/cf-analytics

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`](../../commands/create-content.md).

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:

| 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) | | completed_at | datetime | Completion tim