| name | cf-analytics |
| description | Render an ASCII analytics dashboard of ContentForge production history — quality score trends, phase-by-phase pipeline timing, brand and content-type comparisons, compliance and citation metrics, outlier detection, and triggered alerts over a 7/30/90-day window. Triggers on "/contentforge:cf-analytics", "are our quality scores improving", "which pipeline phase is slowest", "show content production stats", "compare brands by quality". Reads tracking records written by Phase 8 to the brand's tracking backend (local JSON, Google Sheets, or Airtable) and alert rules from config/analytics-config.json; needs 10+ tracked pieces for meaningful trends. Analysis only — invokes no agents and never reads or stores content text. |
| effort | low |
| argument-hint | [--period 7|30|90] [--brand <name>] [--type <content-type>] [--focus quality|timing|compliance|citations] |
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:
- Load Data — Read tracking records from the brand's tracking backend (Google Sheets / Airtable / local JSON)
- Filter & Parse — Apply time period, brand, content type, and metric focus filters
- Calculate Aggregates — Average scores, trends, percentiles, phase timing breakdowns
- Detect Outliers — Flag data points beyond 2.0 standard deviations from mean
- Generate Insights — Identify patterns, correlations, and improvement opportunities
- Present Dashboard — Render ASCII analytics display with charts and recommendations
- 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:
| Column | Type | Description |
|---|
| requirement_id | string | Unique content ID (REQ-001) |
| title | string | Content title |
| brand | string | Brand profile used |
| content_type | enum | article, blog, whitepaper, faq, research_paper, video_script, case_study, newsletter |
| word_count | integer | Final word count |
| quality_score | float | Composite score (0-10) |
| content_quality | float | Dimension score (0-10) |
| citation_integrity | float | Dimension score (0-10) |
| brand_compliance | float | Dimension score (0-10) |
| seo_performance | float | Dimension score (0-10) |
| readability | float | Dimension score (0-10) |
| processing_time_min | float | Total pipeline time in minutes |
| phase_1_time | float | Research phase duration |
| phase_2_time | float | Fact-check phase duration |
| phase_3_time | float | Drafting phase duration |
| phase_4_time | float | Validation phase duration |
| phase_5_time | float | Structuring phase duration |
| phase_6_time | float | SEO phase duration |
| phase_6_5_time | float | Humanizer phase duration |
| phase_7_time | float | Reviewer phase duration |
| phase_8_time | float | Output phase duration |
| loops_used | integer | Total feedback loops triggered |
| loop_details | string | Which loops fired (e.g., "P4>P3 x1, P7>P5 x1") |
| citations_count | integer | Number of citations in final output |
| broken_links | integer | Broken links detected (should be 0) |
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:
| Alert | Condition | Severity |
|---|
| Quality Decline | 3 consecutive pieces from same brand score <7.0 | High |
| Phase Slowdown | Any phase averages >1.5x its benchmark time | Medium |
| Citation Drop | Citation density drops below content-type minimum | Medium |
| Loop Spike | Average loops/piece exceeds 2.0 for any content type | High |
| Score Floor | Any piece scores below 5.0 composite | Critical |
| Volume Gap | Fewer than 10 data points in analysis window | Info |
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
Agents: None (data analysis only; pseudocode reference: utilities/analytics-tracker.md)
Output: ASCII analytics dashboard with trends, comparisons, alerts, and recommendations
