oa-agent-library-builder

Category: Tools & Productivity | Uploader: OpenAEC-Foundation | Downloads: 0 | Version: v1.0（Latest）

Grows the agent library from successful oa runs. Use when reviewing completed agent output that follows a clear, repeatable pattern, or when the user mentions saving an agent as a template. Activates for: oa collect shows success, save as template, make reusable, add to library.

Changelog: Source: GitHub https://github.com/OpenAEC-Foundation/Open-Agents

Directory Structure

Current level: .claude/skills/oa-agent-library-builder/

📄 SKILL.md 6.0 KB

SKILL.md

---
name: oa-agent-library-builder
user-invocable: false
description: "Grows the agent library from successful oa runs. Use when reviewing completed agent output that follows a clear, repeatable pattern, or when the user mentions saving an agent as a template. Activates for: oa collect shows success, save as template, make reusable, add to library."
disable-model-invocation: true
---

## Critical Rules

ALWAYS save a template when an agent run produces a clear, repeatable pattern — reuse prevents redundant spawning.

NEVER create a template for one-off tasks — templates add maintenance cost without reuse value.

## Template Extraction Process

### Step 1: Read the Source Material

Read the completed agent's workspace to extract task definition and result:

```bash
cat <workspace>/CLAUDE.md
cat <workspace>/output/result.md
```

### Step 2: Analyze the Pattern

Determine these properties from the source material:
- **Task category**: What domain does this belong to? (code-dev, data-transform, review-quality, etc.)
- **Tools needed**: Which Claude Code tools did the agent use? (Read, Write, Edit, Glob, Grep, Bash, WebFetch, WebSearch)
- **Complexity**: Is this a parsing task, standard implementation, or complex reasoning?
- **Atomicity**: Can this run as a single prompt without multi-step orchestration?

### Step 3: Assign Model Hint

Select the smallest model that can reliably perform the task because each saved template means future runs use the optimal model instead of defaulting to a heavier one:

| Task Type | Model | When to Use |
|-----------|-------|-------------|
| Parsing, validation, formatting, simple transforms | `anthropic/claude-haiku-4-5-20251001` | Well-defined input/output, no ambiguity |
| Standard implementation, code generation, analysis | `anthropic/claude-sonnet-4-6` | Requires reasoning but follows known patterns |
| Complex orchestration, architecture, multi-domain | `anthropic/claude-opus-4-6` | Needs deep reasoning or creative problem-solving |

Default to Haiku when possible. Every Haiku agent saves significant tokens over Sonnet/Opus.

### Step 4: Generate the Template JSON

Create a JSON file following this exact format:

```json
{
  "name": "Descriptive Agent Name",
  "description": "One-line description of what this agent does and when to use it.",
  "model": "anthropic/claude-haiku-4-5-20251001",
  "systemPrompt": "You are a [role] that [primary task].\n\nOutput format: [describe expected output].\n\nRules:\n1. [First rule]\n2. [Second rule]\n3. [Additional rules as needed]",
  "tools": ["Read", "Glob", "Grep"],
  "maturity": "tool-capable",
  "category": "code-dev",
  "tags": ["tag1", "tag2", "tag3"]
}
```

Field requirements:
- **name**: Title case, 2-4 words, action-oriented (e.g., "Validate IFC Psets")
- **description**: Single sentence, starts with verb, explains purpose and scope
- **model**: Full model identifier from the model hint table above
- **systemPrompt**: Clear role, output format, and numbered rules. Max 500 words. Must be self-contained.
- **tools**: Minimum set required. Read-only agents use `["Read", "Glob", "Grep"]`. Writing agents add `"Write"` and `"Edit"`. Shell agents add `"Bash"`.
- **maturity**: One of `"prompt-only"`, `"tool-capable"`, `"orchestrator"`
- **category**: Must match an existing directory in `agents/library/`
- **tags**: 2-5 lowercase keywords for discovery

### Step 5: Quality Gate

Before writing the template, verify ALL of these conditions:

1. **Single purpose**: The systemPrompt describes exactly one task. If it says "and also", split into two agents.
2. **No duplicates**: Search `agents/library/` for agents with similar names, descriptions, or tags.

```bash
grep -rl "<keyword>" agents/library/
```

3. **Testable output**: The template defines a clear output format that can be verified.
4. **Minimal tools**: Remove any tool the agent does not strictly need.

### Step 6: Write the Template File

Save to `agents/library/<category>/<topic>.json`:

```
agents/library/code-dev/validate-ifc-psets.json
agents/library/data-transform/csv-to-json.json
agents/library/review-quality/check-naming-conventions.json
```

Existing categories in `agents/library/`:
- `aec-blender`, `aec-bonsai`, `aec-cross`, `aec-ifcopenshell`, `aec-sverchok`
- `code-dev`, `communication`, `core`, `data-transform`
- `erpnext`, `file-system`, `git-versioning`, `research`
- `review-quality`, `text-language`

If no category fits, create a new directory and document why in DECISIONS.md.

## Complete Example

```json
{
  "name": "Validate IFC Psets",
  "description": "Validates IFC property set names and values against a reference schema.",
  "model": "anthropic/claude-haiku-4-5-20251001",
  "systemPrompt": "You are an IFC property set validator.\n\nRead the provided IFC file or extracted property sets and validate them against the reference schema.\n\nOutput format: For each property set, report:\n- Pset name: PASS/FAIL\n- Missing required properties\n- Invalid value types\n- Summary: X/Y property sets valid\n\nRules:\n1. Read the target file before analysis.\n2. Compare each Pset_ prefixed set against the schema.\n3. Flag missing required properties as FAIL.\n4. Flag type mismatches (string vs integer vs enum).\n5. Report results sorted by severity (FAIL first, then PASS).\n6. Do not modify any files — this is a read-only validation.",
  "tools": ["Read", "Glob", "Grep"],
  "maturity": "tool-capable",
  "category": "aec-ifcopenshell",
  "tags": ["ifc", "validation", "pset", "bim"]
}
```

## Token Efficiency Reminder

- Prefer Haiku for any task with well-defined input/output and no ambiguity.
- Each saved template means future runs use the optimal model instead of defaulting to Opus.
- A library of 50 Haiku agents replaces 50 ad-hoc Opus calls — significant cost reduction.
- Review existing templates periodically: if a Sonnet agent could be Haiku, downgrade it.

## Reference
- Related: `oa-library-templates` — template format and field specifications
- Related: `oa-quality-gates` — validate batch of generated templates before committing

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