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.
npx agentmods add skills/brownglasses/dotplot-mcp/dotplot-analyze-productnpx skills add brownglasses/dotplot-mcp --skill dotplot-analyze-productgit clone --depth 1 https://github.com/brownglasses/dotplot-mcpWhat 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.
| Model | Per session | Once 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 |
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.
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_URLset? 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 did —
orders,sessions,posts,messages,subscriptions. Aneventstable 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 byUNION 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, translateget_report_stringsand usegenerate_reportwithlang="custom".
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.
- yesterday First seen · 81 lines · 135 tokens per session scan A 07e51d4c71d2
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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