003 Name Skill 67718b9a

---
name: hive-create
description: Step-by-step guide for building goal-driven agents. Creates package structure, defines goals, adds nodes, connects edges, and finalizes agent class. Use when actively building an agent.
license: Apache-2.0
metadata:
  author: hive
  version: "2.1"
  type: procedural
  part_of: hive
  requires: hive-concepts
---

# 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:

```bash
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:**

```bash
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

| Type         | tools param             | Use when                                |
| ------------ | ----------------------- | --------------------------------------- |
| `event_loop` | `'["tool1"]'` or `'[]'` | LLM-powered work with or without tools  |
| `function`   | N/A                     | Deterministic Python operations, no LLM |

---

## REFERENCE: NodeSpec Fields

| Field                  | Default | Description                                                           |
| ---------------------- | ------- | --------------------------------------------------------------------- |
| `client_facing`        | `False` | Streams output to user, blocks for input between turns                |
| `nullable_output_keys` | `[]`    | Output keys that may remain unset (mutually exclusive outputs)        |
| `max_node_visits`      | `1`     | Max executions per run. Set >1 for feedback loop targets. 0=unlimited |

---

## REFERENCE: Edge Conditions & Priority

| 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 |

**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.

```python
# 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