mcpbeat Sign in

Journey Deploy Agent Skill

Use as the deploy-verification stage of the Butterbase journey, after journey-frontend (or after any build stage if there is no frontend). Smoke-tests the deployed app — hits the live URL, invokes any deployed functions, checks auth round-trip. Writes results to docs/butterbase/04-build-log.md. Blocks journey-submit if smoke fails.

671 tokens
context cost
the whole folder, loaded on every use
1
files
instructions only
0
copies elsewhere
how many repositories repackaged it
532
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/butterbase-ai/butterbase-skills --skill journey-deploy

The instruction itself

7 sections, as written by the author

Journey: Deploy verification

Stage 4 of the guided journey. Verify the deployed app actually works end-to-end.

When to use

  • Dispatched by journey when current_stage: deploy.
  • Directly via /butterbase-skills:journey-deploy.

Preflight

If docs/butterbase/03-preflight.md is missing, older than 24 hours, or 00-state.md has app_id: null, invoke butterbase-skills:journey-preflight first. Wait for it to return successfully before proceeding.

Inputs

  • docs/butterbase/00-state.md — for app_id, deployed_url.
  • docs/butterbase/02-plan.md — for the function list to smoke.
  • docs/butterbase/04-build-log.md — to know what shipped.

Procedure

  • Refresh docs. Call butterbase_docs with topic: "frontend". For end-to-end smoke patterns, also WebFetch https://docs.butterbase.ai/deploy. Skip if cache is fresh.

For each check, log a one-line result to 04-build-log.md. Halt on the first ✗ and ask the user to fix or skip.

  • Frontend reachable. If deployed_url is set, fetch it via Bash curl -sS -o /dev/null -w "%{http_code}" <url>. Expect 200. Log <ISO> deploy curl 200 ok or ... <code> fail.
  • Functions respond. For each function in the plan with HTTP trigger, call mcp__butterbase__invoke_function with a representative payload. Expect 2xx. Log per function.
  • Auth round-trip (if configured). If the plan included OAuth, prompt the user: "Open <deployed_url>, click 'Sign in with <provider>', and confirm you land logged-in. Did it work? (yes/no)". Log the answer.
  • Function logs clean. Call mcp__butterbase__manage_function action: get_logs for the most recent invocations. Show the user any error-level lines. Ask: "Anything concerning here? (no → continue, yes → fix and re-run)".
  • If all checks pass, tick - [x] deploy in 00-state.md, set current_stage: submit (if hackathon_mode: true) or current_stage: done (otherwise).
  • Return to journey orchestrator.

Outputs

  • Multiple lines in 04-build-log.md.
  • Updated 00-state.md.

Anti-patterns

  • ❌ Treating a 200 from the frontend root as proof the app works — actually invoke a function.
  • ❌ Skipping the auth round-trip check just because OAuth is configured server-side — judges will try to log in.

Other skills for the same job

different authors, same section of the catalogue
Azure Kubernetes Automatic Readiness
by microsoft
vendor ×3

Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.

13k tokens
Capacity
by microsoft
vendor ×3

Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.

6k tokens scripts
Customize
by microsoft
vendor ×3

Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).

8k tokens
Deploy Model
by microsoft
vendor ×3

Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).

26k tokens scripts
Preset
by microsoft
vendor ×3

Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).

9k tokens
Lamindb
by christophacham
×3

This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.

22k tokens
Latchbio Integration
by christophacham
×3

Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.

12k tokens
Modal
by christophacham
×3

Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.

17k tokens

How to use it

Copy the folder

Take butterbase-ai/journey-deploy from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.