dotplot-whats-changed

A comparison tool for checking a product’s current numbers against its previous report. It uses saved local snapshots of the same user data and measurement.

In plain words
What is it for?
Use it to compare results with last week or the previous report, check whether a fix worked, and judge whether changes in rates are large enough to matter.
Why use it?
It helps distinguish a real change from a misleading comparison caused by different data or too few users. It also makes clear when there is no earlier report to compare.

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

Made for: Claude Code, Codex.

Per session 86 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 566 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 $0.00086 $0.00566
Opus 5 $0.00043 $0.00283
Sonnet 5 $0.00017 $0.00113
Haiku 4.5 $0.00009 $0.00057

Measured yesterday against content hash 8d1ce8c48983, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 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-whats-changed/SKILL.md · 60 lines

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 → note says 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 purchase are 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.

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 · 60 lines · 86 tokens per session scan A 8d1ce8c48983

Subscribe to this mod's changes

dotplot-whats-changed is a skill published in the GitHub repository brownglasses/dotplot-mcp (1 stars, last pushed 19d ago), licensed MIT. It adds 86 tokens to every session and 566 once invoked, about $0.0004 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.

Related

Other skills, from other repositories

data-analysis

业务数据分析助手(电商/增长方向)。连接 MySQL 数据库或读取 CSV/Excel,完成需求澄清 → SQL 查询 → 数据体检 → 全自动清洗 → 自动 EDA 探索 → 漏斗/留存/复购/RFM/指标异动归因分析 → 可视化 → 结论先行分析报告的完整流程;内置只读安全防护、MySQL 8 SQL 模板、10 个开箱即用脚本和演示电商数据,对新手友好。当用户提到分析数据、查数据库、写 SQL、漏斗、留存、转化率、GMV、DAU、复购、用户分群、指标为什么涨/跌、做报表,或给出任何 csv/xlsx/数据库数据想找业务结论时都应使用——用户不必说出"数据分析"四个字。.

drtgryhf-svg/growth-compass · 198 tokens

call-coach

B2B discovery call coach. Paste any transcript — Granola export, Fireflies, Fathom, plain text, a file path — and get a full coaching report: framework flags, gaps the rep failed to build, admissions captured, and a complete next-call script. Uses local memory files to track deals across multiple calls so coaching…

NachoLafuente/5050-gtm · 127 tokens

cohort-analysis

Build a full SaaS cohort analysis from a CRM (Attio/Stripe/CSV) joined to revenue (Stripe/Attio/CSV). Outputs a styled Excel workbook with conditional formatting (Customer Churn, MRR Churn, CAC Payback) plus per-section CSVs. Use when the user says "/cohort-analysis", "build a cohort table", "cohort analysis for "…

NachoLafuente/5050-gtm · 110 tokens

attio-deepline-enrich

Enrich records inside Attio with a clear cost gate before any credit is spent. Pull a chosen object (companies, people, or custom) from Attio, fill chosen attributes via Deepline providers (emails, phones, firmographics, LinkedIn, ICP fields), show the exact credit and dollar cost, wait for an explicit yes, run it…

NachoLafuente/5050-gtm · 155 tokens

cfo-skill

Read-only CFO data dashboard for bootstrapped startups. Two modes, CSV templates (works with any stack: Attio/HubSpot/Salesforce + Qonto/Mercury/Brex + Stripe + Moss/Ramp/Pleo) or live API pull from Attio + Qonto + Stripe + Moss. Computes runway, burn rate, MRR/ARR, NRR, customer concentration, AR aging, DSO, vendor…

NachoLafuente/5050-gtm · 170 tokens

linkedin-self-improvement-loop

A build-measure-learn loop for your LinkedIn. Ingests your Creator analytics export, keeps a persistent belief model of what drives your reach and engagement, reconciles last cycle's beliefs against the new data, proposes ONE experiment to run next, and hands draft briefs to a drafting skill. Run it on a cadence and…

NachoLafuente/5050-gtm · 136 tokens