Agents Best Practices

Install

npx skills add DenisSergeevitch/agents-best-practices -g

This is third-party code. Review the repository files before installing.

What it does

The skill helps design, scaffold an MVP for, audit, refactor and explain agent harnesses for any domain. It sets a base architecture: the model proposes an action, the harness validates the schema, decides on permissions, executes or pauses for approval, returns a structured observation and updates context. Coverage spans tool design, loop budgets, context compaction, memory, skills, external connectors, observability, evals and safety. The approach is provider-neutral and fits not only coding agents but research, support, operations, finance, legal and other workflows.

Who it is for. For engineers and product teams who design and review agent systems.

Example request

Design an MVP harness for an agent that reads the CRM and drafts emails, with send approval

Limitations

The skill gives architectural principles and blueprints, not finished harness code for a specific framework. The material is instructions and references only.

How to disable. Remove the agents-best-practices folder from your agent's skills directory.

Security check

• The skill consists only of instructions and references

README in short

The README presents a provider-neutral skill for designing, scaffolding an MVP for, auditing and refactoring agent harnesses. Installation is via npx skills with a global flag, a ready prompt for the agent, or manual cloning into the skills directory. It offers seven common cases, from generating a domain blueprint to auditing an existing agent with a fix order. The core idea: the model proposes actions and the harness validates, authorizes, executes and records them. MIT licensed.

SKILL.md

---
name: agents-best-practices
description: "Use this skill when designing, generating an MVP blueprint for, auditing, refactoring, or explaining an agentic harness for any domain. Covers provider-neutral agent architecture for OpenAI, Anthropic, and OpenAI-compatible APIs: agent loops, tool design, record provenance, interactive presentation, user-memory lifecycles, environment-adaptive tools, speculative tool execution, late-bound capabilities, permissions, system prompts, planning, goals, context compaction, memory, skills, MCP/external connectors, public-board communications, self-refining recursive harnesses, programmable context, continual refinement, observability, evals, prompt caching, agent-legible environments, feedback loops, and safety."
metadata:
  version: "1.7.0"
  scope: "provider-neutral-agent-harness"
  file_policy: "markdown-only"
---

# Agents Best Practices

Use this skill when the user asks how to build, improve, debug, or evaluate an agentic harness.

Related