analytics-explore
SkillBuild and run a GrowthBook Product Analytics chart via the REST API — visualize a metric over time, aggregate a fact table, or chart a raw warehouse table, then return the numbers plus a deep link to the chart. Use when the user asks "show me signups by country", "chart daily active users", "how man
Install
git clone https://github.com/growthbook/skills.git ~/.claude/skills/analytics-exploreWhat is analytics-explore?
Build and run a GrowthBook Product Analytics chart via the REST API — visualize a metric over time, aggregate a fact table, or chart a raw warehouse table, then return the numbers plus a deep link to the chart. Use when the user asks "show me signups by country", "chart daily active users", "how many orders last week", "plot revenue over time", "break that down by plan", or any "show me / chart / plot / how many" question about product data. For discovering what metrics and fact tables exist first, use metric-search. For experiment results, use experiment-analyze — this skill is for general analytics, not A/B test readouts.
What this can do
Capabilities declared in this component's own frontmatter — not inferred.
Run shell commands
Declares Bash
~158 tokens of context used while enabled, before you invoke anything
All declared tools (1)
BashDocumentation
README · ~11 min readanalytics-explore
Build and run a Product Analytics exploration — GrowthBook's ad-hoc charting surface — and report the numbers with a deep link to the rendered chart. Three dataset types are supported: an existing fact metric, a fact table aggregation, or a raw warehouse table. This skill runs warehouse queries but never changes GrowthBook configuration; it does not create metrics, fact tables, or dashboards.
All API calls go through the bundled helper. Under the Claude Code plugin install, it lives at ${CLAUDE_PLUGIN_ROOT}/scripts/gb-call (the plugin root). Under npx skills install, it lives at scripts/gb-call relative to this skill's directory. It expects GB_API_KEY in env.
Workflow
1. Pick a datasource
Explorations are scoped to one SQL datasource. If a datasource is already established in this conversation, reuse it.
gb-call GET /api/v1/data-sources
- 0 datasources → tell the user none is configured and stop.
Reviews
Log in to leave a review.
No reviews yet — be the first.
Explore related
Other things in this space — across every part of the ecosystem, not just skills.
Skillssimilar to this one
All skills →create-atomic-tool
Build a `BaseTool[InSchema, OutSchema]` subclass — input/output schemas, `BaseToolConfig`, `run()` (and optional `run_async()`), env-driven secrets, typed failure outputs. Use when the user asks to "add a tool", "create a tool", "wrap an API as a tool", "build a `BaseTool`", "make a calculator/searc
6.1K stars
create-atomic-context-provider
Build a `BaseDynamicContextProvider` that injects a named, titled block into an agent's system prompt at every `run()` — current time, user identity, retrieved RAG docs, session state, cached DB schema. Use when the user asks to "add a context provider", "inject X into the prompt", "give the agent d
6.1K stars
framework
Guide for the Atomic Agents Python framework — schemas, agents, tools, context providers, prompts, orchestration, and provider configuration. Use when code imports from `atomic_agents`, defines an `AtomicAgent`, `BaseTool`, or `BaseIOSchema`, or the user asks about multi-agent orchestration or LLM-p
6.1K stars
Plugins
All plugins →receipts
A personal Claude Code impact report for justifying your usage to a manager or a self-review: what you shipped, which projects it went to, and each project's share of your usage. Reads your ~/.claude/projects transcripts and runs read-only git locally; only counts and project names are sent to write
32.8K stars
atomic-agents
Comprehensive development workflow for building AI agents with the Atomic Agents framework. Includes specialized agents for schema design, architecture planning, code review, and tool development. Features guided workflows, progressive-disclosure skills, and best practice validation.
6.1K stars
databases-on-aws
Expert database guidance for the AWS database portfolio. Design schemas, execute queries, handle migrations, and choose the right database for your workload.
844 stars
MCP Servers
All mcp servers →Commands
All commands →altimate
Delegate a task to altimate-code, the specialised data-engineering CLI agent (warehouse access, column-level lineage, cross-DB, FinOps, query optimization)
windsor-types
Generate TypeScript type definitions for a Windsor.ai connector's data schema
design-survey
Interactive design session for a new survey — research goal, audience, hypotheses, questions, modality, output schema
Subagents
All subagents →explore
Read-only code explorer that the plugin's other agents dispatch to map a codebase — locate files, trace how a flow is wired, find every caller of a symbol, answer "where does X happen".
32.8K stars
scan-verifier
Restricted read-only verifier dispatched by the Claude Security scan workflow to vote on one candidate finding; not for direct invocation.
32.8K stars
scan-researcher
Restricted read-only vulnerability researcher dispatched by the Claude Security scan workflow; not for direct invocation or general exploration.
32.8K stars
Hooks
All hooks →PostToolUse (mcp__.*__find_columns|mcp__.*__get_dataset_columns|mcp__.*__get_dataset)
Pre-query column validation via schema caching. Catches unknown column errors before they hit the API by building a per-session schema cache from find_columns / get_dataset_columns / get_dataset results.
PreToolUse (mcp__.*__run_query)
Pre-query column validation via schema caching. Catches unknown column errors before they hit the API by building a per-session schema cache from find_columns / get_dataset_columns / get_dataset results.