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 skills add brownglasses/dotplot-mcp --skill dotplot-whats-changedgit clone --depth 1 https://github.com/brownglasses/dotplot-mcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/brownglasses/dotplot-mcp/dotplot-whats-changed)<a href="https://agentmods.dev/skills/brownglasses/dotplot-mcp/dotplot-whats-changed"><img src="https://agentmods.dev/badge/skills/brownglasses/dotplot-mcp/dotplot-whats-changed/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/brownglasses/dotplot-mcp/dotplot-whats-changed"><img src="https://agentmods.dev/badge/skills/brownglasses/dotplot-mcp/dotplot-whats-changed.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00086 | $0.00566 |
| Opus 5.5 | $0.00034 | $0.00226 |
| Sonnet 5.5 | $0.00017 | $0.00113 |
| Haiku 4.5 | $0.00009 | $0.00057 |
Grade A, and why
dotplot-whats-changed 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 2mo ago.
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.
What it actually says
What changed since last time
Snapshots are saved locally to ./.dotplot/history.json every time a report is
made. This reads them.
1. Use the same data source and the same value event
A comparison is only meaningful if both sides were measured the same way. Pull
the events the same way as before (same query, same tables), and pass the same
value_event — switching from purchase to signup between runs produces a
dramatic-looking change that means nothing.
Call history_compare, or analyze (which saves a new snapshot and shows the
comparison card in the report).
2. Trust the tool when it says there's nothing to compare
Only snapshots of the same dataset are compared — matched by a fingerprint of the user IDs, not by the file name or the event name. So:
- First run on this product →
notesays there's no history yet. Say that plainly. Don't imply nothing changed. - Analyzed a different product from the same folder → it won't be compared, on
purpose. Two products both using the event name
purchaseare not the same product.
3. Read the size of the move, not just the direction
Every rate in the comparison has a denominator. With 30 users, one person changes churn by 3 points — a "5 point improvement" is two people changing their minds. State the user counts alongside the percentages.
A move is worth acting on when it's larger than a few users' worth of noise, and when you can point at something that changed in the product to explain it.
4. Connect it to what they did
The useful version of this answer is not "regular_rate went from 8% to 14%". It is "the onboarding change you shipped on the 5th is followed by more users reaching a second week — worth continuing, though it's 25 users so far."
If they shipped nothing in between, say that too. Metrics drift on their own, and reading meaning into that drift is how teams end up chasing noise.
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.
- 2mo ago First seen · 60 lines · 86 tokens per session scan A 8d1ce8c48983
dotplot-whats-changed is a skill published in the GitHub repository brownglasses/dotplot-mcp (1 stars, last pushed 2mo ago), licensed MIT. It adds 86 tokens to every session and 566 once invoked, about $0.0003 per session on Opus 5.5. 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.
dotplot-whats-changed compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| dotplot-whats-changedbrownglasses/dotplot-mcp | Skill | 1 | 2mo ago | MIT | A | 86 | Claude Code |
| data-analysisdrtgryhf-svg/growth-compass | Skill | 1 | 2mo ago | MIT | A | 198 | Claude Code, Codex |
| call-coachNachoLafuente/5050-gtm | Skill | 3 | 4mo ago | MIT | A | 127 | Claude Code, Codex |
| cohort-analysisNachoLafuente/5050-gtm | Skill | 3 | 4mo ago | MIT | A | 110 | Claude Code, Codex |
| attio-deepline-enrichNachoLafuente/5050-gtm | Skill | 3 | 4mo ago | MIT | A | 155 | Claude Code, Codex |
| cfo-skillNachoLafuente/5050-gtm | Skill | 3 | 4mo ago | MIT | A | 170 | Claude Code, Codex |
| linkedin-self-improvement-loopNachoLafuente/5050-gtm | Skill | 3 | 4mo ago | MIT | A | 136 | Claude Code, Codex |