Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add rshankras/claude-code-apple-skills --skill market-researchgit clone --depth 1 https://github.com/rshankras/claude-code-apple-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/rshankras/claude-code-apple-skills/market-research)<a href="https://agentmods.dev/skills/rshankras/claude-code-apple-skills/market-research"><img src="https://agentmods.dev/badge/skills/rshankras/claude-code-apple-skills/market-research/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/rshankras/claude-code-apple-skills/market-research"><img src="https://agentmods.dev/badge/skills/rshankras/claude-code-apple-skills/market-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00067 | $0.03160 |
| Opus 5 | $0.00034 | $0.01580 |
| Sonnet 5 | $0.00013 | $0.00632 |
| Haiku 4.5 | $0.00007 | $0.00316 |
Grade A, and why
market-research scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 434 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Market Research Skill
Performs deep market research for iOS/macOS app ideas. Provides market sizing, growth analysis, and opportunity assessment.
When This Skill Activates
Use this Skill when the user wants to:
- Understand market size and potential
- Analyze market growth trends
- Calculate TAM/SAM/SOM
- Assess market maturity
- Identify entry barriers
- Understand distribution channels
- Estimate revenue potential
- Deep-dive after initial problem discovery
This is a follow-up to the product-agent skill — use this when you need market depth.
What This Skill Does
1. Market Sizing (TAM/SAM/SOM)
- TAM (Total Addressable Market): Total revenue opportunity if you captured 100% of the market
- SAM (Serviceable Available Market): Segment of TAM you can reach with your product/distribution
- SOM (Serviceable Obtainable Market): Realistic share you can capture in near term (1-3 years)
2. Growth Analysis
- Historical growth rates
- Future projections (3-5 years)
- Growth drivers
- Market trends
3. Market Maturity Assessment
- Stage: Emerging, Growing, Mature, or Declining
- Market lifecycle position
- Implications for new entrants
4. Entry Barriers
- Technical barriers
- Brand/network effects
- Regulatory requirements
- Capital requirements
- Customer acquisition costs
5. Distribution Channels
- How apps in this category reach users
- App Store dynamics
- Alternative channels (web, enterprise, etc.)
6. Revenue Potential
- Average revenue per user (ARPU)
- Conversion rates
- LTV (Lifetime Value)
- Revenue models in use
Output Structure
{
"market_category": "Task Management",
"market_sizing": {
"tam": {
"value": "$4.5B",
"description": "Global productivity software market",
"methodology": "Total potential revenue if product served all users globally"
},
"sam": {
"value": "$900M",
"description": "iOS/macOS task management apps (20% of TAM)",
"methodology": "Addressable via App Store distribution on Apple platforms"
},
"som": {
"value": "$45M",
"description": "Realistic 3-year capture (5% of SAM)",
"methodology": "Based on typical indie app market share penetration"
}
},
"market_growth": {
"historical_growth": "12% CAGR (2021-2025)",
"projected_growth": "10% CAGR (2026-2030)",
"growth_drivers": [
"Remote work adoption",
"Increased digital task management",
"Mobile-first workflows"
],
"headwinds": [
"Market saturation",
"Consolidation toward major players"
]
},
"market_maturity": {
"stage": "Mature",
"characteristics": [
"Established leaders (Todoist, Things)",
"Clear product categories",
"Slowing growth rate",
"Focus on feature differentiation"
],
"implications": "Differentiation critical. Hard to compete on basics. Must have unique angle."
},
"entry_barriers": {
"low": [
"Technical implementation (task management is straightforward)"
],
"medium": [
"Building user base in crowded market",
"Achieving reliable sync across devices"
],
"high": [
"Brand recognition (Todoist, Things have 10+ years)",
"Network effects (team collaboration features)",
"Customer switching costs (data lock-in)"
],
"overall_assessment": "Medium-High - Technical execution is achievable, but market position is difficult"
},
"distribution_channels": {
"primary": {
"channel": "App Store",
"percentage": "75%",
"dynamics": "Discoverability challenging. ASO critical. Top charts dominated by established apps."
},
"secondary": [
{
"channel": "Direct website",
"percentage": "15%",
"dynamics": "For power users. Allows higher pricing. Better for subscription retention."
},
{
"channel": "Word of mouth / Communities",
"percentage": "10%",
"dynamics": "Productivity communities, Reddit, Twitter. High-intent users."
}
]
},
"revenue_potential": {
"arpu": {
"freemium": "$12/year (5% convert at $20/year)",
"paid_only": "$30-40/year",
"premium": "$60-100/year"
},
"conversion_rates": {
"free_to_paid": "3-7% industry average",
"trial_to_paid": "15-25% with 14-day trial"
},
"ltv": "$150-300 (2-5 year user lifecycle)",
"realistic_year_1": "$50K-200K (1K-5K users at $40 ARPU)",
"realistic_year_3": "$500K-2M (10K-50K users with growth)",
"path_to_scale": "Requires strong differentiation, word-of-mouth growth, and retention >85%"
},
"market_opportunity_score": "6/10 - Moderate",
"reasoning": "Large market with growth, but mature and competitive. Success requires clear differentiation and excellent execution. Not a 'gold rush' market, but sustainable business possible for well-positioned product."
}
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago First seen · 434 lines · 67 tokens per session scan A 858b4765be8e
market-research is a skill published in the GitHub repository rshankras/claude-code-apple-skills (719 stars, last pushed 1mo ago), licensed MIT. It adds 67 tokens to every session and 3,160 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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