Content Readiness Analyzer¶

Capability Slug: content_readiness_analyzer Plan Tier: Command Credit Cost: 5 / 10 / 15 credits per run (varies by analysis type) Category: Intelligence & Attribution Default mode: comprehensive Ready threshold: 70

Overview¶

Most Vellocity capabilities perform better when your inputs — brand voice, knowledge base, market definition — are complete. Content Readiness Analyzer scores those inputs against the same weighted algorithm the Co-Sell Partner Discovery & Matching capability uses for partner matching, but applied to self-assessment instead. It surfaces the gaps that would otherwise drag down every downstream capability that consumes brand context.

The capability is intentionally an inversion of co-sell matching. Same dimensions, same weights, different subject. If you're investing in better content generation or co-sell matching outputs and want to know what's holding the inputs back, this is the diagnostic.

The three weighted dimensions¶

Weight	Dimension	What it measures
40%	ICP Completeness	Target audience, buyer personas, firmographics, industry definition
35%	Product Clarity	Knowledge base coverage, value propositions, competitive differentiators, website enrichment
25%	Market Definition	Target keywords, GTM goals, market positioning

Sub-fields within each dimension are scored deterministically (not by AI), and missing fields come with explicit recommendations and impact projections.

ICP Completeness — 100-point breakdown (40% weight)¶

Sub-field	Points	Full credit when...
Industry defined	25	`company.industry` is set
Target audience defined	25	`company.target_audience` is set
Key personas	25	Personas defined on the company record
Buyer journey stages	25	Buyer journey stages defined

Missing fields return entries like:

{
  "industry": {
    "status": "missing",
    "recommendation": "Define your industry vertical for better content targeting",
    "impact": "Improves co-sell matching by up to 25%"
  }
}

Product Clarity — 100-point breakdown (35% weight)¶

Sub-field	Points	Full credit when...	Partial credit when...
Knowledge base documents	30	≥ 5 docs uploaded	2-4 docs (15 pts)
Value propositions	25	≥ 3 defined	1-2 defined (12 pts)
Competitive differentiators	25	≥ 2 defined	1 defined (12 pts)
Website enrichment	20	Fresh and not stale	Stale enrichment (10 pts)

The knowledge-base threshold of 5 documents is intentional — that's the minimum where RAG-based content generation produces consistently grounded, accurate output. Below that, capabilities that pull from the KB tend to generic-out.

Market Definition — 100-point breakdown (25% weight)¶

Sub-field	Points	Full credit when...
Target keywords	~33	Keywords defined on the company
GTM goals	~33	GTM goals set
Market positioning	~33	Market positioning statement defined

These drive search-relevance scoring in other capabilities (notably SEO Intelligence and RSS Feed Intelligence Scorer) and signal how clearly you've articulated where you play.

The four status bands¶

Status	Threshold
`ready`	Overall score ≥ 85 and zero high-priority gaps
`mostly_ready`	Overall score ≥ 70 (the `READY_THRESHOLD`)
`needs_improvement`	Overall score ≥ 50
`not_ready`	Below 50

Status is a deterministic banding plus a high-priority-gap check — not an AI judgment. The thresholds are constants in the capability and stable across runs.

The three analysis types¶

Type	Credits	What it adds
`quick`	5	Deterministic scan across all three dimensions; baseline scores, status, gap list, prioritized actions, impact summary
`comprehensive` (default)	10	Same as quick, with full per-field details and recommendations populated for every missing field
`deep_with_recommendations`	15	Same as comprehensive, plus AI-generated next-step recommendations from Claude — but only when the overall score is below 70. Above the threshold, the deep mode behaves like comprehensive (no need to add AI guidance to a passing score).

The "AI only when needed" pattern in deep_with_recommendations is intentional — it's a credit-conscious default that gives the model nothing to do when there's nothing to fix.

What gets returned¶

{
  "success": true,
  "credits_used": 10,
  "overall_readiness_score": 76,
  "status": "mostly_ready",
  "dimension_scores": {
    "icp_completeness": {
      "score": 75,
      "weight": "40%",
      "weighted_contribution": 30,
      "details": {
        "industry": { "status": "complete", "value": "..." },
        "target_audience": { "status": "complete", "value": "..." },
        "key_personas": {
          "status": "missing",
          "recommendation": "...",
          "impact": "..."
        },
        "buyer_journey": { "status": "complete", "value": "..." }
      }
    },
    "product_clarity": { /* same shape */ },
    "market_definition": { /* same shape */ }
  },
  "gaps": [
    {
      "field": "key_personas",
      "dimension": "icp_completeness",
      "priority": "high|medium|low",
      "recommendation": "...",
      "impact": "..."
    }
  ],
  "next_actions": [ /* prioritized version of gaps */ ],
  "impact_summary": {
    "potential_score_gain": 10,
    "improvement_message": "..."
  },
  "ai_recommendations": { /* only on deep_with_recommendations + score < 70 */ }
}

gaps vs next_actions — gaps is the raw list of missing fields with priority. next_actions is the same data sorted by priority and dimension weight so the most-impactful actions appear first.

Required and optional parameters¶

Parameter	Required	Description
`company_id`	Yes	The company record to score
`analysis_type`	No	`quick` (5 cr), `comprehensive` (10 cr, default), or `deep_with_recommendations` (15 cr)
`include_partner_context`	No	Default `false`. When `true`, looks up the associated `Partner` record on the same team and folds partner-side data into the ICP and market scores. Useful when the company has a paired partner profile.

When to use it¶

Scenario	Recommended mode
Onboarding gate — "is this account ready to generate AI content?"	`quick`
Quarterly readiness review	`comprehensive`
Diagnosing why content generation or co-sell matching is producing weak output	`deep_with_recommendations`
Setting up a new partner profile and want a checklist	`comprehensive` (the gaps array doubles as a setup checklist)
Pre-launch check before kicking off a major GTM motion	`deep_with_recommendations`

It is not:

A continuous monitoring system. Each run is a discrete snapshot. Run it on a schedule if you want trend data.
A scoring model for content quality. For "is this listing or article good?", use Marketplace Listing Optimizer (marketplace_listing_optimizer) or Predict Marketplace Performance (predict_performance) — different question, different tool.
A substitute for Assess Launch Readiness. That one scores a specific listing version against blocking issues; this one scores the underlying brand and KB inputs that feed into every listing.

Composing with other capabilities¶

The same dimension weights show up across multiple capabilities. Improving readiness here lifts scoring quality everywhere those weights apply.

Capability	How it benefits
Co-Sell Partner Discovery & Matching (`cosell_partner_matching`)	Same weighted algorithm — better self-completeness directly improves match quality
RSS Feed Intelligence Scorer (`rss_post_scoring`)	Cross-pollinated dimension weights — better readiness lifts VIS scores on every post
AWS Innovation Monitor (`aws_innovation_monitor`)	Uses brand profile for relevance scoring against AWS announcements
Generate Text Content (`generate_text`)	Pulls from the same brand-voice context — completeness drives output quality
AWS Blog Co-Author (`aws_blog_co_author`)	KB grounding (the 5-doc threshold here is also the threshold there)

API access¶

Exposed through the agent runtime as content_readiness_analyzer. Partner API keys scoped with view_companies plus the agent capability slug can run all three analysis modes. The deterministic-scan modes (quick, comprehensive) don't require Claude access; only deep_with_recommendations invokes Bedrock and only when the score is below the ready threshold. See Partner API Capabilities.