Hive Create

Agent Construction - EXECUTE THESE STEPS

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

CRITICAL: DO NOT explore the codebase, read source files, or search for code before starting. All context you need is in this skill file. When this skill is loaded, IMMEDIATELY begin executing Step 1 — call the MCP tools listed in Step 1 as your FIRST action. Do not explain what you will do, do not investigate the project structure, do not read any files — just execute Step 1 now.

STEP 1: Initialize Build Environment

EXECUTE THESE TOOL CALLS NOW (silent setup — no user interaction needed):

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

Save the tool list for step 3 — you will need it for node design in STEP 3.

THEN immediately proceed to STEP 2 (do NOT display setup results to the user — just move on).

STEP 2: Define Goal Together with User

DO NOT propose a complete goal on your own. Instead, collaborate with the user to define it.

START by asking the user to help shape the goal:

I've set up the build environment and discovered [N] available tools. Let's define the goal for your agent together.

To get started, can you help me understand:

1. What should this agent accomplish? (the core purpose)
2. How will we know it succeeded? (what does "done" look like)
3. Are there any hard constraints? (things it must never do, quality bars, etc.)

WAIT for the user to respond. Use their input to draft:

• 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)

PRESENT the draft goal 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 to workflow design"},
        {"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 the draft, ask again

STEP 3: Design Conceptual Nodes

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: "event_loop" (recommended for all LLM work) or "function" (deterministic, no LLM)
• input_keys (what data this node receives)
• output_keys (what data this node produces)
• tools (ONLY tools that exist from Step 1 — empty list if no tools needed)
• client_facing: True if this node interacts with the user
• nullable_output_keys (for mutually exclusive outputs or feedback-only inputs)
• max_node_visits (>1 if this node is a feedback loop target)

Prefer fewer, richer nodes (4 nodes > 8 thin nodes). Each node boundary requires serializing outputs. A research node that searches, fetches, and analyzes keeps all source material in its conversation history.

PRESENT the nodes to the user for review:

Proposed Nodes ([N] total):

#	Node ID	Type	Description	Tools	Client-Facing
1	intake	event_loop	Gather requirements from user	—	Yes
2	research	event_loop	Search and analyze sources	web_search, web_scrape	No
3	review	event_loop	Present findings for approval	—	Yes
4	report	event_loop	Generate final report	save_data	No

Data Flow:

• intake produces: research_brief
• research receives: research_brief → produces: findings, sources
• review receives: findings, sources → produces: approved_findings or feedback
• report receives: approved_findings → produces: final_report

THEN call AskUserQuestion:

AskUserQuestion(questions=[{
    "question": "Do you approve these nodes?",
    "header": "Nodes",
    "options": [
        {"label": "Approve", "description": "Nodes look good, proceed to graph design"},
        {"label": "Modify", "description": "I want to change the nodes"}
    ],
    "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: Design Full Graph and Review

DETERMINE the edges connecting the approved nodes. For each edge:

• edge_id (kebab-case)
• source → target
• condition: on_success, on_failure, always, or conditional
• condition_expr (Python expression, only if conditional)
• priority (positive = forward, negative = feedback/loop-back)

RENDER the complete graph as ASCII art. Make it large and clear — the user needs to see and understand the full workflow at a glance.

IMPORTANT: Make the ASCII art BIG and READABLE. Use a box-and-arrow style with generous spacing. Do NOT make it tiny or compressed. Example format:

┌─────────────────────────────────────────────────────────────────────────────┐
│                           AGENT: Research Agent                            │
│                                                                            │
│  Goal: Thoroughly research technical topics and produce verified reports   │
└─────────────────────────────────────────────────────────────────────────────┘

    ┌───────────────────────┐
    │       INTAKE          │
    │  (client-facing)      │
    │                       │
    │  in:  topic           │
    │  out: research_brief  │
    └───────────┬───────────┘
                │ on_success
                ▼
    ┌───────────────────────┐
    │      RESEARCH         │
    │                       │
    │  tools: web_search,   │
    │         web_scrape    │
    │                       │
    │  in:  research_brief  │
    │       [feedback]      │
    │  out: findings,       │
    │       sources         │
    └───────────┬───────────┘
                │ on_success
                ▼
    ┌───────────────────────┐
    │       REVIEW          │
    │  (client-facing)      │
    │                       │
    │  in:  findings,       │
    │       sources         │
    │  out: approved_findings│
    │       OR feedback     │
    └───────┬───────┬───────┘
            │       │
   approved │       │ feedback (priority: -1)
            │       │
            ▼       └──────────────────┐
    ┌───────────────────────┐          │
    │       REPORT          │          │
    │                       │          │
    │  tools: save_data     │          │
    │                       │          │
    │  in:  approved_       │          │
    │       findings        │          │
    │  out: final_report    │          │
    └───────────────────────┘          │
                                       │
            ┌──────────────────────────┘
            │ loops back to RESEARCH
            ▼ (max_node_visits: 3)


    EDGES:
    ──────
    1. intake → research         [on_success, priority: 1]
    2. research → review         [on_success, priority: 1]
    3. review → report           [conditional: approved_findings is not None, priority: 1]
    4. review → research         [conditional: feedback is not None, priority: -1]

PRESENT the graph and edges to the user:

Here is the complete workflow graph:

[ASCII art above]

Edge Summary:

#	Edge	Condition	Priority
1	intake → research	on_success	1
2	research → review	on_success	1
3	review → report	conditional: approved_findings is not None	1
4	review → research	conditional: feedback is not None	-1

THEN call AskUserQuestion:

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

WAIT for user response.

• If Approve: Proceed to STEP 5
• If Modify: Ask what they want to change, update the graph, re-render, ask again

STEP 5: Build the Agent

NOW — and only now — write the actual code. The user has approved the goal, nodes, and graph.

5a: Register nodes and edges with MCP

FOR EACH approved node, call:

mcp__agent-builder__add_node(
    node_id="...",
    name="...",
    description="...",
    node_type="event_loop",
    input_keys='["key1", "key2"]',
    output_keys='["key1"]',
    tools='["tool1"]',
    system_prompt="...",
    client_facing=True/False,
    nullable_output_keys='["key"]',
    max_node_visits=1
)

FOR EACH approved 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
)

VALIDATE the graph:

mcp__agent-builder__validate_graph()

• If invalid: Fix the issues and re-validate
• If valid: Continue to 5b

5b: Write Python package files

EXPORT the graph data:

mcp__agent-builder__export_graph()

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/hive-create/examples/deep_research_agent/ as a template for file structure and patterns. It demonstrates: STEP 1/STEP 2 prompts, client-facing nodes, feedback loops, nullable_output_keys, and data tools.

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

STEP 6: Verify and Test

RUN validation:

cd /home/timothy/oss/hive && PYTHONPATH=exports uv run python -m AGENT_NAME validate

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

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

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

REFERENCE: Node Types

REFERENCE: NodeSpec Fields

REFERENCE: Edge Conditions & Priority

Priority: Positive = forward edge (evaluated first). Negative = feedback edge (loops back to earlier node). Multiple ON_SUCCESS edges from same source = parallel execution (fan-out).

REFERENCE: System Prompt Best Practice

For internal event_loop nodes (not client-facing), instruct the LLM to use set_output:

Use set_output(key, value) to store your results. For example:
- set_output("search_results", <your results as a JSON string>)

Do NOT return raw JSON. Use the set_output tool to produce outputs.

For client-facing event_loop nodes, use the STEP 1/STEP 2 pattern:

**STEP 1 — Respond to the user (text only, NO tool calls):**
[Present information, ask questions, etc.]

**STEP 2 — After the user responds, call set_output:**
- set_output("key", "value based on user's response")

This prevents the LLM from calling set_output before the user has had a chance to respond. The "NO tool calls" instruction in STEP 1 ensures the node blocks for user input before proceeding.

EventLoopNode Runtime

EventLoopNodes are auto-created by GraphExecutor at runtime. Both direct GraphExecutor and AgentRuntime / create_agent_runtime() handle event_loop nodes automatically. No manual node_registry setup is needed.

# Direct execution
from framework.graph.executor import GraphExecutor
from framework.runtime.core import Runtime

storage_path = Path.home() / ".hive" / "my_agent"
storage_path.mkdir(parents=True, exist_ok=True)
runtime = Runtime(storage_path)

executor = GraphExecutor(
    runtime=runtime,
    llm=llm,
    tools=tools,
    tool_executor=tool_executor,
    storage_path=storage_path,
)
result = await executor.execute(graph=graph, goal=goal, input_data=input_data)

DO NOT pass runtime=None to GraphExecutor — it will crash with 'NoneType' object has no attribute 'start_run'.

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
6. Too many thin nodes - Prefer fewer, richer nodes (4 nodes > 8 nodes)
7. Missing STEP 1/STEP 2 in client-facing prompts - Client-facing nodes need explicit phases to prevent premature set_output
8. Forgetting nullable_output_keys - Mark input_keys that only arrive on certain edges (e.g., feedback) as nullable on the receiving node
9. Adding framework gating for LLM behavior - Fix prompts or use judges, not ad-hoc code
10. Writing code before user approves the graph - Always get approval on goal, nodes, and graph BEFORE writing any agent code