Use as the final stage of the Butterbase journey when hackathon_mode is true and journey-deploy has passed. Resolves which hackathon to submit to (asking the user when multiple are open), walks every field in the hackathon's returned field_schema with the user one at a time, then calls prep_and_submit_hackathon_entry. Writes the receipt to docs/butterbase/05-submission.md.
npx skills add https://github.com/butterbase-ai/butterbase-skills --skill journey-submit
Stage 5 (final) of the guided journey. Resolve the active hackathon, confirm fields, submit.
journey when current_stage: submit and hackathon_mode: true./butterbase-skills:submit."submit is disabled outside hackathon mode") if hackathon_mode: false.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.
Additionally: refuse to run unless deploy is ticked in 00-state.md. If it is not, tell the user to run /butterbase-skills:journey-deploy first.
docs/butterbase/01-idea.md — title / tagline candidates.docs/butterbase/02-plan.md — feature list.docs/butterbase/04-build-log.md — what actually shipped.docs/butterbase/00-state.md — app_id, deployed_url.Always start with action: "prep". Do not assume there is exactly one hackathon, and do not invent a field schema from prior context — the platform owns the schema and it varies per hackathon.
// First, ask the user: "Do you have a submission code from the organizer? (paste it, or 'no')"
// If yes, pass it. If no, omit it.
{
"tool": "prep_and_submit_hackathon_entry",
"arguments": {
"action": "prep",
"submission_code": "<optional, from user>"
}
}
The response shape is:
{
"matched": null | { "slug", "name", "submission_deadline", "ends_at", "field_schema": { "fields": [...] } },
"match_reason": null | "submission_code" | "already_bound" | "single_open",
"open_hackathons": [{ "slug", "name", "starts_at", "ends_at", "submission_deadline" }, ...],
"next_call": { ... } // present only when matched is non-null
}
Branch on the result:
| matched | open_hackathons.length | Action |
|---|---|---|
| non-null | any | Resolved — continue to Step 2 with matched.field_schema. Mention match_reason to the user ("Resolved via your submission code", "You're already a participant in X", "Only one hackathon is open: X"). |
| null | 0 | No hackathon is currently open for submissions. Tell the user, stop, and do not tick submit. |
| null | 1 | One hackathon is open but the user isn't bound and didn't supply a code. Ask: "The only open hackathon is '<name>' (deadline <submission_deadline>). Submit to this one? If so, paste the submission_code the organizer gave you." Re-run prep with the code. |
| null | ≥ 2 | Multiple open hackathons — ask the user which one. Present the list (name, slug, deadline) and ask: "Which hackathon are you submitting to? Paste its submission_code." Re-run prep with the code. Never guess. |
> Anti-pattern: do not re-run prep in a loop hoping it resolves — it won't until the user provides a submission_code. One question, then re-prep.
matched.field_schema.fields is the authoritative list of fields for *this* hackathon. Walk every field in order. For each:
label (not key) and description.is_url: true or type: "url") for the deployed app → deployed_url from 00-state.md.01-idea.md.01-idea.md + must-haves.required: false.options-typed fields, present the allowed choices verbatim.Use next_call.arguments.data from the prep response as the literal template — it has one key per field with a placeholder string. Replace each placeholder with the confirmed value.
Show the assembled data object back to the user verbatim and ask: "Submit now? (yes/no)". On yes:
{
"tool": "prep_and_submit_hackathon_entry",
"arguments": {
"action": "submit",
"hackathon_slug": "<matched.slug from prep — REQUIRED when multiple are open>",
"app_id": "<app_id from 00-state.md — strongly recommended, unlocks +50 scoring points>",
"submission_code": "<same code passed to prep, if any — required on first submission>",
"data": { /* user-confirmed values keyed by field.key */ }
}
}
> Always pass hackathon_slug = matched.slug from prep. Without it, submit re-resolves and may target a different hackathon if more than one is open.
> Always pass app_id — scoring awards up to 50 points for Butterbase usage on that specific app.
Capture the response (submission.id, submission.version, submission.updated_at, participant_created) and write docs/butterbase/05-submission.md:
# Submission
- submitted_at: <submission.updated_at>
- submission_id: <submission.id>
- version: <submission.version>
- hackathon_slug: <submission.hackathon_slug>
- hackathon_name: <matched.name>
- app_id: <submission.app_id>
- participant_created: <true|false>
## Fields submitted
<one bullet per field: label → value>
Tick - [x] submit in 00-state.md, set current_stage: done. Print a one-line success to the user including the hackathon name and submission id.
prep_and_submit_hackathon_entry (action: "prep" then "submit").docs/butterbase/05-submission.md.action: "prep" and calling submit directly with a hardcoded field list.open_hackathons on the prep response.matched.field_schema.fields.hackathon_slug from prep (can re-resolve to the wrong hackathon).app_id when one exists (forfeits up to 50 scoring points).yes from the user on the assembled payload.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.
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.
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).
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).
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).
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.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
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.
Take butterbase-ai/journey-submit from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
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.