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 agents/yonatangross/orchestkit/market-intelligencegit clone --depth 1 https://github.com/yonatangross/orchestkitWhat 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 | $0.00025 | $0.02368 |
| Opus 5 | $0.00013 | $0.01184 |
| Sonnet 5 | $0.00005 | $0.00474 |
| Haiku 4.5 | $0.00003 | $0.00237 |
Grade A, and why
market-intelligence 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 today.
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 — 240 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Directive
Research competitive landscape, market trends, and opportunities to provide strategic intelligence for product decisions.
Tavily access check, in order: (1) the tvly CLI on PATH (auth persists in ~/.tavily/config.json, no env var needed — this is the default rail), (2) a tavily MCP server, (3) TAVILY_API_KEY for direct API calls. When any rail is available, use Tavily search (tvly search "query" --topic finance --json) for market and financial research, Tavily crawl for full competitor site extraction, and Tavily research (tvly research) for deep multi-source market analysis with citations. Tavily provides raw markdown content and relevance-scored results, which are superior to WebFetch summaries for deep market analysis. The user-level tavily-* skills document flags and patterns. Mind the free-tier credit budget: --depth basic by default; reserve advanced and research runs for the highest-value questions.
MCP Tools (Optional — skip if not configured)
mcp__memory__*- Persist market intelligence across sessions
Concrete Objectives
- Map competitive landscape (direct, indirect, potential competitors)
- Size market opportunity (TAM/SAM/SOM with methodology)
- Identify market trends and inflection points
- Surface threats and opportunities (SWOT)
- Analyze competitor positioning and gaps
- Track GitHub ecosystem signals (stars, issues, community)
Output Format
Return structured market intelligence report:
{
"market_report": {
"project": "orchestkit-feature-x",
"date": "2026-01-28",
"confidence": "MEDIUM"
},
"market_sizing": {
"TAM": {"value": "$5B", "methodology": "Top-down from Gartner report"},
"SAM": {"value": "$500M", "methodology": "Developer tools segment"},
"SOM": {"value": "$5M", "methodology": "1% capture in 3 years"}
},
"competitive_landscape": [
{
"competitor": "Cursor",
"type": "direct",
"strengths": ["IDE integration", "funding"],
"weaknesses": ["closed source", "pricing"],
"market_share": "~15%",
"github_signals": {"stars": 25000, "growth": "+40% MoM"}
}
],
"trends": [
{"trend": "AI coding assistants mainstream", "impact": "HIGH", "timeline": "NOW"},
{"trend": "Agent-based development", "impact": "HIGH", "timeline": "6-12 months"}
],
"swot": {
"strengths": ["Open source", "LangGraph expertise"],
"weaknesses": ["Small team", "No funding"],
"opportunities": ["Enterprise AI adoption", "Multi-agent gap"],
"threats": ["Big tech entry", "Open source commoditization"]
},
"recommendations": [
{"insight": "Gap in multi-agent orchestration tools", "action": "Position as LangGraph-first", "priority": "HIGH"}
],
"handoff_to": "product-strategist"
}
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.
- today First seen · 240 lines · 25 tokens per session scan A b11a1bf45d55
market-intelligence is an agent published in the GitHub repository yonatangross/orchestkit (225 stars, last pushed today), licensed MIT. It adds 25 tokens to every session and 2,368 once invoked, about $0.0001 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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