product-analytics
Analyze bounded product funnel and retention evidence with deterministic rates, explicit measurement definitions and source-bound recommendations; expose…
Skill by runxhq
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/runxhq-product-analytics
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
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About
Use cases: data
Reviews
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Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
- Runs as
- Node.js package
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/product-analytics
What's inside
- Skill name
product-analytics- Description
- Analyze bounded product funnel and retention evidence with deterministic rates, explicit measurement definitions and source-bound recommendations; expose instrumentation and comparison gaps without claiming lift.
- License
- Apache-2.0
- Instructions
- 64 lines in SKILL.md · view on GitHub
Before installing
- Ships executable scripts
Files (13)
- SKILL.md
- analyzes-activation.yaml · fixtures/
- composes-in-graph.yaml · fixtures/
- holds-invalid-funnel.yaml · fixtures/
- holds-mismatched-definition.yaml · fixtures/
- composed.graph · fixtures/support/
- withholds-unbound-action.yaml · fixtures/
- mark.svg
- product-analytics.mjs
- product-analytics.test.mjs
- catalog.md · references/
- mark-license.md · references/
- run.yaml
SKILL.md (first lines)
# Product analytics Explain where a defined product journey loses people or accounts, what the retention evidence shows, and which measurement or product change to consider. This skill covers activation and product usage. `organic-growth` owns search and acquisition strategy; it may consume the relevant findings. `analyze` accepts supplied governed aggregate counts. Retrieve a saved insight through a selected PostHog connector, or use another provider, before invoking this skill. Preserve the actual report and its source digest; do not invent counts to fit the input. This runner does not query a provider, execute SQL, capture events, change instrumentation or create tasks. ## Measurement admission Each report carries the definition its own source was computed under: product, timezone, person/account identity, cohort, filters, funnel rule and retention rule. Read it from that report's saved query; never copy one report's definition onto another. Funnel counts must count the same eligible entities through ordered steps, not independent event totals. Retention needs an explicit eligible cohort and return period. Mark incomplete provider results incomplete. Retain observation time; a cached PostHog result is not fresh merely because its HTTP read happened today. The pure calculation step preserves counts and computes rates. Zero eligible entities produce `null`, not a measured 0% conversion or retention rate. Ordered counts cannot increase and retained entities cannot exceed the eligible cohort. Compare a baseline only when its definition equals the current definition field for field, with the same ordered events and retention period over an equal-duration, nonoverlapping window. Rate changes are percentage points, never mislabeled relative percentage lift. Missing denominators, incompatible definitions or incomplete reports withhold recommendations and expose the measurement gap. ## Interpretation Use the deterministic rates; do not replace or round them into different facts. Distinguish observations, hypotheses and actions. Concentrate on the consequential drop-off, plausible instrumentation problems and a small next experiment. A change between periods does not establish causality or statistical significance. Counts alone cannot establish sample comparability, seasonality or experiment validity. Do not average cohort percentages or claim significance without the actual method and experiment evidence. State those limits when relevant. Each material finding and proposed action must cite admitted source digests. Recommend a concrete measurement, owner role and stop/reconsider condition; do not invent a named owner, deadline, revenue impact or work already completed. Native evidence verification binds the resulting review to the exact measurement context. The calling application owns continuing work, scheduling and approved changes. ## Agent task contract ### `product-analytics-analyze` Return `analytics_candidate` with `decision: ready`, the exact `objective`, a concise `summary`, `findings` and `limitations`. Each finding has `claim`, `kind` (`observed`, `hypothesis` or `action`) and `source_digests`. An action also has `measurement`, `owner_role` and `stop_condition`. Every finding and action is verified through the same native claim binding. Cite only admitted digests. Explain zero denominators and uncertainty plainly. Return `needs_more_evidence` when the bounded evidence cannot support useful recommendations. Never claim execution.
From runxhq/runx, Apache-2.0.
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Signals
- Stars
- 104
- Forks
- 109
- Open issues
- 16
- Contributors
- 16
- Last commit (this folder)
- Oct 17, 2026 (11 days ago)
- Last release
- Oct 18, 2026 (11 days ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- Apache-2.0
- Repository
- github.com/runxhq/runx
- Website
- runx.ai
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Cite this page
product-analytics skill. Agent Mart. https://agentmart.dev/skills/runxhq-product-analytics (last updated 2026-10-24)