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Process Builder

OrganizationPopular
a5c-ai
process-builder

Scaffold new babysitter process definitions following SDK patterns, proper structure, and best practices. Guides the 3-phase workflow from research to implementation.

Overview

Publishera5c-ai
Repositorybabysitter
Skill nameprocess-builder
Stars
1.8K
Forks
112
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 a5c-ai on GitHub. Read the source before you install it.

Installation

Install the Process Builder 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/a5c-ai/babysitter/tree/main/.claude/skills/process-builder
  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/a5c-ai/babysitter.git /tmp/babysitter
mkdir -p .claude/skills
cp -r /tmp/babysitter/.claude/skills/process-builder .claude/skills/process-builder
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Process Builder 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 Process Builder 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 Process Builder 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.

Process Builder

Create new process definitions for the babysitter event-sourced orchestration framework.

Quick Reference

Processes live in: library/
├── methodologies/          # Reusable development approaches (TDD, BDD, Scrum, etc.)
│   └── [name]/
│       ├── README.md       # Documentation
│       ├── [name].js       # Main process
│       └── examples/       # Sample inputs
│
└── specializations/        # Domain-specific processes
    ├── [category]/         # Engineering specializations (direct children)
    │   └── [process].js
    └── domains/
        └── [domain]/       # Business, Science, Social Sciences
            └── [spec]/
                ├── README.md
                ├── references.md
                ├── processes-backlog.md
                └── [process].js

3-Phase Workflow

Phase 1: Research & Documentation

Create foundational documentation:

bash
# Check existing specializations
ls library/specializations/

# Check methodologies
ls library/methodologies/

Create:

  • README.md - Overview, roles, goals, use cases, common flows
  • references.md - External references, best practices, links to sources

Phase 2: Identify Processes

Create processes-backlog.md with identified processes:

markdown
# Processes Backlog - [Specialization Name]

## Identified Processes

- [ ] **process-name** - Short description of what this process accomplishes
  - Reference: [Link to methodology or standard]
  - Inputs: list key inputs
  - Outputs: list key outputs

- [ ] **another-process** - Description
  ...

Phase 3: Create Process Files

Create .js process files following SDK patterns (see below).


Process File Structure

Every process file follows this pattern:

javascript
/**
 * @process [category]/[process-name]
 * @description Clear description of what the process accomplishes end-to-end
 * @inputs { inputName: type, optionalInput?: type }
 * @outputs { success: boolean, outputName: type, artifacts: array }
 *
 * @graph
 *   domains: [domain:software-engineering]
 *   skillAreas: [skill-area:your-skill-area]
 *   topics: [topic:your-topic]
 *   roles: [role:your-role]
 *   workflows: [workflow:your-workflow]
 *
 * @example
 * const result = await orchestrate('[category]/[process-name]', {
 *   inputName: 'value',
 *   optionalInput: 'optional-value'
 * });
 *
 * @references
 * - Book: "Relevant Book Title" by Author
 * - Article: [Title](https://link)
 * - Standard: ISO/IEEE reference
 */

import { defineTask } from '@a5c-ai/babysitter-sdk';

/**
 * [Process Name] Process
 *
 * Methodology: Brief description of the approach
 *
 * Phases:
 * 1. Phase Name - What happens
 * 2. Phase Name - What happens
 * ...
 *
 * Benefits:
 * - Benefit 1
 * - Benefit 2
 *
 * @param {Object} inputs - Process inputs
 * @param {string} inputs.inputName - Description of input
 * @param {Object} ctx - Process context (see SDK)
 * @returns {Promise<Object>} Process result
 */
export async function process(inputs, ctx) {
  const {
    inputName,
    optionalInput = 'default-value',
    // ... destructure with defaults
  } = inputs;

  const artifacts = [];

  // ============================================================================
  // PHASE 1: [PHASE NAME]
  // ============================================================================

  ctx.log?.('info', 'Starting Phase 1...');

  const phase1Result = await ctx.task(someTask, {
    // task inputs
  });

  artifacts.push(...(phase1Result.artifacts || []));

  // Breakpoint for human review (when needed)
  await ctx.breakpoint({
    question: 'Review the results and approve to continue?',
    title: 'Phase 1 Review',
    context: {
      runId: ctx.runId,
      files: [
        { path: 'artifacts/output.md', format: 'markdown', label: 'Output' }
      ]
    }
  });

  // ============================================================================
  // PHASE 2: [PHASE NAME] - Parallel Execution Example
  // ============================================================================

  const [result1, result2, result3] = await ctx.parallel.all([
    () => ctx.task(task1, { /* args */ }),
    () => ctx.task(task2, { /* args */ }),
    () => ctx.task(task3, { /* args */ })
  ]);

  // ============================================================================
  // PHASE 3: [ITERATION EXAMPLE]
  // ============================================================================

  let iteration = 0;
  let targetMet = false;

  while (!targetMet && iteration < maxIterations) {
    iteration++;

    const iterResult = await ctx.task(iterativeTask, {
      iteration,
      previousResults: /* ... */
    });

    targetMet = iterResult.meetsTarget;

    if (!targetMet && iteration % 3 === 0) {
      // Periodic checkpoint
      await ctx.breakpoint({
        question: `Iteration ${iteration}: Target not met. Continue?`,
        title: 'Progress Checkpoint',
        context: { /* ... */ }
      });
    }
  }

  // ============================================================================
  // COMPLETION
  // ============================================================================

  return {
    success: targetMet,
    iterations: iteration,
    artifacts,
    // ... other outputs matching @outputs
  };
}

// ============================================================================
// TASK DEFINITIONS
// ============================================================================

/**
 * Task: [Task Name]
 * Purpose: What this task accomplishes
 */
const someTask = defineTask({
  name: 'task-name',
  description: 'What this task does',

  // Task definition - executed externally by orchestrator
  // This returns a TaskDef that describes HOW to run the task

  inputs: {
    inputName: { type: 'string', required: true },
    optionalInput: { type: 'number', default: 10 }
  },

  outputs: {
    result: { type: 'object' },
    artifacts: { type: 'array' }
  },

  async run(inputs, taskCtx) {
    const effectId = taskCtx.effectId;

    return {
      kind: 'node',  // or 'agent', 'skill', 'shell', 'breakpoint'
      title: `Task: ${inputs.inputName}`,
      node: {
        entry: 'scripts/task-runner.js',
        args: ['--input', inputs.inputName, '--effect-id', effectId]
      },
      io: {
        inputJsonPath: `tasks/${effectId}/input.json`,
        outputJsonPath: `tasks/${effectId}/result.json`
      },
      labels: ['category', 'subcategory']
    };
  }
});

SDK Context API Reference

The ctx object provides these intrinsics:

MethodPurposeBehavior
ctx.task(taskDef, args, opts?)Execute a taskReturns result or throws typed exception
ctx.breakpoint(payload)Human approval gatePauses until approved via human
ctx.sleepUntil(isoOrEpochMs)Time-based gatePauses until specified time
ctx.parallel.all([...thunks])Parallel executionRuns independent tasks concurrently
ctx.parallel.map(items, fn)Parallel mapMaps items through task function
ctx.now()Deterministic timeReturns current Date (or provided time)
ctx.log?.(level, msg, data?)LoggingOptional logging helper
ctx.runIdRun identifierCurrent run's unique ID

Task Kinds

KindUse CaseExecutor
nodeScripts, builds, testsNode.js process
agentLLM-powered analysis, generationClaude Code agent
skillClaude Code skillsSkill invocation
shellSystem commandsShell execution
breakpointHuman approvalBreakpoints UI/service
sleepTime gatesOrchestrator scheduling
orchestrator_taskInternal orchestrator workSelf-routed

Breakpoint Patterns

Basic Approval Gate

javascript
await ctx.breakpoint({
  question: 'Approve to continue?',
  title: 'Checkpoint',
  context: { runId: ctx.runId }
});

With File References (for UI display)

javascript
await ctx.breakpoint({
  question: 'Review the generated specification. Does it meet requirements?',
  title: 'Specification Review',
  context: {
    runId: ctx.runId,
    files: [
      { path: 'artifacts/spec.md', format: 'markdown', label: 'Specification' },
      { path: 'artifacts/spec.json', format: 'json', label: 'JSON Schema' },
      { path: 'src/implementation.ts', format: 'code', language: 'typescript', label: 'Implementation' }
    ]
  }
});

Conditional Breakpoint

javascript
if (qualityScore < targetScore) {
  await ctx.breakpoint({
    question: `Quality score ${qualityScore} is below target ${targetScore}. Continue iterating or accept current result?`,
    title: 'Quality Gate',
    context: {
      runId: ctx.runId,
      data: { qualityScore, targetScore, iteration }
    }
  });
}

Common Patterns

Quality Convergence Loop

javascript
let quality = 0;
let iteration = 0;
const targetQuality = inputs.targetQuality || 85;
const maxIterations = inputs.maxIterations || 10;

while (quality < targetQuality && iteration < maxIterations) {
  iteration++;
  ctx.log?.('info', `Iteration ${iteration}/${maxIterations}`);

  // Execute improvement tasks
  const improvement = await ctx.task(improveTask, { iteration });

  // Score quality (parallel checks)
  const [coverage, lint, security, tests] = await ctx.parallel.all([
    () => ctx.task(coverageTask, {}),
    () => ctx.task(lintTask, {}),
    () => ctx.task(securityTask, {}),
    () => ctx.task(runTestsTask, {})
  ]);

  // Agent scores overall quality
  const score = await ctx.task(agentScoringTask, {
    coverage, lint, security, tests, iteration
  });

  quality = score.overall;
  ctx.log?.('info', `Quality: ${quality}/${targetQuality}`);

  if (quality >= targetQuality) {
    ctx.log?.('info', 'Quality target achieved!');
    break;
  }
}

return {
  success: quality >= targetQuality,
  quality,
  iterations: iteration
};

Phased Workflow with Reviews

javascript
// Phase 1: Research
const research = await ctx.task(researchTask, { topic: inputs.topic });

await ctx.breakpoint({
  question: 'Review research findings before proceeding to planning.',
  title: 'Research Review',
  context: { runId: ctx.runId }
});

// Phase 2: Planning
const plan = await ctx.task(planningTask, { research });

await ctx.breakpoint({
  question: 'Review plan before implementation.',
  title: 'Plan Review',
  context: { runId: ctx.runId }
});

// Phase 3: Implementation
const implementation = await ctx.task(implementTask, { plan });

// Phase 4: Verification
const verification = await ctx.task(verifyTask, { implementation, plan });

await ctx.breakpoint({
  question: 'Final review before completion.',
  title: 'Final Approval',
  context: { runId: ctx.runId }
});

return { success: verification.passed, plan, implementation };

Parallel Fan-out with Aggregation

javascript
// Fan out to multiple parallel analyses
const analyses = await ctx.parallel.map(components, component =>
  ctx.task(analyzeTask, { component }, { label: `analyze:${component.name}` })
);

// Aggregate results
const aggregated = await ctx.task(aggregateTask, { analyses });

return { analyses, summary: aggregated.summary };

Testing Processes

CLI Commands

bash
# Create a new run
babysitter run:create \
  --process-id methodologies/my-process \
  --entry ./library/methodologies/my-process.js#process \
  --inputs ./test-inputs.json \
  --json

# Iterate the run
babysitter run:iterate .a5c/runs/<runId> --json

# List pending tasks
babysitter task:list .a5c/runs/<runId> --pending --json

# Post a task result
babysitter task:post .a5c/runs/<runId> <effectId> \
  --status ok \
  --value ./result.json

# Check run status
babysitter run:status .a5c/runs/<runId>

# View events
babysitter run:events .a5c/runs/<runId> --limit 20 --reverse

Sample Test Input File

json
{
  "feature": "User authentication with JWT",
  "acceptanceCriteria": [
    "Users can register with email and password",
    "Users can login and receive a JWT token",
    "Invalid credentials are rejected"
  ],
  "testFramework": "jest",
  "targetQuality": 85,
  "maxIterations": 5
}

Process Builder Workflow

1. Gather Requirements

Ask the user:

QuestionPurpose
Domain/CategoryDetermines directory location
Process Namekebab-case identifier
GoalWhat should the process accomplish?
InputsWhat data does the process need?
OutputsWhat artifacts/results does it produce?
PhasesWhat are the major steps?
Quality GatesWhere should humans review?
Iteration StrategyFixed phases vs. convergence loop?

2. Research Similar Processes

bash
# Find similar processes
ls library/methodologies/
ls library/specializations/

# Read similar process for patterns
cat library/methodologies/atdd-tdd/atdd-tdd.js | head -200

# Check methodology README structure
cat library/methodologies/atdd-tdd/README.md

3. Check Methodologies Backlog

bash
cat library/methodologies/backlog.md

4. Create the Process

For Methodologies:

  1. Create methodologies/[name]/README.md (comprehensive documentation)
  2. Create methodologies/[name]/[name].js (process implementation)
  3. Create methodologies/[name]/examples/ (sample inputs)

For Specializations:

  1. If domain-specific: specializations/domains/[domain]/[spec]/
  2. If engineering: specializations/[category]/[process].js
  3. Create README.md, references.md, processes-backlog.md first
  4. Then create individual process.js files

5. Validate Structure

Checklist:

  • JSDoc header with @process, @description, @inputs, @outputs, @example, @references
  • @graph block with relevant atlas node IDs (at minimum one domain)
  • Import from @a5c-ai/babysitter-sdk
  • Main export async function process(inputs, ctx)
  • Input destructuring with defaults
  • Clear phase comments (// === PHASE N: NAME ===)
  • Logging via ctx.log?.('info', message)
  • Tasks via ctx.task(taskDef, inputs)
  • Breakpoints at key decision points
  • Artifact collection throughout
  • Return object matches @outputs schema

Examples by Type

Methodology Process (atdd-tdd style)

javascript
/**
 * @process methodologies/my-methodology
 * @description My development methodology with quality convergence
 * @inputs { feature: string, targetQuality?: number }
 * @outputs { success: boolean, quality: number, artifacts: array }
 */
export async function process(inputs, ctx) {
  const { feature, targetQuality = 85 } = inputs;
  // ... implementation
}

Specialization Process (game-development style)

javascript
/**
 * @process specializations/game-development/core-mechanics-prototyping
 * @description Prototype and validate core gameplay mechanics through iteration
 * @inputs { prototypeName: string, mechanicsToTest: array, engine?: string }
 * @outputs { success: boolean, mechanicsValidated: array, playtestResults: object }
 */
export async function process(inputs, ctx) {
  const { prototypeName, mechanicsToTest, engine = 'Unity' } = inputs;
  // ... implementation
}

Domain Process (science/research style)

javascript
/**
 * @process specializations/domains/science/bioinformatics/sequence-analysis
 * @description Analyze genomic sequences using standard bioinformatics workflows
 * @inputs { sequences: array, analysisType: string, referenceGenome?: string }
 * @outputs { success: boolean, alignments: array, variants: array, report: object }
 */
export async function process(inputs, ctx) {
  const { sequences, analysisType, referenceGenome = 'GRCh38' } = inputs;
  // ... implementation
}

Atlas Graph Metadata

Every generated process file MUST include a @graph JSDoc block in its file header comment alongside the standard @process, @description, @inputs, and @outputs tags.

Format

javascript
/**
 * @process specializations/my-domain/my-process
 * @description ...
 * @inputs { ... }
 * @outputs { ... }
 *
 * @graph
 *   domains: [domain:software-engineering, domain:devops]
 *   skillAreas: [skill-area:caching-strategies]
 *   topics: [topic:microservices, topic:event-sourcing]
 *   roles: [role:backend-engineer, role:sre]
 *   workflows: [workflow:code-review]
 */

How to choose node IDs

Read the atlas graph domain directory (packages/atlas/graph/domain/) to find valid node IDs. The directory contains YAML files grouped by category:

  • domains/ — high-level domain nodes (e.g. domain:software-engineering, domain:devops, domain:data-engineering)
  • skill-areas/ — specific skill area nodes
  • topics/ — granular topic nodes
  • roles/ — role nodes (engineers, practitioners, researchers)
  • workflows/ — workflow nodes

Pick 2–4 edges that genuinely relate to the process. Do not guess IDs — read the actual YAML files to find valid ones. At minimum, every process must reference at least one domain: node.

Why this matters

This metadata connects the process to the atlas knowledge graph. A pre-build generator script parses the @graph block and creates graph nodes and edges for discoverability. Processes without this block will not appear in graph-based search results or recommendations.


Resources

  • SDK Reference: library/reference/sdk.md
  • Methodology Backlog: library/methodologies/backlog.md
  • Specializations Backlog: library/specializations/backlog.md
  • Example: ATDD/TDD: library/methodologies/atdd-tdd/
  • Example: Spec-Driven: library/methodologies/spec-driven-development.js
  • README: Root README.md for full framework documentation

Frequently asked questions

What does the Process Builder AI skill do?

Scaffold new babysitter process definitions following SDK patterns, proper structure, and best practices. Guides the 3-phase workflow from research to implementation.

Why use Process Builder on TypingMind?

Because you install it once and use it with any model. Process Builder 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 Process Builder in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/a5c-ai/babysitter/tree/main/.claude/skills/process-builder. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Process Builder?

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 Process Builder?

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

Is the Process Builder AI skill free?

Yes. It is published on GitHub by a5c-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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