Journey Plan logo

Journey Plan

Organization
butterbase-ai
journey-plan

Use as stage 2 of the Butterbase journey, after journey-idea has written 01-idea.md. Translates the idea + capability map into a concrete Butterbase plan — tables (with columns/types/RLS shape), auth providers, function list (name + trigger), storage buckets, AI/RAG/realtime/durable usage, and the chosen frontend stack. In hackathon mode, ruthlessly cuts scope into a "ship now" vs "post-hackathon" split. Produces docs/butterbase/02-plan.md.

Overview

Publisherbutterbase-ai
Repositorybutterbase-skills
Skill namejourney-plan
Stars
535
Forks
42
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 butterbase-ai on GitHub. Read the source before you install it.

Installation

Install the Journey Plan 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/butterbase-ai/butterbase-skills/tree/main/skills/journey-plan
  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/butterbase-ai/butterbase-skills.git /tmp/butterbase-skills
mkdir -p .claude/skills
cp -r /tmp/butterbase-skills/skills/journey-plan .claude/skills/journey-plan
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Journey Plan 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 Journey Plan 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 Journey Plan 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.

Journey: Plan

Stage 2 of the guided journey. Turn the idea brief into an actionable Butterbase plan.

When to use

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

Inputs

  • docs/butterbase/01-idea.md (must exist — if absent, bounce back to journey-idea).
  • docs/butterbase/00-state.md (for hackathon_mode, hackathon_deadline).

External services — check built-ins first

When the plan needs email, messaging, calendar, CRM, docs, or project-management integration:

  1. Invoke butterbase-skills:integrations and call manage_integrations action: "list_available" to see what Composio covers for this app.
  2. If a toolkit fits, the plan should record "via manage_integrations (toolkit: )" instead of naming an external SDK.

When the plan needs payments:

  1. Invoke butterbase-skills:payments.
  2. Default to Stripe Connect via manage_billing unless the user's region forces a fallback (see the payments skill).
  3. Record the choice ("Stripe Connect" or " via function proxy") in the plan's Payments section.

Procedure

Work through these sections in order. After each section, write the result to 02-plan.md before moving on. One question at a time per the spec's questioning discipline.

  1. Tables. Read the capability map. Propose a starter table list with columns and types — recommend, don't ask blank. Example: "Tables I'm seeing: users, orders, items. Missing any?" Then for each table: "<table>.<column>: should this be a uuid / text / int / timestamp / enum?". Confirm primary keys, foreign keys, indexes that are obvious (foreign-key columns).

  2. RLS model. For each table: "Can user A see user B's <table> rows? ① no, strict isolation ② yes, public-read ③ only shared via explicit grant." Decide policy shape. In hackathon mode, prefer option ① and recommend manage_rls action: create_user_isolation.

  3. Auth. "OAuth providers: ① Google only ② Google + GitHub ③ email/password too ④ none (anonymous app)." Also ask: "Need a demo / judge account seeded? (hackathon mode only)".

  4. Functions. For each function from the capability map: "<name>: trigger = HTTP / cron / WebSocket? If cron: schedule? If HTTP: idempotency needed?".

  5. Storage. If used: "Which objects (avatars, attachments, …)? Public-read or per-user?".

  6. AI / RAG / realtime / durable. Only if used. Capture model choice (AI), collections (RAG), tables to subscribe to (realtime), object kinds (durable).

6b. Agents. Only if create_agent is tagged in the idea. For each agent, capture:

  • name (slug), one-line purpose.
  • Tool surface: which builtins (query_table, insert_row, …), which functions (must exist in the Functions section), which MCP servers (URL + transport).
  • Visibility: private | authenticated | public. If public AND any write tool is reachable, mark safety_acknowledged_needed: true and set per-IP / per-user / per-app rate limits + daily_budget_usd.
  • Default model.
  • Note that the agent record will not be carried by clone replay — the spec JSON (under agents/<name>.json) rides along in the repo snapshot, so this matters for the publish stage too.
  1. Frontend stack. "Frontend: ① Vite + React ② Next.js ③ static HTML ④ none (API-only)." Write to 00-state.md frontend_stack.

7b. Publish-as-template. Read publish_as_template from 00-state.md front-matter (set by journey-idea). If yes or unlisted, plan for it now:

  • README outline (one-liner, env-var-per-function list, OAuth setup, agent re-import, MCP server registration, seed data, first-run smoke).
  • Which env vars use the auto-mint convention (butterbase_api_key) vs. require manual user input on clone.
  • Whether agents/*.json files need to be exported and committed (yes if Agents section is non-empty).
  • Note that publishing requires butterbase repo push to upload the source tree as a snapshot — without it, cloners get an empty file tree.

Toolchain question

Ask: "Will your app's code use @butterbase/sdk only in the frontend, only server-side (functions, scripts), or both?" Record under Toolchain → SDK surfaces.

Ask: "Want to install @butterbase/cli for the local dev loop (logs, scaffolding, key rotation)? (yes/no — default yes)" Record under Toolchain → CLI usage.

  1. Scope cut (hackathon mode). Re-read the must-haves list. For each, ask: "Ship now or post-hackathon?" Write the cut list into 02-plan.md's "Post-hackathon" section.

  2. Annotate skipped stages. For every build stage NOT used in this plan (check the capability map and feature list), update 00-state.md's checklist to read - [ ] <stage> (n/a) for that row. Also do this for rls if hackathon_mode: true (mark as (folded into schema)).

  3. Final approval. Show the user the assembled plan and ask: "Plan looks good? (yes / revise <section>)". Loop until yes.

02-plan.md format

markdown
# Plan

## Tables
- `users` (id uuid pk, email text unique, created_at timestamp)
- `orders` (id uuid pk, user_id uuid fk→users.id, status enum[pending,paid,shipped], total int, created_at timestamp; index on user_id)
- ...

## RLS
- `orders`: user-isolation (create_user_isolation, owner column = user_id)
- ...

## Auth
- Providers: Google
- Demo user: yes (email demo@example.com, password set via seed)

## Functions
- `stripe-webhook` — HTTP, idempotency table `_processed_events`
- `daily-digest` — cron 0 9 * * *  UTC

## Storage
- bucket: `avatars` (per-user, private; download via presigned URL)

## AI / RAG / realtime / durable
- (none)

## Agents
- `order-summariser` — purpose: summarise a user's recent orders on demand.
  - Tools: builtin `query_table`, function `format-currency`.
  - Visibility: authenticated. Rate: 60/hr per user. Daily budget: $5.
  - Default model: claude-haiku-4-5-20251001.
  - Spec file: `agents/order-summariser.json` (committed to repo).
- (omit section entirely if no agents)

## Publish-as-template
- Intent: yes / unlisted / no
- README outline: <bullet list of sections>
- Env vars cloners must supply: <list per function>
- Auto-mint eligible keys: `butterbase_api_key` (etc.)
- Agent specs to bundle: `agents/*.json`
- Snapshot push: `butterbase repo push` at end of journey-templates.
- (omit section entirely if publish_as_template = no)

## Frontend
- Vite + React

## Toolchain

- **SDK surfaces**: <client-side only | server-side only | both>
  - Client-side: install `@butterbase/sdk` in the frontend; use `auth`, `db`, `storage`, `realtime`.
  - Server-side: install `@butterbase/sdk` in functions / scripts; use the service-key flow for elevated access.
- **CLI usage**: <yes / no>
  - Yes (default): use `butterbase` CLI for local scaffolding, log tailing (`butterbase logs <fn>`), function invocation, and key rotation.
- **Why both**: MCP tools provision and orchestrate; SDK + CLI are the runtime + dev loop.

## Build order
1. schema
2. rls (folded into schema in hackathon mode)
3. auth
4. storage
5. functions
6. ai          (if used)
7. rag         (if used)
8. realtime    (if used)
9. durable     (if used)
10. agents     (if used — must come after functions, ai, and any MCP-server setup)
11. frontend
12. deploy
13. templates (optional — only if publish_as_template != no)

## Post-hackathon
- email notifications (deferred)
- admin dashboard (deferred)

Outputs

  • Writes docs/butterbase/02-plan.md.
  • Updates 00-state.md: tick - [x] plan, set frontend_stack, set current_stage: preflight, annotate skipped build stages with (n/a).

Anti-patterns

  • ❌ Asking the user to design every table column without recommending defaults first.
  • ❌ Skipping the scope-cut section in hackathon mode.
  • ❌ Writing the plan only at the end — write as you go so progress survives a crash.

Frequently asked questions

What does the Journey Plan AI skill do?

Use as stage 2 of the Butterbase journey, after journey-idea has written 01-idea.md. Translates the idea + capability map into a concrete Butterbase plan — tables (with columns/types/RLS shape), auth providers, function list (name + trigger), storage buckets, AI/RAG/realtime/durable usage, and the chosen frontend stack. In hackathon mode, ruthlessly cuts scope into a "ship now" vs "post-hackathon" split. Produces docs/butterbase/02-plan.md.

Why use Journey Plan on TypingMind?

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

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

Which AI models can use Journey Plan?

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 Journey Plan?

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

Is the Journey Plan AI skill free?

Yes. It is published on GitHub by butterbase-ai 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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