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Ci Scaffolding

Community
neuromechanist
ci-scaffolding

This skill should be used when the user says "set up CI", "create GitHub Actions", "scaffold CI pipeline", "add CI/CD", "configure continuous integration", "create test workflow", "create release workflow", "add pre-commit hooks", "set up linting pipeline", "configure ruff in CI", "configure biome in CI", "add typo checking", or wants to add, update, or customize CI/CD pipelines for their project. For initial project setup including basic CI, see init-project. Use ci-scaffolding for adding or customizing CI/CD in existing projects.

Overview

Publisherneuromechanist
Repositoryresearch-skills
Skill nameci-scaffolding
Stars
51
Forks
9
Bundled files
2
LicenseBSD-3-Clause
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 neuromechanist on GitHub. Read the source before you install it.

Installation

Install the Ci Scaffolding 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/neuromechanist/research-skills/tree/main/plugins/project/skills/ci-scaffolding
  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/neuromechanist/research-skills.git /tmp/research-skills
mkdir -p .claude/skills
cp -r /tmp/research-skills/plugins/project/skills/ci-scaffolding .claude/skills/ci-scaffolding
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ci Scaffolding 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 Ci Scaffolding 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 Ci Scaffolding 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.

CI/CD Scaffolding

Generate and configure CI/CD pipelines following project conventions. Supports Python (ruff + pytest + coverage) and TypeScript/JavaScript (biome + bun test) stacks, with templates for testing, documentation, and release workflows.

When to Use

  • Adding CI/CD to a new project
  • Updating existing GitHub Actions workflows
  • Setting up pre-commit hooks for linting
  • Configuring coverage reporting
  • Adding release automation (tag-based)

Triggers and scope (every workflow)

CI runs only when files it checks change, once per push.

  1. Trigger on pull_request for any base (fix PRs and epic phase PRs need CI too) plus push to the integration branches. Never on: [push, pull_request]; it runs twice for every PR push.
  2. Add concurrency that cancels superseded PR runs.
  3. Scope each workflow to the files it reads: paths for docs and deploy workflows, paths-ignore for code workflows. Ignore only paths no job reads; if CI builds or lints the docs, keep them in that job's scope.
  4. A workflow skipped by a path filter or [skip ci] leaves its required check pending and blocks merge. For a required check, use the always-running gate job in references/ci-best-practices.md, give that workflow no trigger-level paths or paths-ignore, and require only the gate.
  5. Give every job timeout-minutes (default 20) and tests a per-test timeout; a hung job otherwise runs for 6 hours.
  6. Required checks never call live external services; put those in a scheduled or non-required job.
  7. One workflow per concern; never two workflows running the same tests.
  8. Prefer no post-merge bot commit: bump versions inside the PR or at release. If a bot must commit after merge (version bump, file sync), push it with [skip ci], alone in its push, only to the integration branch, never to a PR head. GitHub also reads the marker from a PR's head commit, so if the integration branch later heads a release PR (for example develop to main), its required checks never report; in that setup skip inside the workflow instead: a gate job detects the bot's commit and skips its steps, so the required checks still pass. A bot push needs a PAT or App token to trigger anything; GITHUB_TOKEN pushes trigger no workflows. A tag on a [skip ci] commit also skips tag-triggered workflows, so create releases with gh release create or a dispatch.
  9. One merge deploys and publishes once per environment, from the final commit. When a bot bumps after merge, the same workflow deploys: a bump job, then a deploy job with needs: bump that checks out the bumped SHA. No separate push-triggered deploy runs for that branch.
yaml
on:
  pull_request:
    paths-ignore: ['**.md', 'docs/**', '.context/**', '.memory/**']
  push:
    branches: [main, develop]
    paths-ignore: ['**.md', 'docs/**', '.context/**', '.memory/**']
concurrency:
  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
  cancel-in-progress: ${{ github.event_name == 'pull_request' }}

[skip ci] commits

  • Add [skip ci] to the subject of a commit that changes only files no CI job reads (notes, .context/, .memory/, prose docs CI does not build). Exception: bot bump and sync commits per rule 8.
  • GitHub reads only the head commit of a push. Never push a [skip ci] commit on top of untested code commits.
  • GitHub matches the token anywhere in that message, body included. Never quote it in a commit message you want tested; write "skip-ci marker" instead.
  • On a PR branch, use it only when gh pr checks N --required lists no required checks; otherwise rely on the gate job's changes filter.

Pipeline Templates

Python Project Pipeline

Standard Python CI pipeline:

yaml
# .github/workflows/test.yml
name: Tests
# on + concurrency: copy the block from "Triggers and scope"
jobs:
  test:
    runs-on: ubuntu-latest
    timeout-minutes: 20
    steps:
      - uses: actions/checkout@v4
      - uses: astral-sh/setup-uv@v5
      - run: uv sync --dev
      - run: uv run ruff check .
      - run: uv run ruff format --check .
      - run: uv run pytest --cov --cov-report=xml

Key conventions:

  • Use astral-sh/setup-uv (not pip)
  • Ruff for both linting and formatting
  • pytest with coverage (XML report for CI integration)
  • No mocks in test configuration

TypeScript/JavaScript Project Pipeline

yaml
# .github/workflows/test.yml
name: Tests
# on + concurrency: copy the block from "Triggers and scope"
jobs:
  test:
    runs-on: ubuntu-latest
    timeout-minutes: 20
    steps:
      - uses: actions/checkout@v4
      - uses: oven-sh/setup-bun@v2
      - run: bun install
      - run: bun run biome check .
      - run: bun test

Key conventions:

  • Use oven-sh/setup-bun (not npm/node)
  • Biome for linting and formatting
  • bun test for testing

Release Workflow

Tag-based release automation:

yaml
# .github/workflows/release.yml
name: Release
on:
  push:
    tags: ['v*']
jobs:
  release:
    runs-on: ubuntu-latest
    timeout-minutes: 20
    permissions:
      contents: write
    steps:
      - uses: actions/checkout@v4
      - uses: softprops/action-gh-release@v2
        with:
          generate_release_notes: true

Documentation Workflow

MkDocs deployment to GitHub Pages:

yaml
# .github/workflows/docs.yml
name: Docs
on:
  push:
    branches: [main]
    paths: ['docs/**', 'mkdocs.yml']
permissions:
  contents: write
jobs:
  deploy:
    runs-on: ubuntu-latest
    timeout-minutes: 20
    steps:
      - uses: actions/checkout@v4
      - uses: astral-sh/setup-uv@v5
      - run: uv sync --group docs
      - run: uv run mkdocs gh-deploy --force

Typo Checking

Add typo checking to any project:

yaml
# In its own workflow without path filters (it reads docs)
- uses: crate-ci/typos@master

Pre-commit Hook Setup

Python (ruff)

bash
#!/bin/sh
# .git/hooks/pre-commit
STAGED=$(git diff --cached --name-only --diff-filter=d -- '*.py')
[ -z "$STAGED" ] && exit 0
echo "$STAGED" | xargs uv run ruff check --fix --unsafe-fixes
echo "$STAGED" | xargs uv run ruff format
git add $STAGED

TypeScript (biome)

bash
#!/bin/sh
# .git/hooks/pre-commit
STAGED=$(git diff --cached --name-only --diff-filter=d -- '*.ts' '*.tsx' '*.js' '*.jsx')
[ -z "$STAGED" ] && exit 0
echo "$STAGED" | xargs bunx biome check --write
git add $STAGED

Scaffolding Workflow

Step 1: Detect project type

Scan for language markers (pyproject.toml, package.json, Cargo.toml, go.mod) and existing CI configuration.

Step 2: Select pipelines

Based on detection:

  • Always: test workflow
  • If Python with docs: docs workflow
  • If releases needed: release workflow
  • If no pre-commit hook: install one

Step 3: Generate files

Create .github/workflows/ directory and write workflow files. Never overwrite existing workflows without asking.

Step 4: Verify

Run act locally if available to validate workflows, or do a dry-run check of YAML syntax. Confirm every workflow has scoped triggers and concurrency, and that a docs-only change starts no code workflow.

Additional Resources

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 Ci Scaffolding AI skill do?

This skill should be used when the user says "set up CI", "create GitHub Actions", "scaffold CI pipeline", "add CI/CD", "configure continuous integration", "create test workflow", "create release workflow", "add pre-commit hooks", "set up linting pipeline", "configure ruff in CI", "configure biome in CI", "add typo checking", or wants to add, update, or customize CI/CD pipelines for their project. For initial project setup including basic CI, see init-project. Use ci-scaffolding for adding or customizing CI/CD in existing projects.

Why use Ci Scaffolding on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/neuromechanist/research-skills/tree/main/plugins/project/skills/ci-scaffolding. 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 Ci Scaffolding?

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 Ci Scaffolding?

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

Is the Ci Scaffolding AI skill free?

Yes. It is published on GitHub by neuromechanist under the BSD-3-Clause 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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