dotplot-analyze-product

A workflow for analyzing real user behavior with Dot Plot, a product analytics service. It finds event data, chooses an action that represents delivered value, and produces reports about the aha moment, onboarding, and weekly retention.

In plain words
What is it for?
Use it to assess how users reach value, inspect onboarding drop-off, measure retention, and decide what product work to do next.
Why use it?
It prevents guesses based on vanity metrics by requiring the analysis to use recorded product actions and by stating plainly when useful tracking data is missing.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/brownglasses/dotplot-mcp/dotplot-analyze-product
Any agent
npx skills add brownglasses/dotplot-mcp --skill dotplot-analyze-product
Clone the repo
git clone --depth 1 https://github.com/brownglasses/dotplot-mcp

Made for: Claude Code, Codex.

Per session 135 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 813 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00135 $0.00813
Opus 5 $0.00068 $0.00407
Sonnet 5 $0.00027 $0.00163
Haiku 4.5 $0.00014 $0.00081

Measured yesterday against content hash 07e51d4c71d2, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

dotplot-analyze-product scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/dotplot-analyze-product/SKILL.md · 81 lines

How it starts

The opening of the file, as written. The whole thing — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Analyze this product

The statistics come from the dotplot MCP server, never from you. Your job is finding the data, choosing well, and explaining what it means.

1. Find the event data

Call analyze with no arguments first if you don't already know where the data is — it returns the procedure. In short:

  • Is DOTPLOT_DB_URL set? Is there a connection string in .env? Is a Postgres/Supabase MCP already connected?
  • Read the schema. You're looking for tables recording things users didorders, sessions, posts, messages, subscriptions. An events table is nice but most early products don't have one, and that is fine.
  • Turn those tables into events with load_from_db, one SELECT per action joined by UNION ALL:
SELECT user_id, created_at::date AS date, 'purchase' AS event FROM orders
UNION ALL
SELECT user_id, added_at::date, 'add_to_wishlist' FROM wishlist_items

If there's no database and nothing is tracked, say so plainly and switch to /dotplot-add-tracking. Don't invent a report.

2. Choose the value event yourself when you can

analyze picks the most-repeated non-vanity action, and says why. But you have read the codebase, so you know things the code cannot: that purchase is value and view_item is browsing, even though they look identical in the numbers. When you know better, pass value_event explicitly.

Check the result's value_event.why and others_available. If the choice looks wrong, call again — it's cheap.

3. Report it like a person

Open the report, then say the headline out loud. Lead with what to do, not with the numbers.

Three things to get right:

  • The aha moment is a correlation. Say so. "Users who did X became regulars" can mean X causes retention, or that engaged users do everything. The honest recommendation is to put it in onboarding and A/B test it — not to declare it.
  • Small numbers deserve hedging. The report drops weeks with under five users, but a rate over 8 users is still thin. Mention the denominator when it's small.
  • Match their language. Pass lang="ko"/"ja"/"en"; for anything else, translate get_report_strings and use generate_report with lang="custom".

Read the full file on GitHub · 81 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. yesterday First seen · 81 lines · 135 tokens per session scan A 07e51d4c71d2

Subscribe to this mod's changes

dotplot-analyze-product is a skill published in the GitHub repository brownglasses/dotplot-mcp (1 stars, last pushed 19d ago), licensed MIT. It adds 135 tokens to every session and 813 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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