用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt;运行前请先检查来源。
npx skills add https://github.com/a5c-ai/babysitter --skill market-research-aggregator命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地?可下载 SkillsMP 当前能够提供的文件。
Reference for querying the Atlas knowledge graph through its MCP tools — the SECONDARY enrichment/comparison layer that adds best-practice context to systems you have ALREADY scanned from your real sources (`az`, repos, dirs). Use when you need to look up nodes, edges, kinds, clusters, stats, or wiki pages in Atlas to compare against your real inventory. (atlas graph, query atlas, atlas mcp, search the graph, graph neighbors, atlas record, atlas kinds, enrichment layer)
Atlas turns your STATED NEED into a real systems atlas by SCANNING your actual sources (Azure via `az`, git repos, local dirs) and process/data mining them, THEN enriching against the Atlas knowledge graph. Use this skill when asked to inventory/map your real systems, scan your cloud + repos + directories, mine the real processes or data they contain, or collect their real constraints/gotchas. (atlas, scan my systems, inventory our azure account, map my repos, real systems atlas, process mining, data mining, collect nuances, system discovery)
This skill should be used when the user asks to "find skills in the wild", "assimilate popular workflows", "discover SKILL.md files in repos", "research external skills", "find workflow patterns", "survey the skill landscape", "what skills exist out there", or wants to investigate public repositories for extractable processes, babysitter plugins, and reusable procedural insights. Searches GitHub for SKILL.md files, classifies repos by archetype, and maintains structured research under docs/reference-repos/.
正在显示 SKILL.md
基于 SOC 职业分类
| name | market-research-aggregator |
| description | Market intelligence aggregation skill for synthesizing market data from multiple sources |
| allowed-tools | ["Read","Write","Glob","Grep","Bash"] |
| metadata | {"specialization":"decision-intelligence","domain":"business","category":"knowledge-management","priority":"medium","tools-libraries":["pandas","requests","custom integrations"]} |
| graph | {"domains":["domain:business-intelligence"],"skillAreas":["skill-area:market-research","skill-area:competitive-intelligence","skill-area:market-sizing"],"roles":["role:data-analyst","role:strategic-planner","role:marketing-strategist"]} |
The Market Research Aggregator skill provides capabilities for collecting, synthesizing, and analyzing market intelligence from multiple sources. It enables systematic market sizing, trend identification, and opportunity assessment by combining data from various research providers and public sources.
# Define market to analyze
market_definition = {
"name": "Enterprise Project Management Software",
"description": "Software solutions for managing projects, resources, and portfolios in large organizations",
"scope": {
"product_types": ["On-premise", "Cloud-based", "Hybrid"],
"customer_segments": ["Enterprise (>1000 employees)"],
"excluded": ["SMB solutions", "Personal productivity tools"]
},
"geographic_scope": ["North America", "Europe", "Asia Pacific"],
"time_frame": {"historical": "2019-2023", "forecast": "2024-2028"},
"currency": "USD",
"units": "Revenue"
}
# Configure data sources
data_sources = {
"primary_research": [
{
"source": "Gartner",
"report_name": "Market Share: Enterprise Project Management Software",
"date": "2024-01",
"data_type": "market_share",
"reliability": "high"
},
{
"source": "IDC",
"report_name": "Worldwide Project Management Software Forecast",
"date": "2023-12",
"data_type": "market_forecast",
"reliability": "high"
}
],
"secondary_research": [
{
"source": "Industry Association Reports",
"reliability": "medium"
},
{
"source": "Company Annual Reports",
"reliability": "high"
}
],
"public_data": [
{
"source": "Government Statistics",
"data_type": "industry_employment",
"reliability": "high"
}
]
}
# Market size calculation
market_sizing = {
"tam": {
"definition": "Total addressable market for all project management software globally",
"methodology": "top_down",
"calculation": {
"total_enterprises": 500000,
"adoption_rate": 0.85,
"average_spend": 150000,
"result": 63750000000
},
"sources": ["Gartner", "IDC"],
"confidence": "high"
},
"sam": {
"definition": "Serviceable addressable market in target geographies for enterprise segment",
"methodology": "bottom_up",
"calculation": {
"target_enterprises": 75000,
"product_fit_rate": 0.60,
"average_spend": 200000,
"result": 9000000000
},
"confidence": "medium"
},
"som": {
"definition": "Serviceable obtainable market based on competitive position",
"methodology": "competitive_analysis",
"calculation": {
"sam": 9000000000,
"realistic_share": ,
:
},
: ,
:
}
}
# Identify and track trends
trend_analysis = {
"trends": [
{
"name": "AI-powered project management",
"direction": "accelerating",
"impact": "high",
"timeline": "2024-2027",
"evidence": [
"75% of vendors adding AI features",
"40% budget increase for AI capabilities"
],
"implications": ["Feature differentiation", "Pricing pressure", "Skill requirements"]
},
{
"name": "Consolidation of point solutions",
"direction": "steady",
"impact": "medium",
"evidence": ["M&A activity up 30%", "Platform play preference"]
}
],
"methodology": "expert_consensus",
"update_frequency": "quarterly"
}
{
"operation": "define|collect|size|analyze|report",
"market_definition": {
"name": "string",
"scope": "object",
"geographic_scope": ["string"],
"time_frame": "object"
},
"data_sources": {
"primary": ["object"],
"secondary": ["object"],
"public": ["object"]
},
"analysis_request": {
"type": "sizing|trends|segments|geography|competitive",
"parameters"
{
"market_overview": {
"name": "string",
"current_size": "number",
"growth_rate": "number",
"forecast_period": "string"
},
"market_sizing": {
"TAM": {"value": "number", "confidence": "string"},
"SAM": {"value": "number", "confidence": "string"},
"SOM": {"value": "number", "confidence": "string"}
| Method | Approach | Best For |
|---|---|---|
| Top-Down | Start with total market, narrow down | Mature markets with good data |
| Bottom-Up | Build from unit economics | New markets, specific segments |
| Value-Based | Based on customer value delivered | Innovative solutions |
| Competitive | Sum of competitor revenues | Markets with public companies |
| Dimension | Assessment Criteria |
|---|---|
| Accuracy | Source reliability, methodology |
| Completeness | Coverage of market segments |
| Timeliness | Data recency |
| Consistency | Agreement across sources |
| Relevance | Alignment with market definition |