Skill: distribution-analysis

Purpose

Distribution is the most underestimated factor in indie app success. A mediocre product with great distribution beats a great product with no distribution. This skill evaluates all realistic paths to users and adapts its verdict to the founder's tier — a channel that works for a growth-stage operator can be a trap for a beginner.

Input

• Idea slug
• memory/user_profile.md (ICP tier, distribution advantages, budget constraint)
• memory/ideas/<slug>/idea.md (app concept, key features, differentiator)
• Optional: memory/ideas/<slug>/competitors.json (competitor distribution signals)

Distribution Dimensions

Process

Step 1 — Viral Coefficient Estimation

The viral coefficient (k-factor) predicts whether an app can grow organically through user referrals. Estimate k = i × c where:

• i = average number of invitations/shares per user
• c = conversion rate of each invitation

Viral loop identification

Evaluate the app concept against these loop types:

Estimation rubric

1. Identify which loop type(s) apply to the app concept.
2. Estimate i (invitations per user) — consider: does the core UX prompt sharing? How often? To how many people?
3. Estimate c (conversion per invitation) — consider: how compelling is the share artifact? Does the recipient need the app to view it?
4. Compute k = i × c.
5. Classify:

k ≥ 1.0 means every user brings in at least one more user on average — true exponential growth. This is rare for indie apps; be skeptical of estimates above 0.8 unless the app has an inherent or content-as-distribution loop.

Step 2 — ASO Potential Scoring

App Store Optimization is the highest-leverage free channel for indie developers. Score ASO opportunity on a 3-tier rubric:

ASO scoring rubric

ASO score: Sum of all factors (5–15 points).

Featured potential checklist

An app has App Store featured potential if it meets 3+ of these 5 criteria:

1. Uses a newly released Apple/Google platform feature (widgets, Live Activities, visionOS, AI APIs)
2. Has exceptional design quality (would look good in an editorial story)
3. Serves an underrepresented audience or emerging cultural moment
4. Has a clear positive-impact or wellness angle
5. Is a premium/indie app (Apple editorially favors paid apps and small teams)

Step 3 — Creator Economy Fit Assessment

Evaluate whether influencers and creators can authentically promote the app. Not all apps are "creator-friendly" — forcing influencer marketing on a utility app wastes money.

Creator fit criteria

Scoring: Count High/Medium/Low across all 5 factors.

• high fit: 3+ factors scored High
• medium fit: 2 factors High, or 3+ Medium
• low fit: 2+ factors Low, or no factors High

Step 4 — Paid Channel Feasibility

Assess whether paid acquisition can work within indie budget constraints.

If budget_constraint from user profile is "low", cap paid feasibility at "marginal" regardless of other factors — the user cannot sustain the learning curve of paid acquisition.

Step 5 — Founder Distribution Edge

Cross-reference user_profile.md to identify whether the founder has a pre-existing distribution advantage:

Step 6 — Distribution Verdict

Compute the overall verdict by evaluating all dimensions together, then adjust for founder tier.

Raw verdict logic

Tier adjustment

The same distribution profile means different things to different founders. Apply this adjustment:

If user_profile.md is unavailable, skip tier adjustment and note it as a gap.

Output

Write to memory/ideas/<slug>/distribution.json:

{
  "organic_reach_potential": "high | medium | low",
  "viral_loop_exists": false,
  "viral_loop_type": "inherent | collaborative | word-of-mouth | incentivized | content-as-distribution | none",
  "viral_loop_description": "",
  "k_factor_estimate": 0.0,
  "k_factor_classification": "viral-growth-engine | viral-assist | marginal | non-viral",
  "paid_feasibility": "viable | marginal | not-viable",
  "minimum_paid_budget_monthly": 0,
  "paid_feasibility_rationale": "",
  "platform_advantage": {
    "aso_opportunity": "high | medium | low",
    "aso_score_breakdown": {
      "category_competition": 0,
      "keyword_opportunity": 0,
      "search_intent_match": 0,
      "review_velocity_potential": 0,
      "visual_differentiation": 0,
      "total": 0
    },
    "featured_potential": false,
    "featured_criteria_met": []
  },
  "creator_economy_fit": "high | medium | low",
  "creator_fit_rationale": "",
  "creator_fit_breakdown": {
    "content_generation": "high | medium | low",
    "audience_alignment": "high | medium | low",
    "demo_ability": "high | medium | low",
    "authenticity": "high | medium | low",
    "affiliate_fit": "high | medium | low"
  },
  "user_distribution_advantage": "",
  "user_advantage_type": "audience | community | content-skills | seo-aso | relationships | none",
  "recommended_first_channel": "",
  "recommended_first_channel_rationale": "",
  "channels_ranked": [
    { "channel": "", "viability": "high | medium | low", "time_to_first_100_users": "" }
  ],
  "distribution_verdict": "strong | moderate | weak",
  "tier_adjustment_applied": "",
  "distribution_verdict_rationale": ""
}

Notes

• The recommended_first_channel should always be the highest-viability channel the founder can realistically execute given their tier. Don't recommend "TikTok organic" to someone who has never made a video; don't recommend "ASO" to someone who doesn't know what keywords are.
• If competitors.json is available, check competitor distribution strategies — an app succeeding via a channel the founder can replicate is a strong positive signal.
• k-factor estimates are inherently speculative pre-launch. Treat them as directional, not precise. Flag any estimate above 0.5 as "optimistic until validated."