agent-harness-design
SolidDesigning agent harnesses and tool systems — risk taxonomy for tools, permission decisions, draft/commit pattern, structured tool results, agent budgets (10 types), context trust labels against prompt injection, plan-artifact, approval records, observability and traces, evals (13 categories), event model, streaming buffering, 3rd-party skill install checklist, agentic RAG, self-improving SOP loops, model policy, reasoning effort, and Programmatic Tool Calling adoption gates. Use when building a new Agent SDK app, custom orchestrator, MCP server, Cloudflare Worker with tool calls, agentic RAG pipeline, model router, or model-tier policy; when designing tools and permissions; when writing an agent loop; or when you need trust labels for external content. Do NOT use for improving or auditing an already-built harness (use harness-audit / harness-design instead), nor for ordinary Claude Code sessions where the harness is already given.
Install
Quality Score: 87/100
Skill Content
Details
- Author
- AnastasiyaW
- Repository
- AnastasiyaW/codex-claude-code-config
- Created
- 5 months ago
- Last Updated
- 1 months ago
- Language
- Python
- License
- MIT
Integrates with
Bundled in these plugins
Similar Skills
Semantically similar based on skill content — not just same category
agent-harness-design
Use when designing tool definitions for a new agent or subagent, an agent shows high retry rates, ambiguous tool invocations, or silent failures, or an existing agent harness needs a quality review.
harness-engineering
Principles and judgment for designing an "agent harness" — the repo-level scaffolding (docs, feedback loops, constraints, memory, review gates) that lets AI coding agents work reliably and autonomously on a codebase. Use this whenever the user asks to make a repo more agent-friendly, set up or audit AGENTS.md/CLAUDE.md, improve how well Claude Code (or another agent) performs on their project, design feedback loops or verification gates for agents, decide what should go in project docs vs skills vs memory, or is generally trying to get better/more reliable results from AI coding agents on a specific codebase. Do not treat this as a rigid setup script — apply the principles to the specific project's language, size, team, and risk profile, and recommend only what that project actually needs.