Pr Report logo

Pr Report

OrganizationPopular
paperclipai
pr-report

Produce a maintainer-grade PR or contribution report as HTML or Markdown. Use when asked to deeply review a PR, explain a design, compare systems, or prepare a merge recommendation.

Overview

Publisherpaperclipai
Repositorypaperclip
Skill namepr-report
Stars
95.4K
Forks
16.2K
Bundled files
2
LicenseMIT
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.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Pr Report 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/paperclipai/paperclip/tree/master/.agents/skills/pr-report
  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/paperclipai/paperclip.git /tmp/paperclip
mkdir -p .claude/skills
cp -r /tmp/paperclip/.agents/skills/pr-report .claude/skills/pr-report
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Pr Report 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 Pr Report 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 Pr Report 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.

PR Report Skill

Produce a maintainer-grade review of a PR, branch, or large contribution.

Default posture:

  • understand the change before judging it
  • explain the system as built, not just the diff
  • separate architectural problems from product-scope objections
  • make a concrete recommendation, not a vague impression

When to Use

Use this skill when the user asks for things like:

  • "review this PR deeply"
  • "explain this contribution to me"
  • "make me a report or webpage for this PR"
  • "compare this design to similar systems"
  • "should I merge this?"

Outputs

Common outputs:

  • standalone HTML report in tmp/reports/...
  • Markdown report in report/ or another requested folder
  • short maintainer summary in chat

If the user asks for a webpage, build a polished standalone HTML artifact with clear sections and readable visual hierarchy.

Resources bundled with this skill:

  • references/style-guide.md for visual direction and report presentation rules
  • assets/html-report-starter.html for a reusable standalone HTML/CSS starter

Workflow

1. Acquire and frame the target

Work from local code when possible, not just the GitHub PR page.

Gather:

  • target branch or worktree
  • diff size and changed subsystems
  • relevant repo docs, specs, and invariants
  • contributor intent if it is documented in PR text or design docs

Start by answering: what is this change trying to become?

2. Build a mental model of the system

Do not stop at file-by-file notes. Reconstruct the design:

  • what new runtime or contract exists
  • which layers changed: db, shared types, server, UI, CLI, docs
  • lifecycle: install, startup, execution, UI, failure, disablement
  • trust boundary: what code runs where, under what authority

For large contributions, include a tutorial-style section that teaches the system from first principles.

3. Review like a maintainer

Findings come first. Order by severity.

Prioritize:

  • behavioral regressions
  • trust or security gaps
  • misleading abstractions
  • lifecycle and operational risks
  • coupling that will be hard to unwind
  • missing tests or unverifiable claims

Always cite concrete file references when possible.

4. Distinguish the objection type

Be explicit about whether a concern is:

  • product direction
  • architecture
  • implementation quality
  • rollout strategy
  • documentation honesty

Do not hide an architectural objection inside a scope objection.

5. Compare to external precedents when needed

If the contribution introduces a framework or platform concept, compare it to similar open-source systems.

When comparing:

  • prefer official docs or source
  • focus on extension boundaries, context passing, trust model, and UI ownership
  • extract lessons, not just similarities

Good comparison questions:

  • Who owns lifecycle?
  • Who owns UI composition?
  • Is context explicit or ambient?
  • Are plugins trusted code or sandboxed code?
  • Are extension points named and typed?

6. Make the recommendation actionable

Do not stop at "merge" or "do not merge."

Choose one:

  • merge as-is
  • merge after specific redesign
  • salvage specific pieces
  • keep as design research

If rejecting or narrowing, say what should be kept.

Useful recommendation buckets:

  • keep the protocol/type model
  • redesign the UI boundary
  • narrow the initial surface area
  • defer third-party execution
  • ship a host-owned extension-point model first

7. Build the artifact

Suggested report structure:

  1. Executive summary
  2. What the PR actually adds
  3. Tutorial: how the system works
  4. Strengths
  5. Main findings
  6. Comparisons
  7. Recommendation

For HTML reports:

  • use intentional typography and color
  • make navigation easy for long reports
  • favor strong section headings and small reference labels
  • avoid generic dashboard styling

Before building from scratch, read references/style-guide.md. If a fast polished starter is helpful, begin from assets/html-report-starter.html and replace the placeholder content with the actual report.

8. Verify before handoff

Check:

  • artifact path exists
  • findings still match the actual code
  • any requested forbidden strings are absent from generated output
  • if tests were not run, say so explicitly

Review Heuristics

Plugin and platform work

Watch closely for:

  • docs claiming sandboxing while runtime executes trusted host processes
  • module-global state used to smuggle React context
  • hidden dependence on render order
  • plugins reaching into host internals instead of using explicit APIs
  • "capabilities" that are really policy labels on top of fully trusted code

Good signs

  • typed contracts shared across layers
  • explicit extension points
  • host-owned lifecycle
  • honest trust model
  • narrow first rollout with room to grow

Final Response

In chat, summarize:

  • where the report is
  • your overall call
  • the top one or two reasons
  • whether verification or tests were skipped

Keep the chat summary shorter than the report itself.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Pr Report AI skill do?

Produce a maintainer-grade PR or contribution report as HTML or Markdown. Use when asked to deeply review a PR, explain a design, compare systems, or prepare a merge recommendation.

Why use Pr Report on TypingMind?

Because you install it once and use it with any model. Pr Report 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 Pr Report in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/paperclipai/paperclip/tree/master/.agents/skills/pr-report. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Pr Report?

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 Pr Report?

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

Is the Pr Report AI skill free?

Yes. It is published on GitHub by paperclipai under the MIT license. 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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