Agent Tool Builder workflow skill. Use this skill when the user needs Tools are how AI agents interact with the world. A well-designed and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
A direct command skips the review prompt. Inspect the source before running it.
Agent Tool Builder workflow skill. Use this skill when the user needs Tools are how AI agents interact with the world. A well-designed and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
This public intake copy packages plugins/antigravity-awesome-skills/skills/agent-tool-builder from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.
Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.
This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.
Agent Tool Builder Tools are how AI agents interact with the world. A well-designed tool is the difference between an agent that works and one that hallucinates, fails silently, or costs 10x more tokens than necessary. This skill covers tool design from schema to error handling. JSON Schema best practices, description writing that actually helps the LLM, validation, and the emerging MCP standard that's becoming the lingua franca for AI tools. Key insight: Tool descriptions are more important than tool implementations. The LLM never sees your code - it only sees the schema and description.
Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Capabilities, Scope, Tooling, Patterns, 1. Detailed Descriptions (Most Important), 2. Parameter Descriptions.
When to Use This Skill
Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.
User mentions or implies: agent tool
User mentions or implies: function calling
User mentions or implies: tool schema
User mentions or implies: tool design
User mentions or implies: mcp server
User mentions or implies: mcp tool
Operating Table
Situation
Start here
Why it matters
First-time use
metadata.json
Confirms repository, branch, commit, and imported path through the external_source block before touching the copied workflow
Provenance review
ORIGIN.md
Gives reviewers a plain-language audit trail for the imported source
Workflow execution
SKILL.md
Starts with the smallest copied file that materially changes execution
Supporting context
SKILL.md
Adds the next most relevant copied source file without loading the entire package
Handoff decision
## Related Skills
Helps the operator switch to a stronger native skill when the task drifts
Workflow
This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.
Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
Read the overview and provenance files before loading any copied upstream support files.
Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
Validate the result against the upstream expectations and the evidence you can point to in the copied files.
Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
Before merge or closure, record what was used, what changed, and what the reviewer still needs to verify.
Imported Workflow Notes
Imported: Capabilities
agent-tools
function-calling
tool-schema-design
mcp-tools
tool-validation
tool-error-handling
Examples
Example 1: Ask for the upstream workflow directly
Use @agent-tool-builder-v2 to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.
Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.
Example 2: Ask for a provenance-grounded review
Review @agent-tool-builder-v2 against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.
Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.
Example 3: Narrow the copied support files before execution
Use @agent-tool-builder-v2 for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.
Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.
Example 4: Build a reviewer packet
Review @agent-tool-builder-v2 using the copied upstream files plus provenance, then summarize any gaps before merge.
Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.
Imported Usage Notes
Imported: Python Example
"""
import anthropic
from anthropic import beta_tool
client = anthropic.Anthropic()
@beta_tool
def get_weather(location: str, unit: str = "fahrenheit") -> str:
'''Get the current weather in a given location.
Args:
location: The city and state, e.g. San Francisco, CA
unit: Temperature unit, either 'celsius' or 'fahrenheit'
'''
# Implementation
return json.dumps({"temperature": "72°F", "conditions": "Sunny"})
@beta_tool
def search_web(query: str) -> str:
'''Search the web for information.
runner = client.beta.messages.tool_runner(
model="claude-sonnet-4-5",
max_tokens=1024,
tools=[get_weather, search_web],
messages=[
{"role": "user", "content": "What's the weather in Paris?"}
]
)
Process each message
for message in runner:
print(message.content[0].text)
Or just get final result
final = runner.until_done()
"""
Best Practices
Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.
Description quality > implementation quality for LLM accuracy
Aim for fewer than 20 tools - more causes confusion
Validation gates before execution - reject, fix, or escalate, never silent fail
Test tools with the LLM, not just unit tests
Troubleshooting
Problem: The operator skipped the imported context and answered too generically
Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills/skills/agent-tool-builder, fails to mention provenance, or does not use any copied source files at all.
Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.
Problem: The imported workflow feels incomplete during review
Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task.
Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.
Problem: The task drifted into a different specialization
Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better.
Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.
Related Skills
@00-andruia-consultant - Use when the work is better handled by that native specialization after this imported skill establishes context.
@00-andruia-consultant-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
@10-andruia-skill-smith - Use when the work is better handled by that native specialization after this imported skill establishes context.
@10-andruia-skill-smith-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
Additional Resources
Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.
Resource family
What it gives the reviewer
Example path
references
copied reference notes, guides, or background material from upstream
references/n/a
examples
worked examples or reusable prompts copied from upstream
examples/n/a
scripts
upstream helper scripts that change execution or validation
scripts/n/a
agents
routing or delegation notes that are genuinely part of the imported package
agents/n/a
assets
supporting assets or schemas copied from the source package
GOOD - Comprehensive:
{
"name": "get_stock_price",
"description": "Retrieves the current stock price for a given ticker
symbol. The ticker symbol must be a valid symbol for a publicly
traded company on a major US stock exchange like NYSE or NASDAQ.
Returns the latest trade price in USD. Use when the user asks
about current or recent stock prices. Does NOT provide historical
data, company info, or predictions.",
"input_schema": {
"type": "object",
"properties": {
"ticker": {
"type": "string",
"description": "The stock ticker symbol, e.g. AAPL for Apple Inc."
}
},
"required": ["ticker"]
}
}
"""
Imported: 2. Parameter Descriptions
"""
Every parameter needs:
What it is
Format expected
Example value
Edge cases/limitations
{
"location": {
"type": "string",
"description": "City and state/country. Format: 'City, State' for US
(e.g., 'San Francisco, CA') or 'City, Country' for international
(e.g., 'Tokyo, Japan'). Do not use ZIP codes or coordinates."
},
"unit": {
"type": "string",
"enum": ["celsius", "fahrenheit"],
"description": "Temperature unit. Defaults to user's locale if not
specified. Use 'fahrenheit' for US users, 'celsius' for others."
}
}
"""
- Show minimal, partial, and full specification patterns
- Keep concise: 1-5 examples per tool
- Focus on ambiguous cases
Tool Error Handling
Returning errors that help the LLM recover
When to use: Any tool that can fail
ERROR HANDLING BEST PRACTICES:
Imported: Return Informative Errors
"""
BAD:
{"error": "Failed"}
{"error": true}
GOOD:
{
"error": true,
"error_type": "not_found",
"message": "Location 'Atlantis' not found in weather database.
Please provide a real city name like 'San Francisco, CA'.",
"suggestions": ["San Francisco, CA", "Los Angeles, CA"]
}
"""
Imported: Anthropic Tool Result with Error
"""
{
"type": "tool_result",
"tool_use_id": "toolu_01A09q90qw90lq917835lq9",
"content": "Error: Location 'Atlantis' not found in weather database.
Please provide a real city name like 'San Francisco, CA'.",
"is_error": true
}
"""
Imported: Error Categories to Handle
"""
Input Validation Errors
Missing required parameters
Invalid format
Out of range values
External Service Errors
API unavailable
Rate limited
Timeout
Business Logic Errors
Resource not found
Permission denied
Conflict/duplicate
Internal Errors
Unexpected exceptions
Data corruption
"""
Imported: Implementation Pattern
"""
from dataclasses import dataclass
from typing import Union
def get_weather(location: str) -> ToolResult:
# Validate input
if not location or len(location) < 2:
return ToolResult(
success=False,
content="Location must be at least 2 characters",
error_type="validation_error"
)
try:
data = weather_api.fetch(location)
return ToolResult(
success=True,
content=f"Temperature: {data.temp}°F, Conditions: {data.conditions}"
)
except LocationNotFound:
return ToolResult(
success=False,
content=f"Location '{location}' not found",
error_type="not_found",
suggestions=weather_api.suggest_locations(location)
)
except RateLimitError:
return ToolResult(
success=False,
content="Weather service rate limit exceeded. Try again in 60 seconds.",
error_type="rate_limit"
)
except Exception as e:
return ToolResult(
success=False,
content=f"Unexpected error: {str(e)}",
error_type="internal_error"
)
"""
MCP Tool Pattern
Building tools using Model Context Protocol
When to use: Creating reusable, cross-platform tools
MCP TOOL IMPLEMENTATION:
"""
MCP (Model Context Protocol) is Anthropic's open standard for
connecting AI agents to external systems. Build once, use everywhere.
"""
Imported: Basic MCP Server (TypeScript)
"""
import { Server } from "@modelcontextprotocol/sdk/server";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio";
const server = new Server({
name: "weather-server",
version: "1.0.0"
});
// Define tools
server.setRequestHandler("tools/list", async () => ({
tools: [
{
name: "get_weather",
description: "Get current weather for a location. Returns
temperature, conditions, and humidity. Use for weather
queries about specific cities.",
inputSchema: {
type: "object",
properties: {
location: {
type: "string",
description: "City and state, e.g. 'San Francisco, CA'"
},
unit: {
type: "string",
enum: ["celsius", "fahrenheit"],
default: "fahrenheit"
}
},
required: ["location"]
}
}
]
}));
"""
Add to system prompt:
"For maximum efficiency, whenever you need to perform multiple
independent operations, invoke all relevant tools simultaneously
rather than sequentially."
"""
Imported: Validation Checks
Tool Description Must Be Comprehensive
Severity: WARNING
Tool descriptions should be at least 100 characters
Message: Tool description is too short. Add details about when to use it, parameters, and return values.
Parameter Descriptions Required
Severity: WARNING
Every parameter should have a description
Message: Parameter missing description. Describe what it is and the expected format.
