Openaso Aso
hubab1/OpenASO
A skill your agent uses when performing App Store Optimization work with OpenASO MCP data: ASO audits, keyword research, metadata optimization, screenshot strategy, review analysis, competitor…
Evaluates organic reach potential, paid feasibility, platform distribution advantages, creator economy fit, and founder edge for a B2C app idea.
Install Distribution Analysis
$ npx skills add MaxKmet/idea-validation-agents --skill distribution-analysis -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install MaxKmet/idea-validation-agents distribution-analysis --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/MaxKmet/idea-validation-agents.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/distribution-analysis .claude/skills/distribution-analysis && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "distribution-analysis" agent skill from https://github.com/MaxKmet/idea-validation-agents/tree/main/skills/distribution-analysis into .claude/skills/distribution-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "distribution-analysis", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/MaxKmet/idea-validation-agents/tree/main/skills/distribution-analysisType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add MaxKmet/idea-validation-agents --skill distribution-analysis -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install MaxKmet/idea-validation-agents distribution-analysis --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/MaxKmet/idea-validation-agents.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/distribution-analysis .agents/skills/distribution-analysis && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "distribution-analysis" agent skill from https://github.com/MaxKmet/idea-validation-agents/tree/main/skills/distribution-analysis into .agents/skills/distribution-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "distribution-analysis", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add MaxKmet/idea-validation-agents --skill distribution-analysis -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install MaxKmet/idea-validation-agents distribution-analysis --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/MaxKmet/idea-validation-agents.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/distribution-analysis .cursor/skills/distribution-analysis && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "distribution-analysis" agent skill from https://github.com/MaxKmet/idea-validation-agents/tree/main/skills/distribution-analysis into .cursor/skills/distribution-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "distribution-analysis", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/MaxKmet/idea-validation-agents.git --path skills/distribution-analysis--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add MaxKmet/idea-validation-agents --skill distribution-analysis -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install MaxKmet/idea-validation-agents distribution-analysis --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/MaxKmet/idea-validation-agents.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/distribution-analysis .gemini/skills/distribution-analysis && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "distribution-analysis" agent skill from https://github.com/MaxKmet/idea-validation-agents/tree/main/skills/distribution-analysis into .gemini/skills/distribution-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "distribution-analysis", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install MaxKmet/idea-validation-agents distribution-analysisInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add MaxKmet/idea-validation-agents --skill distribution-analysis -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/MaxKmet/idea-validation-agents.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/distribution-analysis .github/skills/distribution-analysis && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "distribution-analysis" agent skill from https://github.com/MaxKmet/idea-validation-agents/tree/main/skills/distribution-analysis into .github/skills/distribution-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "distribution-analysis", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add MaxKmet/idea-validation-agents --skill distribution-analysis -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install MaxKmet/idea-validation-agents distribution-analysis --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/MaxKmet/idea-validation-agents.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/distribution-analysis .opencode/skills/distribution-analysis && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "distribution-analysis" agent skill from https://github.com/MaxKmet/idea-validation-agents/tree/main/skills/distribution-analysis into .opencode/skills/distribution-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "distribution-analysis", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
distribution-analysis- GitHub stars
- 474
- Token cost
- ~3.1k tokens
- SKILL.md length
- 1,387 words
- Files
- 1
- Skills in repo
- 15
- Repo updated
- First seen
- Licence
- MIT
At a glance
Evaluates organic reach potential, paid feasibility, platform distribution advantages, creator economy fit, and founder edge for a B2C app idea.
- Works in 6 steps: Viral Coefficient Estimation → ASO Potential Scoring → Creator Economy Fit Assessment → …
- Tasks that involve Quizzes and assessments
- SKILL.md covers Purpose, Input, Distribution Dimensions and Process, plus 2 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Distribution Analysis is an agent skill from MaxKmet/idea-validation-agents. 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.
Its SKILL.md is about 3.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Education, covering Quizzes and assessments and App store release. The repository describes itself as: AI agents that act as your personal venture analyst - from startup idea brainstorming to full validation and go-to-market strategy. Built for developers who'd rather validate in… The licence is MIT.
When your agent uses it
- Tasks that involve Quizzes and assessments
- Tasks that involve App store release
Example prompts
- “Use the distribution-analysis skill to evaluate organic reach potential, paid feasibility, platform distribution advantages, creator economy fit…”
- “/distribution-analysis”
Workflow steps
6 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 3a4c800. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are json).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Distribution Analysis loads about 3.1k tokens when it runs. Until then it costs about 63 tokens; SKILL.md has 1,387 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from MaxKmet/idea-validation-agents at commit 3a4c800, republished under its MIT licence (© MaxKmet). 1,387 words, ~3,145 tokens.
Download SKILL.mdSave it as
.claude/skills/distribution-analysis/SKILL.md (or your agent's skills folder).- name
- distribution-analysis
- description
- 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.
<!-- version: 0.2.0 | outputs: memory/ideas/<slug>/distribution.json -->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.
Show full SKILL.md (629 more words)Show less
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_channelshould 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.jsonis 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."
© MaxKmet, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in skills/distribution-analysis of MaxKmet/idea-validation-agents.
Open the folder on GitHubat commit 3a4c800
Compare with similar skills
Distribution Analysis next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Distribution Analysis this skillMaxKmet/idea-validation-agents | 474 | — | ~3.1k | Automated safety check: Pass | MIT | |
| Openaso Asohubab1/OpenASO | 177 | — | ~946 | Automated safety check: Pass | MIT | |
| DeepTutor CLIHKUDS/DeepTutor | 41k | — | ~2.8k | Automated safety check: Pass | Apache-2.0 | |
| AI Engineering Placement Quizrohitg00/ai-engineering-from-scratch | 66k | — | ~2k | Automated safety check: Pass | MIT | |
| Codebase to Coursezarazhangrui/codebase-to-course | 5.7k | — | ~4.4k | Automated safety check: Pass | None | |
| AI Engineering Phase Quizrohitg00/ai-engineering-from-scratch | 66k | — | ~2.1k | Automated safety check: Pass | MIT |
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Related
Categories
Questions about Distribution Analysis
What does Distribution Analysis do?
Evaluates organic reach potential, paid feasibility, platform distribution advantages, creator economy fit, and founder edge for a B2C app idea. Distribution Analysis is an agent skill from MaxKmet/idea-validation-agents. Evaluates organic reach potential, paid feasibility, platform distribution advantages, creator economy fit, and founder edge for a B2C app idea.
When should I use Distribution Analysis?
Distribution Analysis fits situations like: tasks that involve Quizzes and assessments; tasks that involve App store release.
How do I install Distribution Analysis in Claude Code?
Run `npx skills add MaxKmet/idea-validation-agents --skill distribution-analysis -a claude-code`. Or copy the skill folder (skills/distribution-analysis in MaxKmet/idea-validation-agents) into .claude/skills/distribution-analysis in your project. Claude Code loads it when a task matches its description.
How do I install Distribution Analysis in Codex?
Run `npx skills add MaxKmet/idea-validation-agents --skill distribution-analysis -a codex`. Or copy the skill folder (skills/distribution-analysis in MaxKmet/idea-validation-agents) into .agents/skills/distribution-analysis in your project. Codex loads it when a task matches its description.
Can I use Distribution Analysis in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add MaxKmet/idea-validation-agents --skill distribution-analysis -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/distribution-analysis, .gemini/skills/distribution-analysis, .github/skills/distribution-analysis and .opencode/skills/distribution-analysis in your project.
What does Distribution Analysis need to run?
SKILL.md names no scripts, command-line tools or credentials: Distribution Analysis is instructions for the agent only.
Does Distribution Analysis access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Distribution Analysis safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Distribution Analysis use?
Distribution Analysis is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Distribution Analysis use?
About 3.1k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Distribution Analysis?
Skills that share tags, products or a category with Distribution Analysis: Openaso Aso (hubab1/OpenASO, 177 stars), DeepTutor CLI (HKUDS/DeepTutor, 41k stars), AI Engineering Placement Quiz (rohitg00/ai-engineering-from-scratch, 66k stars) and Codebase to Course (zarazhangrui/codebase-to-course, 5.7k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Distribution Analysis?
MaxKmet (a GitHub user) maintains it in MaxKmet/idea-validation-agents, which has 474 GitHub stars. The repository holds 15 skills in this directory. The repository was last updated on June 16, 2026.
Source: MaxKmet/idea-validation-agents on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.