What problem does it solve?
This Skill addresses the common issue of AI agents failing to complete tasks due to poorly designed tool action spaces, uninformative observation outputs, and weak error recovery mechanisms that leave agents stuck on failures.
Core Features & Use Cases
- Action Space Design: Guidelines for building stable, granular tool sets with narrow input schemas and deterministic output shapes to reduce agent confusion and invalid tool calls.
- Observation Formatting: Standards for structuring tool responses with clear status indicators, one-line summaries, actionable next steps, and artifact references to improve agent decision-making.
- Error Recovery & Context Budgeting: Contracts for handling error paths with root cause hints and safe retry instructions, plus rules for optimizing context usage to avoid overload and reduce token waste.
- Use Case: A team building a research AI agent can use this Skill to define micro-tools for high-risk operations like web scraping, format tool outputs to include follow-up actions, and implement context compaction at phase boundaries to boost the agent's task completion rate.
Quick Start
Use the agent-harness-construction skill to redesign the tool action space and observation format for your existing code-editing AI agent to improve its first-attempt success rate.
