What problem does it solve?
This Skill solves the widespread issue of AI agents failing to complete tasks due to poorly designed action spaces, uninformative tool outputs, and weak error recovery logic that leads to dead ends and unnecessary retries.
Core Features & Use Cases
- Action Space Design: Guidance for defining granular, schema-first tools (micro/medium/macro) matched to operation risk to eliminate overlapping tool semantics.
- Standardized Observations: Rules for formatting all tool outputs with status, summary, next actions, and artifacts to remove opaque, unhelpful responses.
- Error Recovery Contracts: Templates for building consistent error paths with root cause hints, safe retry instructions, and explicit stop conditions to reduce agent failures.
- Context Budgeting: Best practices for minimizing system prompt size, loading guidance on demand, and compacting context at phase boundaries to avoid overloading.
- Use Case: A team building a code review agent can use this skill to structure its tool calls, ensure clear error messages for failed file lookups, and track completion rates to iteratively improve performance.
Quick Start
Use the agent-harness-construction skill to redesign the tool action space and observation format for your existing customer support agent to improve its first-attempt task completion rate.
