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
| Dimension |
Questions to Answer |
| Organic reach |
Can this spread without paid spend? Is there a viral loop? What's the estimated viral coefficient? |
| Paid feasibility |
Can paid ads break even at indie scale? What's the minimum viable budget? |
| Platform advantage |
Is there an ASO moat? App Store featured potential? Category competitiveness? |
| Creator economy fit |
Can influencers or creators promote this authentically? Does the app produce shareable output? |
| User's distribution edge |
Does the user have an existing audience, community, or channel expertise? |
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:
| Loop type |
Description |
Typical k-factor |
Example |
| Inherent |
Product is useless alone, requires inviting others |
0.5–1.5 |
Multiplayer games, shared lists |
| Collaborative |
Better with others but works solo |
0.2–0.6 |
Workout trackers with friends, shared budgets |
| Word-of-mouth |
Users talk about it because it's remarkable |
0.1–0.4 |
Apps that produce "wow" output (AI art, unique insights) |
| Incentivized |
Users get a reward for referring |
0.1–0.3 |
Referral credits, unlocked features |
| Content-as-distribution |
App output is inherently shareable on social platforms |
0.3–0.8 |
Photo editors with watermarks, personality quizzes, wrapped/recap screens |
| None |
No natural reason to share |
0.0–0.05 |
Utility apps (calculators, timers) |
Estimation rubric
- Identify which loop type(s) apply to the app concept.
- Estimate i (invitations per user) — consider: does the core UX prompt sharing? How often? To how many people?
- Estimate c (conversion per invitation) — consider: how compelling is the share artifact? Does the recipient need the app to view it?
- Compute k = i × c.
- Classify:
| k-factor |
Classification |
| k ≥ 0.7 |
Viral growth engine — organic growth is a primary acquisition channel |
| 0.3 ≤ k < 0.7 |
Viral assist — referrals supplement other channels meaningfully |
| 0.1 ≤ k < 0.3 |
Marginal virality — some word-of-mouth, not a growth driver |
| k < 0.1 |
Non-viral — growth depends entirely on other channels |
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
| Factor |
High (3 pts) |
Medium (2 pts) |
Low (1 pt) |
| Category competition |
Niche category, top 10 achievable with <500 ratings |
Moderate category, top 50 achievable |
Saturated category, dominated by incumbents with 100K+ ratings |
| Keyword opportunity |
High-volume keywords with low-rated top results (< 4.2 stars, < 1K ratings) |
Keywords exist but top results are solid (4.5+ stars) |
All relevant keywords dominated by well-known brands |
| Search intent match |
Users actively search for this exact solution (tool/utility intent) |
Users search for the category but not this specific angle |
Discovery-dependent — users don't know they want this |
| Review velocity potential |
App has natural prompt moments for asking reviews (completed task, achievement) |
Some prompt moments but not in core loop |
No natural review prompt; must interrupt to ask |
| Visual differentiation |
App icon and screenshots can stand out (unique aesthetic, bold output previews) |
Decent but similar to competitors |
Looks like every other app in the category |
ASO score: Sum of all factors (5–15 points).
| Total |
ASO opportunity |
| 12–15 |
high — ASO should be primary acquisition channel |
| 8–11 |
medium — ASO is viable but won't be the sole driver |
| 5–7 |
low — ASO alone won't generate meaningful installs |
Featured potential checklist
An app has App Store featured potential if it meets 3+ of these 5 criteria:
- Uses a newly released Apple/Google platform feature (widgets, Live Activities, visionOS, AI APIs)
- Has exceptional design quality (would look good in an editorial story)
- Serves an underrepresented audience or emerging cultural moment
- Has a clear positive-impact or wellness angle
- 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
| Factor |
Score: High |
Score: Medium |
Score: Low |
| Content generation |
App produces visual or shareable output that IS the content (before/after, results, transformations) |
App experience is interesting to narrate/demonstrate |
App is invisible — nothing to show on camera |
| Audience alignment |
Clear niche creator communities already talk about this problem space |
Adjacent creator communities exist |
No creator community maps to this product |
| Demo-ability |
Can be demonstrated in a 30–60 second clip with visible value |
Needs 2–3 minute explanation to convey value |
Requires hands-on usage over days to appreciate |
| Authenticity |
Creator would genuinely use the app (not just shill for money) |
Creator could plausibly use it occasionally |
Feels forced — creator has no real use case |
| Affiliate/monetization fit |
App has a price point that supports affiliate commissions ($5+/mo or $20+ one-time) |
Freemium with conversion — harder to attribute |
Free app with no monetization — no creator incentive |
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.
| Budget tier |
Monthly ad spend |
Viable paid strategies |
| Micro (< $200/mo) |
Testing only |
One platform, 2–3 ad creatives, learn CPM/CPI before scaling. Not a primary channel. |
| Light (< $500/mo) |
Targeted campaigns |
One platform with lookalike audiences. Can work if CPI < $2 and LTV > $6. |
| Moderate (< $2000/mo) |
Real optimization |
Multi-creative testing, retargeting. Viable if LTV:CAC > 3:1 on at least one platform. |
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:
| Advantage type |
Impact |
| Existing audience (newsletter, social, YouTube) |
Direct launch channel — reduces cold-start risk significantly |
| Community membership (active in relevant subreddits, Discord, forums) |
Warm audience for validation and early adopters |
| Content creation skills (video, writing, design) |
Can execute organic content channels without outsourcing |
| Technical SEO / ASO experience |
Can capitalize on search-driven channels faster |
| Industry relationships |
Potential for partnerships, cross-promotion, press |
| None identified |
Must rely on product-led or paid growth — harder path |
Step 6 — Distribution Verdict
Compute the overall verdict by evaluating all dimensions together, then adjust for founder tier.
Raw verdict logic
| Condition |
Raw verdict |
| k-factor ≥ 0.5 OR (ASO = high AND creator_fit = high) OR founder has existing audience |
strong |
| k-factor ≥ 0.2 AND at least one other dimension scores medium+ |
moderate |
| All dimensions low/marginal, no organic path, paid not viable at budget |
weak |
Tier adjustment
The same distribution profile means different things to different founders. Apply this adjustment:
| Founder tier |
Adjustment |
| beginner |
Downgrade verdict by one level if the only viable channels require technical skill (SEO, paid optimization, ASO keyword research). Beginners need channels with fast feedback loops: TikTok organic, community posting, referral-based growth. Flag complex channels as "aspirational — learn first." |
| builder |
No adjustment. Builders can execute most channels with some learning curve. Flag paid channels > $500/mo as risky given typical builder budgets. |
| growth |
Upgrade verdict by one level if paid channels are viable and the founder has optimization experience. Growth-tier founders can unlock channels that are traps for beginners. |
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."
1---2name: distribution-analysis3description: Evaluates organic reach potential, paid feasibility, platform distribution advantages, creator economy fit, and founder edge for a B2C app idea. Includes viral coefficient estimation, ASO scoring rubric, and tier-adjusted verdicts.4---56<!-- version: 0.2.0 | outputs: memory/ideas/<slug>/distribution.json -->78# Skill: distribution-analysis910## Purpose1112Distribution 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.1314## Input1516- Idea slug17- `memory/user_profile.md` (ICP tier, distribution advantages, budget constraint)18- `memory/ideas/<slug>/idea.md` (app concept, key features, differentiator)19- Optional: `memory/ideas/<slug>/competitors.json` (competitor distribution signals)2021## Distribution Dimensions2223| Dimension | Questions to Answer |24|---|---|25| Organic reach | Can this spread without paid spend? Is there a viral loop? What's the estimated viral coefficient? |26| Paid feasibility | Can paid ads break even at indie scale? What's the minimum viable budget? |27| Platform advantage | Is there an ASO moat? App Store featured potential? Category competitiveness? |28| Creator economy fit | Can influencers or creators promote this authentically? Does the app produce shareable output? |29| User's distribution edge | Does the user have an existing audience, community, or channel expertise? |3031## Process3233### Step 1 — Viral Coefficient Estimation3435The viral coefficient (k-factor) predicts whether an app can grow organically through user referrals. Estimate k = i × c where:3637- **i** = average number of invitations/shares per user38- **c** = conversion rate of each invitation3940#### Viral loop identification4142Evaluate the app concept against these loop types:4344| Loop type | Description | Typical k-factor | Example |45|---|---|---|---|46| Inherent | Product is useless alone, requires inviting others | 0.5–1.5 | Multiplayer games, shared lists |47| Collaborative | Better with others but works solo | 0.2–0.6 | Workout trackers with friends, shared budgets |48| Word-of-mouth | Users talk about it because it's remarkable | 0.1–0.4 | Apps that produce "wow" output (AI art, unique insights) |49| Incentivized | Users get a reward for referring | 0.1–0.3 | Referral credits, unlocked features |50| Content-as-distribution | App output is inherently shareable on social platforms | 0.3–0.8 | Photo editors with watermarks, personality quizzes, wrapped/recap screens |51| None | No natural reason to share | 0.0–0.05 | Utility apps (calculators, timers) |5253#### Estimation rubric54551. Identify which loop type(s) apply to the app concept.562. Estimate **i** (invitations per user) — consider: does the core UX prompt sharing? How often? To how many people?573. Estimate **c** (conversion per invitation) — consider: how compelling is the share artifact? Does the recipient need the app to view it?584. Compute k = i × c.595. Classify:6061| k-factor | Classification |62|---|---|63| k ≥ 0.7 | **Viral growth engine** — organic growth is a primary acquisition channel |64| 0.3 ≤ k < 0.7 | **Viral assist** — referrals supplement other channels meaningfully |65| 0.1 ≤ k < 0.3 | **Marginal virality** — some word-of-mouth, not a growth driver |66| k < 0.1 | **Non-viral** — growth depends entirely on other channels |6768> 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.6970### Step 2 — ASO Potential Scoring7172App Store Optimization is the highest-leverage free channel for indie developers. Score ASO opportunity on a 3-tier rubric:7374#### ASO scoring rubric7576| Factor | High (3 pts) | Medium (2 pts) | Low (1 pt) |77|---|---|---|---|78| **Category competition** | Niche category, top 10 achievable with <500 ratings | Moderate category, top 50 achievable | Saturated category, dominated by incumbents with 100K+ ratings |79| **Keyword opportunity** | High-volume keywords with low-rated top results (< 4.2 stars, < 1K ratings) | Keywords exist but top results are solid (4.5+ stars) | All relevant keywords dominated by well-known brands |80| **Search intent match** | Users actively search for this exact solution (tool/utility intent) | Users search for the category but not this specific angle | Discovery-dependent — users don't know they want this |81| **Review velocity potential** | App has natural prompt moments for asking reviews (completed task, achievement) | Some prompt moments but not in core loop | No natural review prompt; must interrupt to ask |82| **Visual differentiation** | App icon and screenshots can stand out (unique aesthetic, bold output previews) | Decent but similar to competitors | Looks like every other app in the category |8384**ASO score**: Sum of all factors (5–15 points).8586| Total | ASO opportunity |87|---|---|88| 12–15 | **high** — ASO should be primary acquisition channel |89| 8–11 | **medium** — ASO is viable but won't be the sole driver |90| 5–7 | **low** — ASO alone won't generate meaningful installs |9192#### Featured potential checklist9394An app has App Store featured potential if it meets **3+ of these 5 criteria**:95961. Uses a newly released Apple/Google platform feature (widgets, Live Activities, visionOS, AI APIs)972. Has exceptional design quality (would look good in an editorial story)983. Serves an underrepresented audience or emerging cultural moment994. Has a clear positive-impact or wellness angle1005. Is a premium/indie app (Apple editorially favors paid apps and small teams)101102### Step 3 — Creator Economy Fit Assessment103104Evaluate whether influencers and creators can authentically promote the app. Not all apps are "creator-friendly" — forcing influencer marketing on a utility app wastes money.105106#### Creator fit criteria107108| Factor | Score: High | Score: Medium | Score: Low |109|---|---|---|---|110| **Content generation** | App produces visual or shareable output that IS the content (before/after, results, transformations) | App experience is interesting to narrate/demonstrate | App is invisible — nothing to show on camera |111| **Audience alignment** | Clear niche creator communities already talk about this problem space | Adjacent creator communities exist | No creator community maps to this product |112| **Demo-ability** | Can be demonstrated in a 30–60 second clip with visible value | Needs 2–3 minute explanation to convey value | Requires hands-on usage over days to appreciate |113| **Authenticity** | Creator would genuinely use the app (not just shill for money) | Creator could plausibly use it occasionally | Feels forced — creator has no real use case |114| **Affiliate/monetization fit** | App has a price point that supports affiliate commissions ($5+/mo or $20+ one-time) | Freemium with conversion — harder to attribute | Free app with no monetization — no creator incentive |115116**Scoring**: Count High/Medium/Low across all 5 factors.117- **high fit**: 3+ factors scored High118- **medium fit**: 2 factors High, or 3+ Medium119- **low fit**: 2+ factors Low, or no factors High120121### Step 4 — Paid Channel Feasibility122123Assess whether paid acquisition can work within indie budget constraints.124125| Budget tier | Monthly ad spend | Viable paid strategies |126|---|---|---|127| **Micro** (< $200/mo) | Testing only | One platform, 2–3 ad creatives, learn CPM/CPI before scaling. Not a primary channel. |128| **Light** (< $500/mo) | Targeted campaigns | One platform with lookalike audiences. Can work if CPI < $2 and LTV > $6. |129| **Moderate** (< $2000/mo) | Real optimization | Multi-creative testing, retargeting. Viable if LTV:CAC > 3:1 on at least one platform. |130131If `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.132133### Step 5 — Founder Distribution Edge134135Cross-reference `user_profile.md` to identify whether the founder has a pre-existing distribution advantage:136137| Advantage type | Impact |138|---|---|139| Existing audience (newsletter, social, YouTube) | Direct launch channel — reduces cold-start risk significantly |140| Community membership (active in relevant subreddits, Discord, forums) | Warm audience for validation and early adopters |141| Content creation skills (video, writing, design) | Can execute organic content channels without outsourcing |142| Technical SEO / ASO experience | Can capitalize on search-driven channels faster |143| Industry relationships | Potential for partnerships, cross-promotion, press |144| None identified | Must rely on product-led or paid growth — harder path |145146### Step 6 — Distribution Verdict147148Compute the overall verdict by evaluating all dimensions together, then **adjust for founder tier**.149150#### Raw verdict logic151152| Condition | Raw verdict |153|---|---|154| k-factor ≥ 0.5 OR (ASO = high AND creator_fit = high) OR founder has existing audience | **strong** |155| k-factor ≥ 0.2 AND at least one other dimension scores medium+ | **moderate** |156| All dimensions low/marginal, no organic path, paid not viable at budget | **weak** |157158#### Tier adjustment159160The same distribution profile means different things to different founders. Apply this adjustment:161162| Founder tier | Adjustment |163|---|---|164| **beginner** | Downgrade verdict by one level if the only viable channels require technical skill (SEO, paid optimization, ASO keyword research). Beginners need channels with fast feedback loops: TikTok organic, community posting, referral-based growth. Flag complex channels as "aspirational — learn first." |165| **builder** | No adjustment. Builders can execute most channels with some learning curve. Flag paid channels > $500/mo as risky given typical builder budgets. |166| **growth** | Upgrade verdict by one level if paid channels are viable and the founder has optimization experience. Growth-tier founders can unlock channels that are traps for beginners. |167168If `user_profile.md` is unavailable, skip tier adjustment and note it as a gap.169170## Output171172Write to `memory/ideas/<slug>/distribution.json`:173174```json175{176 "organic_reach_potential": "high | medium | low",177 "viral_loop_exists": false,178 "viral_loop_type": "inherent | collaborative | word-of-mouth | incentivized | content-as-distribution | none",179 "viral_loop_description": "",180 "k_factor_estimate": 0.0,181 "k_factor_classification": "viral-growth-engine | viral-assist | marginal | non-viral",182 "paid_feasibility": "viable | marginal | not-viable",183 "minimum_paid_budget_monthly": 0,184 "paid_feasibility_rationale": "",185 "platform_advantage": {186 "aso_opportunity": "high | medium | low",187 "aso_score_breakdown": {188 "category_competition": 0,189 "keyword_opportunity": 0,190 "search_intent_match": 0,191 "review_velocity_potential": 0,192 "visual_differentiation": 0,193 "total": 0194 },195 "featured_potential": false,196 "featured_criteria_met": []197 },198 "creator_economy_fit": "high | medium | low",199 "creator_fit_rationale": "",200 "creator_fit_breakdown": {201 "content_generation": "high | medium | low",202 "audience_alignment": "high | medium | low",203 "demo_ability": "high | medium | low",204 "authenticity": "high | medium | low",205 "affiliate_fit": "high | medium | low"206 },207 "user_distribution_advantage": "",208 "user_advantage_type": "audience | community | content-skills | seo-aso | relationships | none",209 "recommended_first_channel": "",210 "recommended_first_channel_rationale": "",211 "channels_ranked": [212 { "channel": "", "viability": "high | medium | low", "time_to_first_100_users": "" }213 ],214 "distribution_verdict": "strong | moderate | weak",215 "tier_adjustment_applied": "",216 "distribution_verdict_rationale": ""217}218```219220## Notes221222- 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.223- If `competitors.json` is available, check competitor distribution strategies — an app succeeding via a channel the founder can replicate is a strong positive signal.224- k-factor estimates are inherently speculative pre-launch. Treat them as directional, not precise. Flag any estimate above 0.5 as "optimistic until validated."
MaxKmet