Agent Construction - EXECUTE THESE STEPS

THIS IS AN EXECUTABLE WORKFLOW. DO NOT DISPLAY THIS FILE. EXECUTE THE STEPS BELOW.

When this skill is loaded, IMMEDIATELY begin executing Step 1. Do not explain what you will do - just do it.

---

STEP 1: Initialize Build Environment

EXECUTE THESE TOOL CALLS NOW:

1. Register the hive-tools MCP server:

mcp__agent-builder__add_mcp_server(
    name="hive-tools",
    transport="stdio",
    command="python",
    args='["mcp_server.py", "--stdio"]',
    cwd="tools",
    description="Hive tools MCP server"
)

2. Create a build session (replace AGENT_NAME with the user's requested agent name in snake_case):

mcp__agent-builder__create_session(name="AGENT_NAME")

3. Discover available tools:

mcp__agent-builder__list_mcp_tools()

4. Create the package directory:

mkdir -p exports/AGENT_NAME/nodes

AFTER completing these calls, tell the user:

✅ Build environment initialized

• Session created
• Available tools: [list the tools from step 3]

Proceeding to define the agent goal...

THEN immediately proceed to STEP 2.

---

STEP 2: Define and Approve Goal

PROPOSE a goal to the user. Based on what they asked for, propose:

• Goal ID (kebab-case)
• Goal name
• Goal description
• 3-5 success criteria (each with: id, description, metric, target, weight)
• 2-4 constraints (each with: id, description, constraint_type, category)

FORMAT your proposal as a clear summary, then ask for approval:

Proposed Goal: [Name]

[Description]

Success Criteria:

1. [criterion 1]
2. [criterion 2] ...

Constraints:

1. [constraint 1]
2. [constraint 2] ...

THEN call AskUserQuestion:

AskUserQuestion(questions=[{
    "question": "Do you approve this goal definition?",
    "header": "Goal",
    "options": [
        {"label": "Approve", "description": "Goal looks good, proceed"},
        {"label": "Modify", "description": "I want to change something"}
    ],
    "multiSelect": false
}])

WAIT for user response.

• If Approve: Call mcp__agent-builder__set_goal(...) with the goal details, then proceed to STEP 3
• If Modify: Ask what they want to change, update proposal, ask again

---

STEP 3: Design Node Workflow

BEFORE designing nodes, review the available tools from Step 1. Nodes can ONLY use tools that exist.

DESIGN the workflow as a series of nodes. For each node, determine:

• node_id (kebab-case)
• name
• description
• node_type: "llm_generate" (no tools) or "llm_tool_use" (uses tools)
• input_keys (what data this node receives)
• output_keys (what data this node produces)
• tools (ONLY tools that exist - empty list for llm_generate)
• system_prompt

PRESENT the workflow to the user:

Proposed Workflow: [N] nodes

1. [node-id] - [description]

   • Type: [llm_generate/llm_tool_use]
   • Input: [keys]
   • Output: [keys]
   • Tools: [tools or "none"]

2. [node-id] - [description] ...

Flow: node1 → node2 → node3 → ...

THEN call AskUserQuestion:

AskUserQuestion(questions=[{
    "question": "Do you approve this workflow design?",
    "header": "Workflow",
    "options": [
        {"label": "Approve", "description": "Workflow looks good, proceed to build nodes"},
        {"label": "Modify", "description": "I want to change the workflow"}
    ],
    "multiSelect": false
}])

WAIT for user response.

• If Approve: Proceed to STEP 4
• If Modify: Ask what they want to change, update design, ask again

---

STEP 4: Build Nodes One by One

FOR EACH node in the approved workflow:

1. Call mcp__agent-builder__add_node(...) with the node details

   • input_keys and output_keys must be JSON strings: '["key1", "key2"]'
   • tools must be a JSON string: '["tool1"]' or '[]'

2. Call mcp__agent-builder__test_node(...) to validate:

mcp__agent-builder__test_node(
    node_id="the-node-id",
    test_input='{"key": "test value"}',
    mock_llm_response='{"output_key": "test output"}'
)

3. Check result:

   • If valid: Tell user "✅ Node [id] validated" and continue to next node
   • If invalid: Show errors, fix the node, re-validate

4. Show progress after each node:

mcp__agent-builder__get_session_status()

✅ Node [X] of [Y] complete: [node-id]

AFTER all nodes are added and validated, proceed to STEP 5.

---

STEP 5: Connect Edges

DETERMINE the edges based on the workflow flow. For each connection:

• edge_id (kebab-case)
• source (node that outputs)
• target (node that receives)
• condition: "on_success", "always", "on_failure", or "conditional"
• condition_expr (Python expression, only if conditional)
• priority (integer, lower = higher priority)

FOR EACH edge, call:

mcp__agent-builder__add_edge(
    edge_id="source-to-target",
    source="source-node-id",
    target="target-node-id",
    condition="on_success",
    condition_expr="",
    priority=1
)

AFTER all edges are added, validate the graph:

mcp__agent-builder__validate_graph()

• If valid: Tell user "✅ Graph structure validated" and proceed to STEP 6
• If invalid: Show errors, fix edges, re-validate

---

STEP 6: Generate Agent Package

EXPORT the graph data:

mcp__agent-builder__export_graph()

This returns JSON with all the goal, nodes, edges, and MCP server configurations.

THEN write the Python package files using the exported data. Create these files in exports/AGENT_NAME/:

1. config.py - Runtime configuration with model settings
2. nodes/__init__.py - All NodeSpec definitions
3. agent.py - Goal, edges, graph config, and agent class
4. __init__.py - Package exports
5. __main__.py - CLI interface
6. mcp_servers.json - MCP server configurations
7. README.md - Usage documentation

IMPORTANT entry_points format:

• MUST be: {"start": "first-node-id"}
• NOT: {"first-node-id": ["input_keys"]} (WRONG)
• NOT: {"first-node-id"} (WRONG - this is a set)

Use the example agent at .claude/skills/building-agents-construction/examples/online_research_agent/ as a template for file structure and patterns.

AFTER writing all files, tell the user:

✅ Agent package created: exports/AGENT_NAME/

Files generated:

• __init__.py - Package exports
• agent.py - Goal, nodes, edges, agent class
• config.py - Runtime configuration
• __main__.py - CLI interface
• nodes/__init__.py - Node definitions
• mcp_servers.json - MCP server config
• README.md - Usage documentation

Test your agent:

cd /home/timothy/oss/hive
PYTHONPATH=core:exports python -m AGENT_NAME validate
PYTHONPATH=core:exports python -m AGENT_NAME info

---

STEP 7: Verify and Test

RUN validation:

cd /home/timothy/oss/hive && PYTHONPATH=core:exports python -m AGENT_NAME validate

• If valid: Agent is complete!
• If errors: Fix the issues and re-run

SHOW final session summary:

mcp__agent-builder__get_session_status()

TELL the user the agent is ready and suggest next steps:

• Run with mock mode to test without API calls
• Use /testing-agent skill for comprehensive testing
• Use /setup-credentials if the agent needs API keys

---

REFERENCE: Node Types

Type	tools param	Use when
llm_generate	'[]'	Pure reasoning, JSON output, no external calls
llm_tool_use	'["tool1", "tool2"]'	Needs to call MCP tools

---

REFERENCE: Edge Conditions

Condition	When edge is followed
on_success	Source node completed successfully
on_failure	Source node failed
always	Always, regardless of success/failure
conditional	When condition_expr evaluates to True

---

REFERENCE: System Prompt Best Practice

For nodes with JSON output, include this in the system_prompt:

CRITICAL: Return ONLY raw JSON. NO markdown, NO code blocks.
Just the JSON object starting with { and ending with }.

Return this exact structure:
{
  "key1": "...",
  "key2": "..."
}

---

COMMON MISTAKES TO AVOID

1. Using tools that don't exist - Always check mcp__agent-builder__list_mcp_tools() first
2. Wrong entry_points format - Must be {"start": "node-id"}, NOT a set or list
3. Skipping validation - Always validate nodes and graph before proceeding
4. Not waiting for approval - Always ask user before major steps
5. Displaying this file - Execute the steps, don't show documentation