}/scripts/telemetry.py patterns --brand <slug>

Render two additional panels from the output: Loop edges fired (by edge and content type — an edge firing across many runs of one content type is a contract problem worth a template fix, and the panel should say so) and Recurring humanizer patterns (per-pattern totals and run-presence; runs the script reports as not_instrumented are shown as "unknown (pre-4.0 run)", never as zero). Skip both panels silently only when the brand has no runs at all.

Step 2: Aggregate Calculation

Quality Metrics:

• Mean, median, min, max for composite score and each dimension
• Standard deviation for outlier detection
• Trend direction (improving, stable, declining) via linear regression slope
• Percentile distribution (P25, P50, P75, P90)

Timing Metrics:

• Average total processing time by content type
• Phase-by-phase timing breakdown (mean per phase)
• Slowest phase identification
• Comparison against benchmarks from config/analytics-config.json

Compliance Metrics:

• Average citations per piece
• Citation density (citations per 300 words)
• Average loops per piece
• Loop-free completion rate (% of pieces that passed on first review)
• Brand compliance dimension average

Trend Metrics:

• Rolling 7-day average quality score
• Week-over-week quality change
• Content volume by week

Step 3: Outlier Detection

Flag any record where:

• Quality score is >2.0 standard deviations below the mean
• Processing time is >1.5x the benchmark for its content type
• Loops used >3 (suggests requirement or pipeline issues)
• Any dimension score <5.0 (below minimum pass threshold)

Step 4: Insight Generation

Analyze patterns across the dataset:

• Correlation Analysis: Do longer processing times correlate with higher quality?
• Brand Patterns: Which brands have the most consistent scores?
• Type Patterns: Which content types have the highest loop frequency?
• Phase Bottlenecks: Which phase consumes the most time relative to benchmark?
• Improvement Trajectory: Is the system getting better over time?

Step 5: Dashboard Rendering

Output: Analytics Dashboard

Full Dashboard (Default View)

Before rendering, read references/dashboard-examples.md (in this skill's directory), section "Full Dashboard (Default View)", for the full synthetic layout (quality score overview, weekly trend chart, phase timing breakdown, brand performance comparison, content type averages, feedback loop analysis, alerts, recommendations) — reproduce this shape with real computed values. All numbers in the reference are invented.

Timing-Focused Dashboard (--focus=timing)

Read references/dashboard-examples.md (in this skill's directory), section "Timing-Focused Dashboard (--focus=timing)", for the full synthetic layout (processing-time distribution, time by content type, phase waterfall, bottleneck analysis, throughput metrics) — reproduce this shape with real computed values.

Compliance-Focused Dashboard (--focus=compliance)

Read references/dashboard-examples.md (in this skill's directory), section "Compliance-Focused Dashboard (--focus=compliance)", for the full synthetic layout (citation compliance, brand compliance scores, feedback loop compliance, hallucination report) — reproduce this shape with real computed values.

Alert Rules

Alerts are configured in config/analytics-config.json and trigger when:

Configuration

Analytics behavior is controlled by config/analytics-config.json:

• Quality thresholds (excellent, good, acceptable, needs_review)
• Timing benchmarks per content type
• Alert rule conditions
• Trend analysis parameters (window, min data points, outlier threshold)
• Dashboard defaults (time period, charts to display)
• Score component weights

See config/analytics-config.json for full configuration.

Data Privacy

• Analytics operates on aggregate metrics only — no content text is stored or displayed
• Tracking data includes scores, timing, and metadata — never the content body
• All data stays within your configured tracking backend — no external transmission

Limitations

• Requires at least 10 data points for meaningful trend analysis (30+ recommended)
• Trend direction (improving/declining) is based on linear regression and can be misleading with high variance
• Phase timing accuracy depends on ContentForge logging completeness
• Cannot retroactively analyze content produced before tracking was enabled
• Cross-session persistence follows the tracking backend: local JSON persists on the host filesystem; Google Sheets and Airtable persist in the cloud (and support team access)

Agents Used

None. This skill operates entirely on tracked data — no content generation agents are invoked. It reads records written by the Output Manager (Phase 8) to the brand's tracking backend. The aggregation and trend logic is documented in utilities/analytics-tracker.md — a pseudocode reference doc (not a script); follow it for the calculations.

Integration with Other Skills

Data Sources:

• /contentforge:create-content — Each completed piece adds a tracking record
• /contentforge:batch-process — Batch completions add multiple records
• /contentforge:content-refresh — Refresh completions add versioned records

Acts On Insights:

• Quality decline detected: Review brand profile, run /contentforge:brand-setup refresh
• Timing bottleneck found: Adjust phase configuration in config/scoring-thresholds.json
• Citation drop flagged: Update Phase 3 citation density targets

Related Skills

• /contentforge:create-content — Full content production pipeline (generates tracking data)
• /contentforge:batch-process — Sequential, checkpointed batch processing (generates batch tracking data)
• /contentforge:content-refresh — Content updates (generates refresh tracking data)
• /contentforge:cf-variants — A/B test variation generation
• /contentforge:cf-switch-backend — Change where tracking data lives

Agents: None (data analysis only; pseudocode reference: utilities/analytics-tracker.md) Output: ASCII analytics dashboard with trends, comparisons, alerts, and recommendations