Pr Description logo

Pr Description

Organization
vstorm-co
pr-description

Create or update PR. Use this skill every time user asks to create or update a PR to ensure clear, consistent descriptions between PRs.

Overview

Publishervstorm-co
Repositoryagenticos
Skill namepr-description
Stars
137
Forks
27
Bundled files
Instructions only
LicenseApache-2.0
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 vstorm-co on GitHub. Read the source before you install it.

Installation

Install the Pr Description 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/vstorm-co/agenticos/tree/main/.claude/skills/pr-description
  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/vstorm-co/agenticos.git /tmp/agenticos
mkdir -p .claude/skills
cp -r /tmp/agenticos/.claude/skills/pr-description .claude/skills/pr-description
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Pr Description 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 Description 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 Description 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.

Skill: PR Description

Create or update PR description based on git diff.

Inputs

  • PR number — required when updating an existing PR; provided by the user (referred to as <pr-number> below)
  • Base branch — detect dynamically; do not assume main:
    bash
    BASE=$(gh repo view --json defaultBranchRef -q .defaultBranchRef.name)
    If the user names a different base (e.g. develop), use that instead.

Steps

  1. Gather context:

    • Refresh the base first: git fetch origin "$BASE". gh repo view hands back a branch name; it does not update the local remote-tracking ref, and a stale origin/$BASE describes commits that merged upstream days ago as if they were this PR's.
    • For new PR: git diff "origin/$BASE"...HEAD and git log "origin/$BASE"..HEAD --oneline
    • For existing PR: gh pr diff <pr-number>, and its current body: gh pr view <pr-number> --json title,body. An update is a merge, not a rewrite: a body often carries hand-written limitations, verification evidence and issue links that the diff cannot reconstruct, and --body-file replaces all of it silently. Carry that content forward, and only rewrite what the diff has actually made wrong.
  2. Generate description in this format:

markdown
## Changes

### Summary

[Brief summary of changes - 1-3 sentences]

### Business Context

[Business/technical context - why was this change needed. Infer from branch name, commits, and code changes. Remove this section if unclear.]

### Changes

[Technical approach - main architectural decisions, main changes. Use bullet points.]

### Architecture

[Diagrams, for an architectural change only — see Rules. Reference each attached image as `![alt](./name.png)` with one sentence saying what the reader should take from it. Remove this section for a change with no architectural shape.]

### Verification

[Example queries, commands, or steps to verify the changes work correctly. When the change is observable through the project's own tracing/observability backend (new or changed spans, telemetry attributes, logged events — check whether the repo has one before assuming), include links or excerpts from example traces you actually produced — see Rules.]

### Linked Issues

[Link to any relevant issues or tickets found in branch name or commits. Remove this section if none found.]
  1. Execute (write the body to a file and pass --body-file; a long body with backticks does not survive --body "…" intact):

    • New PR: gh pr create --base "$BASE" --title "<title>" --body-file body.md
    • Existing PR: gh pr edit <pr-number> --body-file body.md
    • Diagrams go in the body as mermaid, so there is nothing to upload and no asset URL to go stale. gh has no image-upload flag - --attach is not a flag gh pr create or gh pr edit has ever exposed - and a local PNG referenced by path renders as a broken image for every reviewer.
  2. Return the PR URL.

Rules

  • No "Test plan" or "Generated with..." sections
  • Keep descriptions concise
  • Use present tense ("Add", "Update", "Remove")
  • Omit Business Context and Linked Issues sections when there's nothing to fill in — don't leave placeholders
  • Include trace/log evidence for observable changes, when the repo has the means to produce it. When the change produces or alters observability (new/changed spans, telemetry attributes, structured logs) and the project has a tracing or logging backend, link to or excerpt example traces/log lines you actually produced in the Verification section — one per meaningful path (happy path + key error paths), using that backend's own link format (e.g. a dashboard deep-link, a log query, a captured span tree). Only cite evidence you genuinely produced — never fabricate ids, URLs, or output. If the project has no such backend, or the change isn't observable through one, skip this and verify with the project's normal test/run commands instead.
  • Attach diagrams for an architectural change. When a change adds or reshapes a flow across components — a new tool or service path, a new gate, a state machine, a cross-channel behaviour — put images in the Architecture section rather than making the reviewer rebuild the shape from the diff. Two usually suffice: one component/flow diagram (who calls what, and what each outcome writes) and one sequence diagram (the call order, with the refusal branches). Put the mermaid straight in the body, in a fenced ```mermaid block: GitHub renders it in a pull request description, so the diagram needs no renderer, no uploaded asset and no version of anything. It also stays readable in the diff and as text, which a PNG does not. A committed architecture doc's diagram belongs wherever this repo keeps those instead, e.g. docs/architecture.md, a docs/architecture/ directory, or an ADR. Diagram the mechanism the PR actually changes, including its failure paths; a box-and-arrow restatement of the module tree earns nothing. Skip the section for a fix, a test-only change, or a one-file tweak.

Frequently asked questions

What does the Pr Description AI skill do?

Create or update PR. Use this skill every time user asks to create or update a PR to ensure clear, consistent descriptions between PRs.

Why use Pr Description on TypingMind?

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

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

Which AI models can use Pr Description?

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 Description?

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

Is the Pr Description AI skill free?

Yes. It is published on GitHub by vstorm-co under the Apache-2.0 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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