Prp Issue

CommunityPopular

Wirasm

prp-issue

Autonomously owns one workstream from an issue, PRD, document, existing plan, or free-form request through planning, implementation, pull request, independent review, corrections, and green CI. Always use when the user asks to implement or ship work end to end, take an issue or idea to a reviewed PR, run plan to PR, invokes /prp-issue, or when prp-orchestrate needs an end-to-end delivery engine.

Overview

Publisher	Wirasm
Repository	`prp`
Skill name	`prp-issue`
Stars	2.2K
Forks	607
Bundled files	Instructions only
License	MIT
Links	Skill source Repository

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 Wirasm on GitHub. Read the source before you install it.

Installation

Install the Prp Issue 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.
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/Wirasm/prp.git /tmp/prp
mkdir -p .claude/skills
cp -r /tmp/prp/plugins/prp-core/skills/prp-issue .claude/skills/prp-issue

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

Use it in TypingMind

Enable Prp Issue 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 Prp Issue 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 Prp Issue 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.

Deliver One Workstream

Own planning through PR and every correction in this context. Preserve accumulated reasoning across that implementation lifecycle; use fresh contexts only where independence is the feature—review.

Input: $ARGUMENTS (if absent, use the conversation).

Contract

Continue autonomously through plan, implementation, PR, review, correction, re-review, and CI.
Compose /prp-plan, /prp-implement, and /prp-review; do not reproduce their craft.
Keep the plan, implementation report, PR, review report, publication URL, validation, and CI as the workstream's proof. Never reduce a handoff to a private summary.
Stop only for a product decision, missing prerequisite primitive, inaccessible dependency, permission boundary, or repeated no-progress failure that cannot be resolved in this context.
Do not merge. The caller or outer orchestrator owns that gate.

1. Resolve and plan in this context

Accept an issue or tracker URL, PRD, document, existing .plan.md, free-form request, conversation context, or reviewed PR.

Review-only request or contributor PR: use /prp-review and stop.
Existing plan: use it; publish it first with /prp-plan publish <path> when issue-derived publication is missing.
Issue with a published plan: let /prp-implement resolve and persist its absolute path from source metadata.
Existing reviewed PR: resolve its plan and implementation report, then resume correction or verification without repeating completed work.
Every other input: invoke /prp-plan now in this context. Keep its reasoning available for implementation.

Require the absolute plan path and, for issue-derived plans, the verified publication URL before review.

2. Implement through PR in this context

Invoke /prp-implement with the plan path—or source issue when resolving a published plan—and any explicit base. Keep ownership in this context through validation, scoped commit, PR creation, linked PRD updates, and the implementation report.

Do not start review without VALIDATION: GREEN, the absolute plan and report paths, and a live PR.

3. Review in a fresh context

Start a fresh agent with this prompt:

Invoke /prp-review on <PR URL or number> with scopes <requested scopes, if any>. Applicable caller decisions and scope constraints, verbatim: <decisions or "None">. Read the linked plan and implementation report, publish the complete review to GitHub, and return the verdict, canonical review-report path, verified publication URL, and any blocker. Do not modify the PR.

Require the complete canonical review report and verified GitHub publication. Wait until all selected review agents have finished and the review coordinator has produced the complete canonical report before addressing any finding; never start correction from partial reviewer messages.

4. Disposition findings and re-review

Read the complete report in this implementation context and disposition every finding. Fix valid Critical or Important findings. Prefer fixing a valid Suggestion now when the correction is narrow, low-risk, aligned, and cheaper than another delivery cycle. Use NOT A FINDING with decisive evidence when it is false or already satisfied. Use TRACKED FOLLOW-UP only for clearly valuable, distinct work with a verified issue link. Use DECLINED for speculative defense-in-depth, overengineering, preference, or unclear or undesirable direction; record why and do not create an issue. Never leave a bare deferred state.

Batch every accepted correction and evidence-backed disposition into one coherent pass, then invoke /prp-implement in review-correction mode in this same context. Start one fresh /prp-review --verify-corrections agent with the previous reviewed head, current PR head, complete canonical report, and dispositions. Never start another review when neither the head nor disposition evidence changed. Do not wait for or check CI between rounds; CI clears once, at the end of the workstream, on the final head.

Repeat correction and focused verification only for an unresolved prior blocker, a disproven disposition, or a defect caused by the correction. Return to a full review only when the correction materially changed the PR's outcome, architecture, or scope. Continue until the independent verdict is READY TO MERGE and every finding has a terminal disposition. Resolve REVIEW INCOMPLETE by obtaining its missing validation or evidence; stop only when that is genuinely unavailable.

5. Require green CI

After READY TO MERGE, wait for every required CI check. A pending check is not green. For a PR-caused failure, invoke /prp-implement in CI-correction mode with the PR and complete failing-check evidence in this context, then run /prp-review --verify-corrections against the changed head. When no required CI exists, rerun the repository's authoritative local gate and record it instead.

6. Return proof and follow-ups

Only after review and CI are green, return the outcome, absolute plan and implementation-report paths, PR URL, latest review verdict, review-report path, publication URL, validation, and CI evidence. Then suggest only meaningful remaining non-blocking follow-ups, including already-created tracking issues; do not present required unfinished work as optional follow-up.

Frequently asked questions

What does the Prp Issue AI skill do?

Why use Prp Issue on TypingMind?

Because you install it once and use it with any model. Prp Issue 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 Prp Issue in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Wirasm/prp/tree/development/plugins/prp-core/skills/prp-issue. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Prp Issue?

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 Prp Issue?

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

Is the Prp Issue AI skill free?

Yes. It is published on GitHub by Wirasm 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Deliver One Workstream

Contract

1. Resolve and plan in this context

2. Implement through PR in this context

3. Review in a fresh context

4. Disposition findings and re-review

5. Require green CI

6. Return proof and follow-ups

Frequently asked questions

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