forge-tool-use

Enforce capped, validated tool-use loops with structured error handling.

Updated Aug 23, 2026
One-click install
npx skills add https://github.com/f4rkh4d/forge-skill --skill forge-tool-use
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: forge-tool-use
Source: https://github.com/f4rkh4d/forge-skill/tree/main/skills/llm/forge-tool-use
Command: npx skills add https://github.com/f4rkh4d/forge-skill --skill forge-tool-use

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) and assets (resource) components.

What problem does it solve?

Unchecked LLM tool/function loops waste tokens, can spin forever, and often fail by throwing exceptions or returning unstructured results, leading to brittle agent behavior.

Core Features & Use Cases

  • Hygienic tool-call loop design: enforces a hard iteration cap, structured loop events, and predictable termination.
  • Validated structured inputs/outputs: validates tool arguments and tool outputs against schemas to enable recovery from “model called tool wrong” vs “tool execution failed.”
  • Robust error-as-data handling: returns tool failures as structured data instead of letting exceptions crash the loop.

Quick Start

Instruct your AI agent to follow the forge-tool-use discipline by using a tool-use loop with a fixed max iterations, validating tool inputs/outputs, and returning tool results as structured content (not stringified prose) even when failures occur.

Frequently Asked Questions about forge-tool-use

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
Why does my LLM agent loop spin forever when making parallel tool calls?▼

LLM tool loops spin forever without a hard iteration cap. Applying a capped, observable tool-use loop enforces predictable termination, stopping runaway behavior and wasted tokens during parallel tool calls.

How do I structure tool errors as data instead of crashing the agent loop?▼

To structure tool errors as data, return tool failures as structured content rather than letting exceptions crash the loop. This robust error-as-data handling enables the LLM to recover from execution failures.

What is the best way to validate function calling arguments in an agent workflow?▼

The best way to validate function calling arguments is using schema-driven tool definitions. Validating structured inputs and outputs against schemas distinguishes between a wrong model tool call and an actual tool execution failure.

Do I need schema validation for parallel tool calls in LLM applications?▼

Yes, schema validation is essential for parallel tool calls. Validating tool arguments and outputs against schemas enables recovery from execution failures and ensures predictable behavior across multiple concurrent function calls.

Can I use context budgeting to prevent unbounded token consumption in tool loops?▼

Context budgeting prevents unbounded token consumption by managing the context window during iterative tool execution. Combined with a fixed max iteration loop, it ensures predictable termination and reduces wasted tokens.

How to fix brittle agent behavior when tool outputs return unstructured results?▼

Fix brittle agent behavior by formatting tool outputs as structured content instead of stringified prose. Disciplined result formatting combined with schema validation ensures reliable consumption of tool execution results.