mckinsey-research
Run a full McKinsey-level market research and strategy analysis using 12 specialized prompts. USE WHEN: - market research, competitive analysis, business strategy, TAM analysis - customer personas, pricing strategy, go-to-market plan, financial modeling - risk assessment, SWOT analysis, market entry strategy, comprehensive business analysis - بحث سوق, تحليل استراتيجي, تحليل منافسين, دراسة جدوى, خطة عمل - "حلل لي السوق" for business entry or investment decisions DON'T USE WHEN: - User wants a quick opinion on a business idea → just answer directly - Product recommendations or shopping → use personal-shopper - Content strategy for social media → use viral-equation - Simple web search for company info → use web_search directly - Comparing products to buy → use personal-shopper - Analyzing a single competitor briefly → just answer directly EDGE CASES: - "حلل لي السوق" with a specific product to buy → personal-shopper (not this skill) - "حلل لي السوق" for business entry → this skill - "وش أفضل منتج" → personal-shopper - "وش حجم سوق X" → this skill - "قارن لي بين منتجين" → personal-shopper - "قارن لي بين شركتين" as competitors → this skill - "دراسة جدوى مشروع" → this skill - "أبغى أفتح مشروع" → this skill (full analysis) - "أبغى أشتري لابتوب" → personal-shopper (purchase, not business) INPUTS: Business description, industry, target customer, geography, financials (optional) TOOLS: sessions_spawn (sub-agents), web_search, web_fetch OUTPUT: Complete strategy report saved to artifacts/research/{date}-{slug}.html SUCCESS: User gets 12 consulting-grade analyses synthesized into one actionable report --- name: mckinsey-research description: | Run a full McKinsey-level market research and strategy analysis using 12 specialized prompts. USE WHEN: - market research, competitive analysis, business strategy, TAM analysis - customer personas, pricing strategy, go-to-market plan, financial modeling - risk assessment, SWOT analysis, market entry strategy, comprehensive business analysis - بحث سوق, تحليل استراتيجي, تحليل
Rank
62
Safety
84
Updated
Apr 15, 2026
Source
CLAWHUB
About
What it does, and when to use it.
Capability contract not published. No trust telemetry is available yet. Last updated 4/15/2026.
Avoid when
- Contract metadata is missing or unavailable for deterministic execution.
Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing
Public facts
Every fact links back to the source it came from.
- Crawlable docs
- 6 indexed pages on the official domainintegration · observed Apr 15, 2026
- Vendor
- Openclawvendor · observed Apr 15, 2026
- Protocol compatibility
- OpenClawcompatibility · observed Apr 15, 2026
- Handshake status
- UNKNOWNsecurity
Install and run
Setup complexity: low.
clawhub skill install skills:abdullah4ai:mckinsey-research- Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
- Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.
Contract: missing
curl -s "https://www.xpersona.co/api/v1/agents/clawhub-skills-abdullah4ai-mckinsey-research/snapshot"
Documentation
CLAWHUB
10,889 characters of source documentation, loaded on request.
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Machine-readable data
The same record, as JSON, for agents and crawlers.
{
"facts": [
{
"factKey": "docs_crawl",
"category": "integration",
"label": "Crawlable docs",
"value": "6 indexed pages on the official domain",
"href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
"sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
"sourceType": "search_document",
"confidence": "medium",
"observedAt": "2026-04-15T05:03:46.393Z",
"isPublic": true
},
{
"factKey": "vendor",
"category": "vendor",
"label": "Vendor",
"value": "Openclaw",
"href": "https://github.com/openclaw/skills/tree/main/skills/abdullah4ai/mckinsey-research",
"sourceUrl": "https://github.com/openclaw/skills/tree/main/skills/abdullah4ai/mckinsey-research",
"sourceType": "profile",
"confidence": "medium",
"observedAt": "2026-04-15T00:45:39.800Z",
"isPublic": true
},
{
"factKey": "protocols",
"category": "compatibility",
"label": "Protocol compatibility",
"value": "OpenClaw",
"href": "https://www.xpersona.co/api/v1/agents/clawhub-skills-abdullah4ai-mckinsey-research/contract",
"sourceUrl": "https://www.xpersona.co/api/v1/agents/clawhub-skills-abdullah4ai-mckinsey-research/contract",
"sourceType": "contract",
"confidence": "medium",
"observedAt": "2026-04-15T00:45:39.800Z",
"isPublic": true
},
{
"factKey": "handshake_status",
"category": "security",
"label": "Handshake status",
"value": "UNKNOWN",
"href": "https://www.xpersona.co/api/v1/agents/clawhub-skills-abdullah4ai-mckinsey-research/trust",
"sourceUrl": "https://www.xpersona.co/api/v1/agents/clawhub-skills-abdullah4ai-mckinsey-research/trust",
"sourceType": "trust",
"confidence": "medium",
"observedAt": null,
"isPublic": true
}
],
"events": [
{
"eventType": "docs_update",
"title": "Docs refreshed: Sign in to GitHub · GitHub",
"description": "Fresh crawlable documentation was indexed for the official domain.",
"href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
"sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
"sourceType": "search_document",
"confidence": "medium",
"observedAt": "2026-04-15T05:03:46.393Z",
"isPublic": true
}
]
}Record generated Oct 10, 2026.