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 agentmods add commands/alphaaiservice/cortex/market-researchgit clone --depth 1 https://github.com/alphaaiservice/cortexWhat 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 | $0.00040 | $0.02850 |
| Opus 5 | $0.00020 | $0.01425 |
| Sonnet 5 | $0.00008 | $0.00570 |
| Haiku 4.5 | $0.00004 | $0.00285 |
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 3d 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 — 330 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Market Research & Competitive Intelligence
Research topic: $ARGUMENTS
Conduct comprehensive market research to ensure the product is world-class. Use WebSearch and WebFetch extensively. Minimum 20 search queries and 5 website fetches before writing the report.
Step 1: Competitor Discovery & Analysis
Execute these searches:
WebSearch: "[topic] best tools 2025 2026"
WebSearch: "[topic] top competitors"
WebSearch: "[topic] alternatives comparison review"
WebSearch: "[topic] vs [topic] comparison"
WebSearch: "[topic] market leaders enterprise"
WebSearch: "[topic] for startups small business"
WebSearch: "[topic] open source github"
For each of the top 5-10 competitors:
WebFetch: [competitor landing page] → extract value proposition, features, positioning
WebFetch: [competitor pricing page] → extract pricing tiers, feature gates, free plan limits
WebFetch: [competitor docs/changelog] → identify tech stack, API capabilities, recent updates
Document:
- Company name & URL
- Founded, funding, team size (if findable)
- Core features (list top 10)
- Pricing model (freemium / per-seat / usage-based / flat rate)
- Pricing tiers (free, starter, pro, enterprise — exact prices)
- Tech stack (if identifiable from job postings, docs, or headers)
- Unique selling points (what they emphasize in marketing)
- Weaknesses (from user reviews, complaints, missing features)
- User reviews/ratings (G2, Capterra, Product Hunt, Reddit)
Step 2: Market Size & Trends
Execute these searches:
WebSearch: "[topic] market size 2025 2026"
WebSearch: "[topic] industry report"
WebSearch: "[topic] growth rate forecast"
WebSearch: "[topic] trends predictions"
WebSearch: "[topic] India market opportunity"
WebSearch: "[topic] SaaS landscape"
Document:
- Total Addressable Market (TAM): Global market size
- Serviceable Addressable Market (SAM): India / target region
- Growth Rate: YoY growth percentage
- Key Trends: 3-5 industry trends shaping the market
- Emerging Technologies: AI/ML adoption, automation trends
- Regulatory Environment: Any compliance requirements (GDPR, data localization, etc.)
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.
- 3d ago First seen · 330 lines · 40 tokens per session scan A 58797afdec3d
market-research is a command published in the GitHub repository alphaaiservice/cortex (1 stars, last pushed 27d ago), licensed MIT. It adds 40 tokens to every session and 2,850 once invoked, about $0.0002 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 2026-08-31.
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