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Ralph Tui Create Beads Rust

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subsy
ralph-tui-create-beads-rust

Convert PRDs to beads for ralph-tui execution using beads-rust (br CLI). Creates an epic with child beads for each user story. Use when you have a PRD and want to use ralph-tui with beads-rust as the task source. Triggers on: create beads, convert prd to beads, beads for ralph, ralph beads, br beads.

Overview

Publishersubsy
Repositoryralph-tui
Skill nameralph-tui-create-beads-rust
Stars
2.4K
Forks
238
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 subsy on GitHub. Read the source before you install it.

Installation

Install the Ralph Tui Create Beads Rust 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/subsy/ralph-tui.git /tmp/ralph-tui
mkdir -p .claude/skills
cp -r /tmp/ralph-tui/skills/ralph-tui-create-beads-rust .claude/skills/ralph-tui-create-beads-rust
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ralph Tui Create Beads Rust 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 Ralph Tui Create Beads Rust 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 Ralph Tui Create Beads Rust 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.

Ralph TUI - Create Beads (beads-rust)

Converts PRDs to beads (epic + child tasks) for ralph-tui autonomous execution using beads-rust (br CLI).

Note: This skill uses the br command from beads-rust. If you have the original beads (bd) installed instead, use the ralph-tui-create-beads skill.


The Job

Take a PRD (markdown file or text) and create beads using br commands:

  1. Extract Quality Gates from the PRD's "Quality Gates" section
  2. Create an epic bead for the feature
  3. Create child beads for each user story (with quality gates appended)
  4. Set up dependencies between beads (schema → backend → UI)
  5. Output ready for ralph-tui run --tracker beads-rust

Step 1: Extract Quality Gates

Look for the "Quality Gates" section in the PRD:

markdown
## Quality Gates

These commands must pass for every user story:
- `pnpm typecheck` - Type checking
- `pnpm lint` - Linting

For UI stories, also include:
- Verify in browser using dev-browser skill

Extract:

  • Universal gates: Commands that apply to ALL stories (e.g., pnpm typecheck)
  • UI gates: Commands that apply only to UI stories (e.g., browser verification)

If no Quality Gates section exists: Ask the user what commands should pass, or use a sensible default like npm run typecheck.


Output Format

Beads use br create command with HEREDOC syntax to safely handle special characters:

bash
# Create epic (link back to source PRD)
br create --type=epic \
  --title="[Feature Name]" \
  --description="$(cat <<'EOF'
[Feature description from PRD]
EOF
)" \
  --external-ref="prd:./tasks/feature-name-prd.md"

# Create child bead (with quality gates in acceptance criteria)
br create \
  --parent=EPIC_ID \
  --title="[Story Title]" \
  --description="$(cat <<'EOF'
[Story description with acceptance criteria INCLUDING quality gates]
EOF
)" \
  --priority=[1-4]

CRITICAL: Always use <<'EOF' (single-quoted) for the HEREDOC delimiter. This prevents shell interpretation of backticks, $variables, and () in descriptions.


Story Size: The #1 Rule

Each story must be completable in ONE ralph-tui iteration (~one agent context window).

ralph-tui spawns a fresh agent instance per iteration with no memory of previous work. If a story is too big, the agent runs out of context before finishing.

Right-sized stories:

  • Add a database column + migration
  • Add a UI component to an existing page
  • Update a server action with new logic
  • Add a filter dropdown to a list

Too big (split these):

  • "Build the entire dashboard" → Split into: schema, queries, UI components, filters
  • "Add authentication" → Split into: schema, middleware, login UI, session handling
  • "Refactor the API" → Split into one story per endpoint or pattern

Rule of thumb: If you can't describe the change in 2-3 sentences, it's too big.


Story Ordering: Dependencies First

Stories execute in dependency order. Earlier stories must not depend on later ones.

Correct order:

  1. Schema/database changes (migrations)
  2. Server actions / backend logic
  3. UI components that use the backend
  4. Dashboard/summary views that aggregate data

Wrong order:

  1. ❌ UI component (depends on schema that doesn't exist yet)
  2. ❌ Schema change

Dependencies with br dep add

Use the br dep add command to specify which beads must complete first:

bash
# Create the beads first
br create --parent=epic-123 --title="US-001: Add schema" ...
br create --parent=epic-123 --title="US-002: Create API" ...
br create --parent=epic-123 --title="US-003: Build UI" ...

# Then add dependencies (issue depends-on blocker)
br dep add ralph-tui-002 ralph-tui-001  # US-002 depends on US-001
br dep add ralph-tui-003 ralph-tui-002  # US-003 depends on US-002

Syntax: br dep add <issue> <depends-on> — the issue depends on (is blocked by) depends-on.

ralph-tui will:

  • Show blocked beads as "blocked" until dependencies complete
  • Never select a bead for execution while its dependencies are open
  • Include dependency context in the prompt when working on a bead

Correct dependency order:

  1. Schema/database changes (no dependencies)
  2. Backend logic (depends on schema)
  3. UI components (depends on backend)
  4. Integration/polish (depends on UI)

Acceptance Criteria: Quality Gates + Story-Specific

Each bead's description should include acceptance criteria with:

  1. Story-specific criteria from the PRD (what this story accomplishes)
  2. Quality gates from the PRD's Quality Gates section (appended at the end)

Good criteria (verifiable):

  • "Add investorType column to investor table with default 'cold'"
  • "Filter dropdown has options: All, Cold, Friend"
  • "Clicking toggle shows confirmation dialog"

Bad criteria (vague):

  • ❌ "Works correctly"
  • ❌ "User can do X easily"
  • ❌ "Good UX"
  • ❌ "Handles edge cases"

Conversion Rules

  1. Extract Quality Gates from PRD first
  2. Each user story → one bead
  3. First story: No dependencies (creates foundation)
  4. Subsequent stories: Depend on their predecessors (UI depends on backend, etc.)
  5. Priority: Based on dependency order, then document order (0=critical, 2=medium, 4=backlog)
  6. All stories: status: "open"
  7. Acceptance criteria: Story criteria + quality gates appended
  8. UI stories: Also append UI-specific gates (browser verification)

Splitting Large PRDs

If a PRD has big features, split them:

Original:

"Add friends outreach track with different messaging"

Split into:

  1. US-001: Add investorType field to database
  2. US-002: Add type toggle to investor list UI
  3. US-003: Create friend-specific phase progression logic
  4. US-004: Create friend message templates
  5. US-005: Wire up task generation for friends
  6. US-006: Add filter by type
  7. US-007: Update new investor form
  8. US-008: Update dashboard counts

Each is one focused change that can be completed and verified independently.


Example

Input PRD:

markdown
# PRD: Friends Outreach

Add ability to mark investors as "friends" for warm outreach.

## Quality Gates

These commands must pass for every user story:
- `pnpm typecheck` - Type checking
- `pnpm lint` - Linting

For UI stories, also include:
- Verify in browser using dev-browser skill

## User Stories

### US-001: Add investorType field to investor table
**Description:** As a developer, I need to categorize investors as 'cold' or 'friend'.

**Acceptance Criteria:**
- [ ] Add investorType column: 'cold' | 'friend' (default 'cold')
- [ ] Generate and run migration successfully

### US-002: Add type toggle to investor list rows
**Description:** As Ryan, I want to toggle investor type directly from the list.

**Acceptance Criteria:**
- [ ] Each row has Cold | Friend toggle
- [ ] Switching shows confirmation dialog
- [ ] On confirm: updates type in database

### US-003: Filter investors by type
**Description:** As Ryan, I want to filter the list to see just friends or cold.

**Acceptance Criteria:**
- [ ] Filter dropdown: All | Cold | Friend
- [ ] Filter persists in URL params

Output beads:

bash
# Create epic (link back to source PRD)
br create --type=epic \
  --title="Friends Outreach Track" \
  --description="$(cat <<'EOF'
Warm outreach for deck feedback
EOF
)" \
  --external-ref="prd:./tasks/friends-outreach-prd.md"

# US-001: No deps (first - creates schema)
br create --parent=ralph-tui-abc \
  --title="US-001: Add investorType field to investor table" \
  --description="$(cat <<'EOF'
As a developer, I need to categorize investors as 'cold' or 'friend'.

## Acceptance Criteria
- [ ] Add investorType column: 'cold' | 'friend' (default 'cold')
- [ ] Generate and run migration successfully
- [ ] pnpm typecheck passes
- [ ] pnpm lint passes
EOF
)" \
  --priority=1

# US-002: UI story (gets browser verification too)
br create --parent=ralph-tui-abc \
  --title="US-002: Add type toggle to investor list rows" \
  --description="$(cat <<'EOF'
As Ryan, I want to toggle investor type directly from the list.

## Acceptance Criteria
- [ ] Each row has Cold | Friend toggle
- [ ] Switching shows confirmation dialog
- [ ] On confirm: updates type in database
- [ ] pnpm typecheck passes
- [ ] pnpm lint passes
- [ ] Verify in browser using dev-browser skill
EOF
)" \
  --priority=2

# Add dependency: US-002 depends on US-001
br dep add ralph-tui-002 ralph-tui-001

# US-003: UI story
br create --parent=ralph-tui-abc \
  --title="US-003: Filter investors by type" \
  --description="$(cat <<'EOF'
As Ryan, I want to filter the list to see just friends or cold.

## Acceptance Criteria
- [ ] Filter dropdown: All | Cold | Friend
- [ ] Filter persists in URL params
- [ ] pnpm typecheck passes
- [ ] pnpm lint passes
- [ ] Verify in browser using dev-browser skill
EOF
)" \
  --priority=3

# Add dependency: US-003 depends on US-002
br dep add ralph-tui-003 ralph-tui-002

Syncing Changes

After creating beads, sync to export to JSONL (for git tracking):

bash
br sync --flush-only

This exports the SQLite database to .beads/issues.jsonl for version control.


Output Location

Beads are stored in: .beads/ directory (SQLite DB + JSONL export)

After creation, run ralph-tui:

bash
# Work on a specific epic
ralph-tui run --tracker beads-rust --epic ralph-tui-abc

# Or let it pick the best task automatically
ralph-tui run --tracker beads-rust

ralph-tui will:

  1. Work on beads within the specified epic (or select the best available task)
  2. Close each bead when complete
  3. Close the epic when all children are done
  4. Output <promise>COMPLETE</promise> when epic is done

Checklist Before Creating Beads

  • Extracted Quality Gates from PRD (or asked user if missing)
  • Each story is completable in one iteration (small enough)
  • Stories are ordered by dependency (schema → backend → UI)
  • Quality gates appended to every bead's acceptance criteria
  • UI stories have browser verification (if specified in Quality Gates)
  • Acceptance criteria are verifiable (not vague)
  • No story depends on a later story (only earlier stories)
  • Dependencies added with br dep add after creating beads
  • Ran br sync --flush-only to export for git tracking

Differences from beads (Go version)

Commandbeads (bd)beads-rust (br)
Createbd createbr create
Dependenciesbd dep addbr dep add
Syncbd syncbr sync --flush-only
Closebd closebr close
Storage.beads/beads.jsonl.beads/*.db + JSONL export

Frequently asked questions

What does the Ralph Tui Create Beads Rust AI skill do?

Convert PRDs to beads for ralph-tui execution using beads-rust (br CLI). Creates an epic with child beads for each user story. Use when you have a PRD and want to use ralph-tui with beads-rust as the task source. Triggers on: create beads, convert prd to beads, beads for ralph, ralph beads, br beads.

Why use Ralph Tui Create Beads Rust on TypingMind?

Because you install it once and use it with any model. Ralph Tui Create Beads Rust 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 Ralph Tui Create Beads Rust in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/subsy/ralph-tui/tree/main/skills/ralph-tui-create-beads-rust. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Ralph Tui Create Beads Rust?

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 Ralph Tui Create Beads Rust?

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

Is the Ralph Tui Create Beads Rust AI skill free?

Yes. It is published on GitHub by subsy 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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