skills/adynato/skills/adynato-aimake

adynato-aimake

SKILL.md

aimake Skill

Use this skill when integrating with aimake via MCP or leveraging its AI-powered delivery tools.

What is aimake?

aimake is an AI-powered delivery pipeline platform where cards are the deliverables, not tickets about work happening elsewhere. The AI drives work forward while humans bring taste and decision-making.

Key Concepts:

  • Cards are atomic, independently deliverable work units
  • Boards organize cards by type (Product, Engineering, Bugs, Docs)
  • Stages are quality gates that enforce professional standards
  • Card AI co-authors deliverables through conversation

Connecting via MCP

aimake exposes its functionality through MCP (Model Context Protocol).

Endpoints

GET  /mcp/manifest      # Returns available tool definitions
POST /mcp/tools/:name   # Executes a specific tool

Example Connection

// Fetch available tools
const manifest = await fetch('https://your-aimake-instance/mcp/manifest');
const tools = await manifest.json();

// Execute a tool
const result = await fetch('https://your-aimake-instance/mcp/tools/search_code_text', {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json',
    'Authorization': 'Bearer YOUR_API_KEY'
  },
  body: JSON.stringify({
    query: 'authentication',
    project_id: 'proj_123'
  })
});

Available MCP Tools

Code Tools

Search and analyze code in connected repositories.

Tool Description Key Parameters
search_code_semantic AI embeddings for conceptual matching query, project_id
search_code_text Ripgrep-based pattern matching query, project_id, file_pattern
read_file Get file content with line numbers path, project_id
list_directory Browse repository structure path, project_id
get_file_tree Complete code structure overview project_id

Example: Semantic Code Search

{
  "tool": "search_code_semantic",
  "input": {
    "query": "how is user authentication handled",
    "project_id": "proj_123"
  }
}

Documentation Tools

Access and search project documentation.

Tool Description Key Parameters
search_docs Full-text search across docs query, project_id
get_doc_page Retrieve specific page page_id, project_id
list_docs List all documentation pages project_id

Example: Search Documentation

{
  "tool": "search_docs",
  "input": {
    "query": "API rate limits",
    "project_id": "proj_123"
  }
}

Kanban Tools

Manage cards and projects in the delivery pipeline.

Tool Description Key Parameters
query_cards Search and filter cards project_id, board, stage, search
get_card Get card details card_id
update_card_field Update a card field card_id, field, value
move_card_to_stage Move card to different stage card_id, stage
spawn_cards Create new cards project_id, board, cards[]
get_project Get project details project_id

Example: Query Cards

{
  "tool": "query_cards",
  "input": {
    "project_id": "proj_123",
    "board": "engineering",
    "stage": "in_progress",
    "search": "authentication"
  }
}

Example: Create Cards

{
  "tool": "spawn_cards",
  "input": {
    "project_id": "proj_123",
    "board": "engineering",
    "cards": [
      { "title": "Add OAuth2 support", "stage": "backlog" },
      { "title": "Implement refresh tokens", "stage": "backlog" }
    ]
  }
}

Understanding Boards and Stages

Board Types

Board Purpose Use For
Product Specs and requirements Feature definitions, user stories
Engineering Implementation tasks Code work, technical tasks
Bugs Issue tracking Bug reports, fixes
Docs Documentation Project docs, guides

Stage Progression

Cards move through stages as work progresses:

Product Board:

problemStatement → acceptanceCriteria → technicalReview → ready

Engineering Board:

backlog → in_progress → review → done

Atomicity Rules

Each card should be atomic and independently deliverable:

Board What's Atomic
Product One shippable experience ("Welcome screen" not "Onboarding")
Engineering One independently shippable component
Bugs One specific, reproducible problem

Working with Card AI

Each card has an AI assistant that helps co-author the deliverable.

What Card AI Can Do

  • Fill in card fields based on conversation
  • Detect scope creep and suggest splitting cards
  • Validate readiness before stage transitions
  • Search code and docs for context
  • Break down large work into atomic cards

Integration Pattern

When building tools that interact with aimake cards:

// 1. Get card context
const card = await mcpTool('get_card', { card_id: 'card_123' });

// 2. Search for relevant code
const codeContext = await mcpTool('search_code_semantic', {
  query: card.title,
  project_id: card.project_id
});

// 3. Update card with findings
await mcpTool('update_card_field', {
  card_id: 'card_123',
  field: 'technicalNotes',
  value: `Relevant files:\n${codeContext.results.map(r => r.path).join('\n')}`
});

Common Integration Patterns

Triage Agent

Use aimake to analyze and route incoming requests:

// 1. Search docs for relevant context
const docs = await mcpTool('search_docs', { query: userRequest, project_id });

// 2. Search code for implementation details
const code = await mcpTool('search_code_semantic', { query: userRequest, project_id });

// 3. Create appropriate card
await mcpTool('spawn_cards', {
  project_id,
  board: isFeature ? 'product' : 'bugs',
  cards: [{ title: summarize(userRequest), stage: 'backlog' }]
});

Context Retrieval

Pull relevant context from aimake for AI conversations:

// Get project overview
const project = await mcpTool('get_project', { project_id });
const fileTree = await mcpTool('get_file_tree', { project_id });
const docs = await mcpTool('list_docs', { project_id });

// Search for specific context
const relevantCode = await mcpTool('search_code_text', {
  query: 'class UserAuth',
  project_id
});

Progress Tracking

Query cards to understand project state:

// Get all in-progress work
const activeWork = await mcpTool('query_cards', {
  project_id,
  stage: 'in_progress'
});

// Get cards related to a feature
const featureCards = await mcpTool('query_cards', {
  project_id,
  search: 'authentication',
  boards: ['product', 'engineering', 'bugs']
});

Tool Response Format

All MCP tools return JSON responses:

// Success
{
  "success": true,
  "data": { /* tool-specific response */ }
}

// Error
{
  "success": false,
  "error": "Error message"
}

Code Search Response

{
  "results": [
    {
      "path": "src/auth/login.ts",
      "lines": "45-67",
      "content": "...",
      "score": 0.92
    }
  ]
}

Card Response

{
  "id": "card_123",
  "title": "Add OAuth2 support",
  "board": "engineering",
  "stage": "in_progress",
  "data": {
    "problemStatement": "...",
    "acceptanceCriteria": "...",
    "technicalNotes": "..."
  }
}
Weekly Installs
7
Repository
adynato/skills
GitHub Stars
1
First Seen
Jan 21, 2026
Installed on
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gemini-cli6
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