Dot Pr Spec Summary logo

Dot Pr Spec Summary

Organization
dotCMS
dot-pr-spec-summary

Summarize a spec PR (or any spec document) in simple words — proposed fix, scope, implications, and test coverage — so a reviewer can decide whether to approve without reading the full spec. Use when the user shares a PR URL/number or a spec document and asks to review, summarize, or understand it. Typical input is a Spec-Kit "PR 1 of 2" carrying a spec.md. Works in English and Spanish.

Overview

PublisherdotCMS
Repositorycore
Skill namedot-pr-spec-summary
Stars
971
Forks
487
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by dotCMS on GitHub. Read the source before you install it.

Installation

Install the Dot Pr Spec Summary AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/dotCMS/core/tree/main/.claude/skills/dot-pr-spec-summary
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/dotCMS/core.git /tmp/core
mkdir -p .claude/skills
cp -r /tmp/core/.claude/skills/dot-pr-spec-summary .claude/skills/dot-pr-spec-summary
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Dot Pr Spec Summary in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Dot Pr Spec Summary on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Dot Pr Spec Summary is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Spec Review Summary

Purpose

Produce a concise, plain-language review summary of a specification so the reader can decide quickly whether to approve it, without reading the full spec. The typical input is a dotCMS Spec-Kit "PR 1 of 2" carrying a spec.md, but any spec PR or document works.

This skill is for the reviewer of a spec someone else wrote. It is complementary to the speckit-* skills, which operate inside the spec authoring workflow.

Inputs

The user may share any combination of:

  • A GitHub PR URL or number (e.g. https://github.com/dotCMS/core/pull/37190 or 37190)
  • Related GitHub issue links
  • Local file paths or pasted documents

Workflow

  1. Fetch the PR (if a PR was given):
    • gh pr view <number> --repo <owner/repo> --json title,body,state,author,files,additions,deletions,baseRefName,headRefName
    • gh pr diff <number> --repo <owner/repo> to read the actual spec content
    • Infer the repo from the URL; default to dotCMS/core when only a number is given.
  2. Follow the trail: if the PR body or spec references a driving issue, parent epic, or investigation issue and the summary would be materially better for it, fetch those with gh issue view — but only what's needed; don't crawl everything.
  3. Read any extra docs the user shared (local files with Read, URLs with WebFetch).
  4. Classify the PR first: is it spec-only (docs, no code) or does it carry implementation? State this up front — it changes what "reviewing" means.

Output format

Lead with a TL;DR paragraph: what the PR is (spec-only vs. code), what it adds, and the one-sentence essence of the proposal.

Then these sections, in prose (short paragraphs and bullets — no walls of headers for a simple spec):

  • The problem, in simple words — the underlying issue as a story a non-expert teammate can follow. Include the key numbers (measurements, counts, thresholds) because they carry the argument.
  • The proposed fix — what will actually change and why this approach was chosen over alternatives the spec considered and rejected. Note anything explicitly optional/descopable.
  • Scope — explicitly split in scope vs. out of scope, including hypotheses ruled out and related findings deliberately not pursued.
  • Implications — blast radius (shared components touched beyond the surfacing feature), behavior-change guarantees, accepted residual risks/limitations, backward compatibility, and any trade-off the spec is quiet about (memory, performance, migration).
  • Test coverage — what tests exist in THIS PR (for spec-only PRs: typically none, by design — say so and cite the TDD/process rule if applicable), and what the spec's success criteria / acceptance scenarios commit future tests to prove. Flag success criteria that are qualitative ("low count", "a handful") and will need pinning down. Describe each criterion or scenario by what it checks ("the page loads with one query instead of hundreds"), never by its spec identifier (see Style rules).

Close with anything a reviewer should push back on: ambiguities, unstated assumptions, missing sections, or scope creep. If there is nothing, don't invent it.

Visuals

The goal is clarity: a cold reader should grasp the whole picture from the visuals alone. Skip visuals entirely when the spec is trivial or purely textual (a naming/policy change) and a picture would add nothing; don't force one.

Mermaid diagram (always, when a visual helps). One mermaid diagram of the core mechanism, ~5–12 nodes, real names (config keys, classes, endpoints) and real numbers, quoted labels where they contain parentheses/colons. Node and edge labels describe the step in words; no spec identifiers (FR-012, SC-001) as labels. Include it in the chat summary as a ```mermaid fence — it renders if pasted into GitHub (PR comments render mermaid natively).

Designed SVG visuals (only in HTML deliverables — see below). When producing an HTML deliverable, build:

  • A mechanism diagram as hand-authored inline SVG. Prefer a before/after layout: the same pipeline drawn twice ("Today" / "Proposed"), unchanged parts explicitly marked "unchanged", the one thing that changes tagged (e.g. a "the only change" pill), and the costly path drawn as a visually heavy arrow that becomes thin/dashed in the after panel. Show real names and real numbers on the marks. Draw capacities/quantities as proportional shapes when possible (e.g. a gauge that visibly overflows), not just as text. Wrap in <figure> with a <figcaption> stating the claim and give the <svg> role="img" + aria-label. Inline SVG needs no JavaScript: it renders offline in any browser.
  • A small chart and/or stat tiles whenever the spec's argument rests on measurements. A number pair like "~688 → 1–2 queries" is a pair of stat tiles; a capacity-vs-population claim is a bar chart with a dashed threshold line. Direct-label everything; single hue + neutral; text in ink tokens, never in series color.
  • If diagramming or data-viz helper skills are available in your environment (e.g. artifact-diagramming, dataviz), load them before drawing; if not, follow the guidance above directly.
  • Also embed the mermaid source in the HTML inside a collapsed <details><summary>Mermaid source — paste into GitHub</summary> block, so the copy-pasteable source travels with the document without duplicating the SVG's story.

Deliverables

Adapt to what your environment supports — the chat summary is the one non-negotiable deliverable.

  1. Chat summary (always) — the sections above, with the ```mermaid fence inline.
  2. Standalone HTML file (when the spec is substantial enough that visuals help) — a self-contained .html page with the same sections and the SVG visuals as primary figures (each in its own overflow-x: auto panel). Requirements:
    • Full document skeleton (<!DOCTYPE html>, <html>, <head> with <meta charset="utf-8">, <body>).
    • For the collapsed mermaid-source block, load mermaid from CDN (https://cdn.jsdelivr.net/npm/mermaid@11/dist/mermaid.esm.min.mjs, startOnLoad: true, theme matched to prefers-color-scheme). Tell the user the mermaid rendering needs internet; the SVG figures do not.
    • Clickable links to the spec PR, its driving issue, and any parent epic/investigation issues referenced (a footer "References" line works well).
    • Name the file after the PR (e.g. spec-review-pr-37190.html) and write it somewhere outside the repo working tree (a temp/scratch directory), then tell the user the path — or deliver it through your client's file-sending mechanism if it has one. Never leave it in the repo where it could be committed accidentally.
  3. Hosted/shareable page (optional, only if your client supports publishing artifacts) — publish the same page and give the user the link. Reuse the same artifact URL when re-reviewing the same PR, so one spec keeps one page.

Verifying rendering (when browser tooling is available): render the HTML file and screenshot each <figure>, eyeballing for label collisions/overflow. If your browser tooling blocks file: URLs, serve the directory with python3 -m http.server in the background, navigate, verify, then kill the server and remove any tool-generated folders (e.g. .playwright-mcp) from the repo. Known quirk: that server sends no charset header, so em-dashes/× may appear garbled in test screenshots — that's the test harness, not the page; the standalone file carries its own <meta charset>. If no browser tooling is available, skip verification and say so.

Style rules

  • Simple words. Explain jargon inline the first time it appears (e.g. "N+1 pattern — one query per row instead of one query total").
  • Keep the key measurements and identifiers exact (query counts, config keys, class names).
  • No spec-internal reference codes. Never carry identifiers like FR-012, SC-001, AC-002, US-3, NFR-4 or T-017 into the summary, the visuals, or the HTML page. They only mean something with the spec open, which is exactly what the reader is trying to avoid. Say what the item is instead: "the requirement that saving a page never blocks on the cache" rather than "FR-012". Prose such as "the third acceptance scenario" is fine when the position matters; a bare code is not. The one exception is the reviewer's closing push-back list, where a code may follow the plain-language description in parentheses so the author can find the line to fix.
  • Link every PR/issue mentioned as a markdown link.
  • Match the user's language (English or Spanish).
  • Selective, not compressed: drop detail that doesn't change the approve/request-changes decision, but write what remains in full sentences.

Frequently asked questions

What does the Dot Pr Spec Summary AI skill do?

Summarize a spec PR (or any spec document) in simple words — proposed fix, scope, implications, and test coverage — so a reviewer can decide whether to approve without reading the full spec. Use when the user shares a PR URL/number or a spec document and asks to review, summarize, or understand it. Typical input is a Spec-Kit "PR 1 of 2" carrying a spec.md. Works in English and Spanish.

Why use Dot Pr Spec Summary on TypingMind?

Because you install it once and use it with any model. Dot Pr Spec Summary is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Dot Pr Spec Summary in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/dotCMS/core/tree/main/.claude/skills/dot-pr-spec-summary. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Dot Pr Spec Summary?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Dot Pr Spec Summary?

As many as you like. As long as a model supports skills, you can use Dot Pr Spec Summary with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Dot Pr Spec Summary AI skill free?

It is published on GitHub by dotCMS. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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