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Contributor

Community
majiayu000
contributor

End-to-end open source contribution workflow: from scanning issues to submitting PRs. Use this skill whenever the user wants to contribute to an open source project, find issues to fix, submit a pull request, fork a repo to contribute, fix a GitHub issue, or mentions 'open source contribution'. Also trigger when they provide a GitHub repo URL and ask about contributing, say things like 'help me submit a PR', 'find good first issues', 'I want to contribute to X', or mention fixing bugs in someone else's project.

Overview

Publishermajiayu000
Repositoryspellbook
Skill namecontributor
Stars
280
Forks
26
Bundled files
Instructions only
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.

  • Self-contained

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

  • Open source

    Published by majiayu000 on GitHub. Read the source before you install it.

Installation

Install the Contributor 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.
  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/majiayu000/spellbook.git /tmp/spellbook
mkdir -p .claude/skills
cp -r /tmp/spellbook/skills/contributor .claude/skills/contributor
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Contributor 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 Contributor 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 Contributor 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.

Contributor

Automated open source contribution workflow that takes you from a GitHub repo URL to merged PRs, with built-in safeguards against common contribution failures.

Why this skill exists

Open source contributions fail for predictable reasons: fixing in the wrong layer (your PR gets closed because the maintainer preferred an upstream fix), colliding with other contributors, not following project conventions, or over-engineering a simple fix. This workflow prevents each of those failures through systematic pre-checks.

Phase 1: Reconnaissance

Before writing any code, gather intelligence about the project and its contribution landscape.

1.1 Identify the target

Ask the user for:

  • The GitHub repo URL (e.g., pydantic/pydantic-ai)
  • Their GitHub username and email for commits
  • Any specific issue they want to work on (or ask to scan for available ones)

1.2 Scan for available issues

Use gh CLI to find issues worth contributing to:

bash
# Get open issues with metadata
gh issue list -R <owner>/<repo> --state open --limit 50 \
  --json number,title,labels,assignees,comments

# Check for competing PRs on each candidate
gh pr list -R <owner>/<repo> --state open \
  --search "<issue_number> in:title,body"

Filter criteria (apply in order):

  1. No assignee
  2. No open PR already fixing it (check both linked PRs and title/body search)
  3. Fewer than 5 competing PRs
  4. Prefer labels: bug, good first issue, help wanted
  5. Prefer issues with maintainer comments suggesting a fix direction

1.3 Deep-read issue comments

For each candidate issue, read the full comment thread:

bash
gh issue view <number> -R <owner>/<repo> --json body,comments

Extract:

  • Maintainer fix direction: Do they prefer fixing here or in an upstream dependency?
  • Suggested approach: Any code pointers, file references, or architectural guidance?
  • Blockers: Is this waiting on another PR or release?
  • Who's working on it: Even without assignment, someone might have commented "I'll take this"

1.4 Check for upstream redirection

This is the single most common failure mode. Before committing to any fix:

bash
# Check if maintainers reference another repo
gh issue view <number> -R <owner>/<repo> --json comments \
  | grep -i "upstream\|genai-prices\|separate repo\|other repo"

# Check related repos for recent PRs mentioning this issue
gh pr list -R <owner>/<related-repo> --state open --limit 10 \
  --json title,body | grep -i "<issue_number>\|<issue_keywords>"

If there's any signal the fix belongs elsewhere, stop and ask the user before proceeding.

Phase 2: Pre-communication

Never submit a PR cold. Always communicate your intent first.

2.1 Post a solution outline on the issue

Before writing code, leave a comment on the issue with your proposed approach. This serves two purposes: it claims the work (politely), and it gives maintainers a chance to redirect you before you waste effort.

Template:

Hi, I've been looking into this and traced the root cause to <X>.

Before I open a PR, I wanted to confirm the preferred approach:
A) <approach A — e.g., fix in this repo by modifying X>
B) <approach B — e.g., upstream fix in related-repo>

I can implement either direction. Happy to adjust based on your preference.

Wait for maintainer response before proceeding to code. If no response after 24-48 hours on an active project, proceed with the most conservative approach (smallest scope fix in the current repo).

2.2 Draft PR strategy

Plan to open as a Draft PR first. Convert to ready-for-review only after:

  • CI passes
  • Maintainer acknowledges the approach (via issue comment or PR review)

Phase 3: Repository Setup

3.1 Fork and clone

bash
gh repo fork <owner>/<repo> --clone --remote
cd <repo>

3.2 Determine the development branch

Don't assume main. Check what recent merged PRs target:

bash
gh pr list -R <owner>/<repo> --state merged --limit 10 \
  --json baseRefName,mergedAt

Use the most common baseRefName from recent merges.

3.3 Read contribution guidelines

Check these files in order (read whichever exist):

CONTRIBUTING.md
.github/CONTRIBUTING.md
.github/PULL_REQUEST_TEMPLATE.md
.github/PULL_REQUEST_TEMPLATE/

Extract:

  • Required commit message format
  • Test requirements
  • Pre-commit hooks or linting requirements
  • DCO/CLA requirements
  • Branch naming conventions

3.4 Understand CI

bash
ls .github/workflows/

Read the CI config to know what checks will run on your PR. Identify the commands for:

  • Linting / formatting
  • Type checking
  • Unit tests
  • Integration tests
  • Pre-commit hooks

3.5 Set up the environment

Follow the project's documented setup process. Run the full test suite once to establish a passing baseline before making any changes.

Phase 4: Code Fix

4.1 Branch per issue

bash
git checkout -b fix/issue-<number>-<short-desc> <base-branch>

4.2 Implementation principles

  • Adopt the maintainer's suggested approach if one exists in the issue comments
  • Minimal fix: change only what's necessary to fix the issue. Don't refactor surrounding code, add features, or "improve" things along the way
  • Match project style: follow the existing code patterns, naming conventions, and architecture
  • No hardcoding: avoid hardcoded values unless the project already uses them in the same context
  • Add tests: every fix needs a corresponding test that would have caught the bug. Follow the project's existing test patterns

4.3 Test your changes

Run the project's test suite. All existing tests must pass. Your new test must also pass. If the project has type checking or linting, run those too.

Language-specific verification:

  • Python: pytest, mypy, ruff (or whatever the project uses)
  • TypeScript: npx tsc --noEmit, project test command
  • Rust: cargo check && cargo test
  • Go: go build ./... && go test ./...

Phase 5: Commit and Submit

5.1 Pre-commit checks

If the project uses pre-commit hooks:

bash
pre-commit run --all-files

Fix any issues before committing.

5.2 Commit conventions

bash
# Configure author
git config user.name "<user's name>"
git config user.email "<user's email>"

# Commit with DCO sign-off
git commit -s -m "<type>: <description>

Fixes #<issue-number>"

Rules:

  • Follow the project's commit message format (check recent commits for examples)
  • Include Fixes #<number> or Closes #<number> to auto-link
  • No Generated by Claude, Co-Authored-By: claude, or any AI attribution
  • Use rebase to keep history clean, never force push

5.3 Push and create PR

bash
git push -u origin fix/issue-<number>-<short-desc>

Create a Draft PR following the project's template:

bash
gh pr create --draft --title "<type>: <short description>" \
  --body "$(cat <<'EOF'
## Summary
<1-2 sentences describing the fix>

Fixes #<issue-number>

## Changes
- <bullet points of what changed>

## Test plan
- <how this was tested>
EOF
)"

5.4 Handle CI results

  • CI passes: Comment on PR that it's ready for review, convert from draft
  • CI fails due to your code: Fix it, push new commit, don't amend
  • CI fails due to infrastructure (network timeouts, flaky tests, service outages): Comment explaining the failure is unrelated to your changes and request a rerun

Phase 6: After Submission

6.1 If PR is closed without merge

Don't panic. Common reasons and responses:

ReasonResponse
Fix moved upstreamAsk to contribute to the upstream repo instead
Approach rejectedAsk what approach they'd prefer, offer to redo
DuplicateAcknowledge, offer to help review the other PR
Scope too largeOffer to split into smaller PRs

Template for closed PRs:

Thanks for the feedback. I understand the fix direction has shifted to <X>.
Would it be helpful if I submitted a PR to <upstream-repo> instead?
Happy to contribute wherever it's most useful.

6.2 If changes are requested

Address review feedback promptly. Make each revision a new commit (don't squash during review — the maintainer may want to see the evolution). Only squash if the maintainer asks.

Anti-patterns to avoid

These are real failure modes from production contributions:

  1. Fixing in the wrong layer: You fix in repo A, but the maintainer creates a PR in repo B minutes before closing yours. Prevention: Phase 1.4 upstream check + Phase 2 pre-communication.

  2. PR pile-up: 5 people submit PRs for the same issue. Prevention: Phase 1.2 competing PR check + Phase 2 claiming the work.

  3. Over-engineering: Adding error handling, type annotations, refactoring, or "improvements" beyond the fix. Prevention: Phase 4.2 minimal fix principle.

  4. CI infrastructure confusion: A flaky test or network timeout in CI gets mistaken for a code problem. Prevention: Phase 5.4 explicit CI failure triage.

  5. Silent submission: Submitting a PR without any prior communication on the issue. Prevention: Phase 2 pre-communication is mandatory.

  6. Wrong base branch: PRing against main when the project develops on dev. Prevention: Phase 3.2 branch detection.

Frequently asked questions

What does the Contributor AI skill do?

End-to-end open source contribution workflow: from scanning issues to submitting PRs. Use this skill whenever the user wants to contribute to an open source project, find issues to fix, submit a pull request, fork a repo to contribute, fix a GitHub issue, or mentions 'open source contribution'. Also trigger when they provide a GitHub repo URL and ask about contributing, say things like 'help me submit a PR', 'find good first issues', 'I want to contribute to X', or mention fixing bugs in someone else's project.

Why use Contributor on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/majiayu000/spellbook/tree/main/skills/contributor. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Contributor?

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 Contributor?

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

Is the Contributor AI skill free?

Yes. It is published on GitHub by majiayu000 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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