Land And Deploy logo

Land And Deploy

CommunityPopular
FlorianBruniaux
land-and-deploy

Merge PR, wait for CI, verify deploy, run canary. The complete landing pipeline.

Overview

PublisherFlorianBruniaux
Repositoryclaude-code-ultimate-guide
Skill nameland-and-deploy
Stars
6K
Forks
782
Bundled files
Instructions only
LicenseCC-BY-SA-4.0
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 FlorianBruniaux on GitHub. Read the source before you install it.

Installation

Install the Land And Deploy 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/FlorianBruniaux/claude-code-ultimate-guide.git /tmp/claude-code-ultimate-guide
mkdir -p .claude/skills
cp -r /tmp/claude-code-ultimate-guide/examples/skills/land-and-deploy .claude/skills/land-and-deploy
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Land And Deploy 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 Land And Deploy 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 Land And Deploy 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.

Land and Deploy

Complete landing pipeline: merge the PR, wait for CI, verify the deployment, run a health check.

Picks up where /ship left off. /ship creates the PR. This command merges it and verifies production.

Non-interactive by default. The user said "land it", so land it. Stop only for the critical readiness gate and hard blockers.

Instructions

Step 1: Pre-flight

bash
# Verify GitHub CLI is authenticated
gh auth status

# Detect PR from current branch (or use argument if provided)
gh pr view --json number,state,title,url,mergeStateStatus,mergeable,baseRefName,headRefName

Stop conditions:

  • GitHub CLI not authenticated → "Run gh auth login first"
  • No PR exists → "No PR found for this branch. Run /ship first."
  • PR already merged → "PR is already merged."
  • PR is closed → "PR is closed. Reopen it first."

Step 2: CI Status Check

bash
# Check current CI status
gh pr checks --json name,state,status,conclusion

# Check for merge conflicts
gh pr view --json mergeable -q .mergeable

Stop conditions:

  • Required checks FAILING → show failing checks, stop
  • mergeable is CONFLICTING → "PR has merge conflicts. Resolve them and push before landing."
  • Required checks PENDING → proceed to Step 3 (wait for CI)
  • All checks passing → skip to Step 3.5 (readiness gate)

Step 3: Wait for CI (if pending)

bash
# Watch CI checks with 15-minute timeout
gh pr checks --watch --fail-fast
  • CI passes → continue to Step 3.5
  • CI fails → stop, show failures
  • Timeout (15 min) → "CI has been running for 15 minutes. Investigate manually."

Record CI wait duration for the deploy report.


Step 3.5: Pre-Merge Readiness Gate

This is the one critical confirmation before an irreversible merge. Collect all evidence, then get explicit approval.

Review staleness check
bash
# How many commits since the last review in this branch?
git log --oneline $(git merge-base HEAD origin/main)..HEAD | wc -l

# What changed after any review was done?
git log --oneline -10

Staleness thresholds:

  • 0–3 commits since review → CURRENT (green)
  • 4+ commits, touching code → STALE (yellow, review may not reflect current code)
  • No review found → NOT RUN (yellow)
Test results
bash
# Run tests now (fast tests only)
npm test 2>/dev/null || pnpm test 2>/dev/null || \
  pytest --tb=short -q 2>/dev/null || \
  go test ./... 2>/dev/null

# Check exit code
echo "Tests exit code: $?"

Failing tests = BLOCKER. Cannot merge with failing tests.

Documentation check
bash
# Were CHANGELOG and docs updated on this branch?
git diff --name-only $(git merge-base HEAD origin/main)...HEAD -- \
  README.md CHANGELOG.md ARCHITECTURE.md CONTRIBUTING.md CLAUDE.md VERSION

If CHANGELOG.md and VERSION were NOT modified and the diff includes new features → WARNING.

Readiness report

Present a summary and ask for explicit confirmation:

╔══════════════════════════════════════════════════════════╗
║              PRE-MERGE READINESS REPORT                  ║
╠══════════════════════════════════════════════════════════╣
║  PR: #NNN: [title]                                       ║
║  Branch: feature-branch → main                           ║
║                                                          ║
║  REVIEWS                                                 ║
║    Review:     CURRENT / STALE (N commits) / NOT RUN     ║
║                                                          ║
║  TESTS                                                   ║
║    Fast tests: PASS / FAIL (blocker)                     ║
║                                                          ║
║  DOCUMENTATION                                           ║
║    CHANGELOG:  Updated / NOT UPDATED (warning)           ║
║    VERSION:    Bumped / NOT BUMPED (warning)             ║
║                                                          ║
║  WARNINGS: N  |  BLOCKERS: N                             ║
╚══════════════════════════════════════════════════════════╝

Options:
  A) Merge (all checks green)
  B) Don't merge yet, address warnings first
  C) Merge anyway (I understand the risks)

If the user chooses B, list exactly what needs to be done and stop.


Step 4: Merge the PR

bash
# Merge (auto-detect method from repo settings, delete branch after)
gh pr merge --auto --delete-branch

# Fallback if auto-merge is not enabled
# gh pr merge --squash --delete-branch

Record the merge commit SHA and timestamp.

If merge fails with permission error → "You don't have merge permissions. Ask a maintainer to merge."

If merge queue is active, poll until merged:

bash
# Poll every 30 seconds, timeout after 30 minutes
gh pr view --json state -q .state

Step 5: Platform Detection

Detect how this project deploys so we know what to verify.

bash
# Detect platform from config files
[ -f fly.toml ]         && echo "PLATFORM: fly"
[ -f render.yaml ]      && echo "PLATFORM: render"
[ -f vercel.json ] || [ -d .vercel ] && echo "PLATFORM: vercel"
[ -f netlify.toml ]     && echo "PLATFORM: netlify"
[ -f Procfile ]         && echo "PLATFORM: heroku"
[ -f railway.toml ]     && echo "PLATFORM: railway"

# Detect GitHub Actions deploy workflows
for f in .github/workflows/*.yml .github/workflows/*.yaml; do
  [ -f "$f" ] && grep -qiE "deploy|release|production|cd" "$f" 2>/dev/null && echo "DEPLOY_WORKFLOW: $f"
done

# Classify diff scope (frontend / backend / docs / config)
git diff --name-only $(git merge-base HEAD~1 origin/main)...HEAD | \
  awk '{
    if (/\.(css|scss|tsx|jsx|html|svg)$/ || /components|pages|public\//) f=1;
    if (/api\/|server\/|backend\/|\.(go|py|rb|java)$/) b=1;
    if (/README|CHANGELOG|docs\/|\.(md)$/) d=1;
    if (/\.env|config\/|\.toml$|\.yaml$/) c=1;
  } END {
    if (f) print "SCOPE_FRONTEND=true";
    if (b) print "SCOPE_BACKEND=true";
    if (d) print "SCOPE_DOCS=true";
    if (c) print "SCOPE_CONFIG=true";
  }'

Decision tree:

  • Docs-only diff → skip deploy verification, go to Step 8
  • No deploy workflow + no URL provided → ask user if this project has a web deploy
  • Otherwise → proceed to Step 6

Step 6: Wait for Deploy

GitHub Actions deploy workflow:

bash
# Find the run triggered by the merge commit
gh run list --branch main --limit 10 --json databaseId,headSha,status,conclusion,workflowName

# Poll until complete (30s interval, 20 min timeout)
gh run view <run-id> --json status,conclusion

Platform-specific strategies:

PlatformDetectionWait strategy
Vercel / NetlifyAuto-deploy on pushWait 60s for propagation, then check
Fly.iofly.toml presentfly status --app <app>, check started status
Renderrender.yaml presentPoll production URL until it responds with 200
HerokuProcfile presentheroku releases --app <app> -n 1
Railwayrailway.toml presentPoll production URL
GitHub Actions only.github/workflows/ with deploy stepPoll gh run view

If deploy fails → offer to investigate logs or create a revert commit.

Record deploy duration for the report.


Step 7: Production Health Check

Use diff scope (from Step 5) to determine check depth:

Diff ScopeCanary Depth
Docs onlyAlready skipped in Step 5
Config onlyHTTP 200 smoke check only
Backend onlyStatus + response time check
Frontend (any)Full: status + response time + content check
MixedFull check

Full health check sequence:

bash
# 1. Page loads (200 status)
curl -sf -o /dev/null -w "%{http_code}" "${PROD_URL}" 2>/dev/null

# 2. Response time check
curl -sf -o /dev/null -w "%{time_total}" "${PROD_URL}" 2>/dev/null

# 3. Health endpoint (if exists)
curl -sf "${PROD_URL}/health" 2>/dev/null || \
curl -sf "${PROD_URL}/api/health" 2>/dev/null

# 4. Content check: page is not blank
curl -sf "${PROD_URL}" 2>/dev/null | wc -c

Pass criteria:

  • HTTP 200 status
  • Response time under 10 seconds
  • Page has content (>500 bytes)
  • Health endpoint returns 200 (if configured)

If any check fails → offer to revert:

Post-deploy health check detected issues:
  [finding, specific]

Options:
  A) Investigate (this may be normal: cache warming, eventual consistency)
  B) Rollback (revert the merge commit)
  C) Continue (I'll monitor manually)

Step 8: Revert (if needed)

bash
# Fetch the latest base branch
git fetch origin main

# Create a revert commit
git checkout main
git revert <merge-commit-sha> --no-edit
git push origin main

If conflicts → "Revert has conflicts. Run git revert <sha> manually to resolve." If branch protections → "Create a revert PR: gh pr create --title 'revert: <title>'"


Step 9: Deploy Report

LAND & DEPLOY REPORT
═════════════════════════════════════════
PR:           #NNN: [title]
Branch:       feature-branch → main
Merged:       [timestamp] (squash / merge)
Merge SHA:    [short SHA]

Timing:
  CI wait:    [Xm Ys / skipped]
  Deploy:     [Xm Ys / no workflow detected]
  Health:     [Xs / skipped]
  Total:      [end-to-end duration]

CI:           PASSED / FAILED / SKIPPED
Deploy:       PASSED / FAILED / NO WORKFLOW
Production:   HEALTHY / DEGRADED / SKIPPED / REVERTED
  Status:     [HTTP status code]
  Response:   [Xms]

VERDICT: DEPLOYED AND VERIFIED / DEPLOYED (UNVERIFIED) / REVERTED
═════════════════════════════════════════

Step 10: Follow-up Suggestions

After the deploy report, suggest relevant next steps:

  • If production URL was verified: "Run /canary <url> for extended 10-minute monitoring."
  • If new features were shipped: "Run /document-release to update project docs."

Important Rules

  • Never force push. Use gh pr merge (it's safe).
  • Never skip CI. Failing checks = stop.
  • Single-pass production check. For extended monitoring, use /canary.
  • Revert is always an option. At every failure point, offer revert as an escape hatch.
  • Delete the feature branch after merge (via --delete-branch).
  • The goal: user types /land-and-deploy, next thing they see is the deploy report.

Usage

/land-and-deploy                                    # Auto-detect PR, no canary URL
/land-and-deploy https://app.example.com            # Auto-detect PR + verify this URL
/land-and-deploy 123                                # Specific PR number
/land-and-deploy 123 https://app.example.com        # PR number + verification URL

Related Commands

  • /ship: run this first to create the PR
  • /canary: extended post-deploy monitoring loop
  • /review-pr: review the PR before landing

$ARGUMENTS

Frequently asked questions

What does the Land And Deploy AI skill do?

Merge PR, wait for CI, verify deploy, run canary. The complete landing pipeline.

Why use Land And Deploy on TypingMind?

Because you install it once and use it with any model. Land And Deploy 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 Land And Deploy in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/FlorianBruniaux/claude-code-ultimate-guide/tree/main/examples/skills/land-and-deploy. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Land And Deploy?

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 Land And Deploy?

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

Is the Land And Deploy AI skill free?

Yes. It is published on GitHub by FlorianBruniaux under the CC-BY-SA-4.0 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇