Task To Pr

Community

owainlewis

task-to-pr

Delivers specs or decided tasks as tested, independently reviewed pull requests. Runs the CI and review repair loop, then leaves passing pull requests open unless merging was explicitly authorized.

Overview

Publisher	owainlewis
Repository	`blueprint`
Skill name	`task-to-pr`
Stars	419
Forks	73
Bundled files	1
License	MIT
Links	Skill source Repository Homepage

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.
1 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by owainlewis on GitHub. Read the source before you install it.

Installation

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

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/owainlewis/blueprint/tree/main/skills/task-to-pr
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.

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/owainlewis/blueprint.git /tmp/blueprint
mkdir -p .claude/skills
cp -r /tmp/blueprint/skills/task-to-pr .claude/skills/task-to-pr

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Task To Pr 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 Task To Pr 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 Task To Pr 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.

Task to PR

Deliver each task as one focused pull request with proof. The spec is the ticket; accept a Markdown spec, GitHub issue, or decided task with acceptance checks. Do not create a second work definition.

Read required skills directly from their SKILL.md files as instructions for this workflow. Do not invoke them through a skill command that the client blocks.

Prepare

Read the source, repository instructions, relevant requirements and architecture, code, and tests. Reuse an existing branch, worktree, and pull request when continuing work.
Confirm the outcome, scope, decisions, and checks are sufficient. If a consequential choice is missing, return to /spec or report the needed decision before implementing. Do not write specs for small, decided work.
Split larger specs with /plan when useful. Each task delivers one working result. Identify dependencies and start independent work together when useful.

Deliver and repair

Create or reuse an isolated branch and worktree. Start independent work from the latest remote default branch. Follow repository naming and worktree rules.
Implement the task and meaningful tests for changed behavior and affected failures. Update requirements, architecture, and the spec when the change affects them. Keep intended architecture and implementation status accurate.
Use /test to prove acceptance and affected rules. Fix failures before committing.
Create a Conventional Commit, push, and open or update the pull request. Follow the repository PR template. Lead with the problem and result; include proof and the source link. Mark it ready for review and move a tracker ticket to Review when supported.
Use /review with a fresh subagent that did not implement the change. Wait for configured CI and automated review on the current commit.
Fix valid findings and failures caused by the change. Reply to review findings with the fix or evidence for making no change. Resolve a thread only when fully addressed.
After changing the implementation, repeat affected /test checks and fresh /review, commit, push, and wait for CI and automated review again. Update PR proof. Continue until all available checks pass and no actionable finding remains.
Report a blocker when progress requires a missing decision, permission, unavailable check, or external repair. Do not weaken tests, bypass repository rules, or claim unrun checks passed.
Record final proof and the PR link in the source spec or task. Update delivery status to reflect the actual result, including whether the PR is open or merged. Update the original tracker item when one exists. Leave passing PRs open for human review by default. Pending human approval is a merge blocker, not a failed implementation check.

Dependencies

Start dependent work only after each prerequisite has an open PR, a fresh Approve review, and no known blocking finding. Stack its branch on the reviewed prerequisite. If there are several prerequisites, combine them in dependency order. Keep each PR diff focused on its own task.

When a prerequisite changes or merges, update dependent branches and PR bases. Repeat affected tests, independent review, and CI against the new base.

Merge only with authority

Explicit /factory or /codex-issue-coordinator use grants their scoped merge authority. Otherwise merge only when the user asks. Automatic skill selection does not grant it. Before merging, require:

Complete scope and acceptance proof on the final commit.
Passing tests, required CI, and a fresh /review verdict of Approve.
No unresolved actionable review finding or consequential decision.
Every repository-required approval.
A ready, mergeable PR current with its required base.

Merge in dependency order using the repository's preferred method. Never bypass branch protection. Confirm GitHub reports the merge, then update delivery status in the source spec or task and update the original issue and project state. Remove manually created worktrees after merge or close; use the host's lifecycle tools for managed worktrees. Keep open-PR worktrees. Merge authority does not authorize deployment or release publication.

Return

Report each PR, tests, review verdict, CI state, and blocker. Stop with passing PRs open unless scoped merge authority was given. In merge mode, report verified merges and any work that still needs human action.

Bundled files

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

agents/openai.yaml1 KB

Frequently asked questions

What does the Task To Pr AI skill do?

Delivers specs or decided tasks as tested, independently reviewed pull requests. Runs the CI and review repair loop, then leaves passing pull requests open unless merging was explicitly authorized.

Why use Task To Pr on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/owainlewis/blueprint/tree/main/skills/task-to-pr. 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 Task To Pr?

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 Task To Pr?

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

Is the Task To Pr AI skill free?

Yes. It is published on GitHub by owainlewis 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.

View all

Solid

openclaw

OrganizationPopular

Write, port, review, or test Solid 2 UI in OpenClaw (Control UI, plugin views, Canvas hosts), including state ownership, Lit interop during the migration, native overlays, testing, performance, and…

Auto Qa

Run OpenClaw-wide autonomous QA and live/stress campaigns across independent subsystem lanes, with verified fixes and a resumable evidence report.

Autoreview

Structured code review when explicitly requested, preferring OpenAI/Codex before Claude.

Claw Score

Audit or refresh OpenClaw maturity scorecard docs from root taxonomy, maturity scores, and QA evidence artifacts without using maintainer discrawl data or committed inventory reports.

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Task to PR

Prepare

Deliver and repair

Dependencies

Merge only with authority

Return

Bundled files

Frequently asked questions

Related AI Skills

Set up your own AI workspace now