Generate interactive analytics dashboards from CRM data. Use when asked to "show pipeline stats", "create a report", "analyze leads", "show conversion rates"...
数据来源:ClawHub。 在 ClawSkills 查看
选择你使用的 Agent
方法一:命令行安装(推荐)
推荐(无需提前安装 clawhub)
npx clawhub@latest --dir ~/.claude/skills install ironclaw-pipeline-analytics或使用 clawhub CLI(需提前安装)
clawhub --dir ~/.claude/skills install ironclaw-pipeline-analytics⚠️ 需要 Node.js 18+,没有 Node?请使用下方方法二直接下载 ZIP。 安装 Node.js →
方法二:手动下载安装(无需 Node)
下载 ZIP,解压后将文件夹放到以下路径,重启 Agent 即可:
安装路径
~/.claude/skills/ironclaw-pipeline-analytics/💡解压后将文件夹放到上方路径,重启 Agent 即可生效
--- name: pipeline-analytics description: Generate interactive analytics dashboards from CRM data. Use when asked to "show pipeline stats", "create a report", "analyze leads", "show conversion rates", "build a dashboard", "visualize outreach data", "funnel analysis", or any data visualization request from DuckDB workspace data. metadata: { "openclaw": { "emoji": "📊" } } ---
Transform natural language questions into DuckDB queries and render results as interactive Recharts dashboards inline in chat.
User asks question in plain English
→ Translate to DuckDB SQL against workspace pivot views (v_*)
→ Execute query
→ Format results as report-json
→ Render as interactive Recharts components
-- List all objects and their entry counts
SELECT o.name, o.display_name, COUNT(e.id) as entries
FROM objects o
LEFT JOIN entries e ON e.object_id = o.id
GROUP BY o.name, o.display_name
ORDER BY entries DESC;
-- List fields for an object
SELECT f.name, f.field_type, f.display_name
FROM fields f
JOIN objects o ON f.object_id = o.id
WHERE o.name = 'leads'
ORDER BY f.position;
-- Available pivot views
SELECT table_name FROM information_schema.tables
WHERE table_name LIKE 'v_%';
SELECT "Status", COUNT(*) as count
FROM v_leads
GROUP BY "Status"
ORDER BY CASE "Status"
WHEN 'New' THEN 1
WHEN 'Contacted' THEN 2
WHEN 'Qualified' THEN 3
WHEN 'Demo Scheduled' THEN 4
WHEN 'Proposal' THEN 5
WHEN 'Closed Won' THEN 6
WHEN 'Closed Lost' THEN 7
ELSE 99
END;
SELECT DATE_TRUNC('week', "Last Outreach"::DATE) as week,
"Outreach Channel",
COUNT(*) as messages_sent
FROM v_leads
WHERE "Last Outreach" IS NOT NULL
GROUP BY week, "Outreach Channel"
ORDER BY week;
SELECT "Source",
COUNT(*) as total,
COUNT(*) FILTER (WHERE "Status" = 'Qualified') as qualified,
COUNT(*) FILTER (WHERE "Status" IN ('Closed Won', 'Converted')) as converted,
ROUND(100.0 * COUNT(*) FILTER (WHERE "Status" = 'Qualified') / COUNT(*), 1) as qual_rate,
ROUND(100.0 * COUNT(*) FILTER (WHERE "Status" IN ('Closed Won', 'Converted')) / COUNT(*), 1) as conv_rate
FROM v_leads
GROUP BY "Source"
ORDER BY total DESC;
SELECT "Outreach Channel",
COUNT(*) as sent,
COUNT(*) FILTER (WHERE "Reply Received" = true) as replied,
ROUND(100.0 * COUNT(*) FILTER (WHERE "Reply Received" = true) / COUNT(*), 1) as reply_rate
FROM v_leads
WHERE "Outreach Status" IS NOT NULL
GROUP BY "Outreach Channel";
SELECT "Source",
AVG(DATEDIFF('day', created_at, "Converted At"::DATE)) as avg_days_to_convert,
MEDIAN(DATEDIFF('day', created_at, "Converted At"::DATE)) as median_days
FROM v_leads
WHERE "Status" = 'Converted' AND "Converted At" IS NOT NULL
GROUP BY "Source";
Generate Recharts-compatible report cards:
{
"type": "report",
"title": "Pipeline Analytics — February 2026",
"generated_at": "2026-02-17T14:30:00Z",
"panels": [
{
"title": "Pipeline Funnel",
"type": "funnel",
"data": [
{"name": "New Leads", "value": 200},
{"name": "Contacted", "value": 145},
{"name": "Qualified", "value": 67},
{"name": "Demo Scheduled", "value": 31},
{"name": "Closed Won", "value": 13}
]
},
{
"title": "Outreach Activity",
"type": "area",
"xKey": "week",
"series": [
{"key": "linkedin", "name": "LinkedIn", "color": "#0A66C2"},
{"key": "email", "name": "Email", "color": "#EA4335"}
],
"data": [
{"week": "Feb 3", "linkedin": 25, "email": 40},
{"week": "Feb 10", "linkedin": 30, "email": 35}
]
},
{
"title": "Lead Source Breakdown",
"type": "donut",
"data": [
{"name": "LinkedIn Scrape", "value": 95, "color": "#0A66C2"},
{"name": "YC Directory", "value": 45, "color": "#FF6600"},
{"name": "Referral", "value": 30, "color": "#10B981"},
{"name": "Inbound", "value": 20, "color": "#8B5CF6"}
]
},
{
"title": "Reply Rates by Channel",
"type": "bar",
"xKey": "channel",
"series": [{"key": "rate", "name": "Reply Rate %", "color": "#3B82F6"}],
"data": [
{"channel": "LinkedIn", "rate": 32},
{"channel": "Email", "rate": 18},
{"channel": "Multi-Channel", "rate": 41}
]
}
]
}
| Type | Use Case | Recharts Component | |------|----------|-------------------| | bar | Comparisons, categories | BarChart | | line | Trends over time | LineChart | | area | Volume over time | AreaChart | | pie | Distribution (single level) | PieChart | | donut | Distribution (with center metric) | PieChart (innerRadius) | | funnel | Stage progression | FunnelChart | | scatter | Correlation (2 variables) | ScatterChart | | radar | Multi-dimension comparison | RadarChart |
| User Says | SQL Pattern | Chart Type | |-----------|-------------|------------| | "show me pipeline" | GROUP BY Status | funnel | | "outreach stats" | COUNT by channel + status | bar + area | | "how are we converting" | conversion rates | funnel + line | | "compare sources" | GROUP BY Source | bar | | "weekly trend" | DATE_TRUNC + GROUP BY | line / area | | "who replied" | FILTER Reply Received | table | | "best performing" | ORDER BY conversion DESC | bar | | "lead breakdown" | GROUP BY any dimension | pie / donut |
Reports can be saved as .report.json files in the workspace:
~/.openclaw/workspace/reports/
pipeline-overview.report.json
weekly-outreach.report.json
monthly-review.report.json
These render as live dashboards in the Ironclaw web UI when opened.
Auto-generate weekly/monthly reports:
{
"name": "Weekly Pipeline Report",
"schedule": { "kind": "cron", "expr": "0 9 * * MON", "tz": "America/Denver" },
"payload": {
"kind": "agentTurn",
"message": "Generate weekly pipeline analytics report. Query DuckDB for this week's data. Create report-json with: funnel, outreach activity (area), reply rates (bar), source breakdown (donut). Save to workspace/reports/ and announce summary."
}
}安装 Ironclaw Pipeline Analytics 后,可以对 AI 说这些话来触发它
Help me get started with Ironclaw Pipeline Analytics
Explains what Ironclaw Pipeline Analytics does, walks through the setup, and runs a quick demo based on your current project
Use Ironclaw Pipeline Analytics to generate interactive analytics dashboards from CRM data
Invokes Ironclaw Pipeline Analytics with the right parameters and returns the result directly in the conversation
What can I do with Ironclaw Pipeline Analytics in my marketing & growth workflow?
Lists the top use cases for Ironclaw Pipeline Analytics, with example commands for each scenario
将技能文件夹放到 ~/.claude/skills/ironclaw-pipeline-analytics/ 目录(个人级,所有项目可用),或 .claude/skills/ironclaw-pipeline-analytics/(项目级)。重启 AI 客户端后,用 /ironclaw-pipeline-analytics 主动调用,或让 AI 根据上下文自动发现并使用。
Ironclaw Pipeline Analytics 支持 Claude、Cursor、OpenClaw,可与这些 AI 平台无缝集成,扩展其能力。
Ironclaw Pipeline Analytics 可免费安装使用。请查阅仓库了解许可证信息。
Generate interactive analytics dashboards from CRM data. Use when asked to "show pipeline stats", "create a report", "analyze leads", "show conversion rates"...
Ironclaw Pipeline Analytics 属于「Marketing & Growth」分类,该分类的技能帮助 AI 智能体在此领域执行专业任务。
Automate my marketing & growth tasks using Ironclaw Pipeline Analytics
Identifies repetitive steps in your workflow and sets up Ironclaw Pipeline Analytics to handle them automatically