What problem does it solve?
This skill provides a framework for designing and optimizing AI agent harnesses, focusing on action space clarity, tool definitions, observation formatting, and robust error recovery to improve task completion rates.
Core Features & Use Cases
- Action Space Design: Use stable, explicit tool names; keep inputs schema-first; deterministic output shapes; avoid catch-all tools unless necessary.
- Granularity Rules: Use micro-tools for high-risk operations; use medium tools for common edit/read/search loops; macro-tools only when round-trip overhead is the dominant cost.
- Observation Design: Every tool response should include:
status: success|warning|error; summary: one-line result; next_actions: actionable follow-ups; artifacts: file paths / IDs.
- Error Recovery Contract: For every error path, include: root cause hint; safe retry instruction; explicit stop condition.
- Context Budgeting: Keep system prompt minimal and invariant; move large guidance into skills loaded on demand; reference files instead of inlining long documents.
- Architecture Pattern Guidance: ReAct: best for exploratory tasks with uncertain path; Function-calling: best for structured deterministic flows; Hybrid (recommended): ReAct planning + typed tool execution.
- Benchmarking: Track: completion rate; retries per task; pass@1 and pass@3; cost per successful task.
- Anti-Patterns: Too many tools with overlapping semantics; opaque tool output with no recovery hints; error-only output without next steps; context overloading with irrelevant references.
Quick Start
Use the agent harness construction skill to describe and implement an agent harness that optimizes action spaces, tool interfaces, observations, and error recovery for reliable autonomous task completion.
