/agents-best-practices

name: agents-best-practices
description: "Use this skill when designing, generating an MVP blueprint for, auditing, troubleshooting, 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, always-on agents and durable runtime, adaptive agent teams, context compaction, memory, skills, MCP/external connectors, public-board communications, realtime voice agents, hardware agents and board deployment, self-refining recursive harnesses, programmable context, continual refinement, observability, evals, prompt caching, agent-legible environments, feedback loops, and safety."
metadata:
  version: "1.18.0"
  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. This is a general-purpose agent architecture skill. Coding agents are one subdomain only; apply the same principles to research, finance, legal, support, operations, sales, healthcare, education, data analysis, procurement, and workflow automation agents.

Freshness and self-update

Canonical source: [DenisSergeevitch/agents-best-practices](https://github.com/DenisSergeevitch/agents-best-practices), branch `main`. Installed copies are snapshots, not guaranteed-current guidance.

Before applying this skill in each new task:

1. **Check upstream once.** Resolve the current `main` commit through an approved network tool and compare it with the installed copy's recorded source revision. A version number alone does not prove freshness. If the copy is behind or its provenance is unknown, retrieve `SKILL.md` and the needed references from that exact commit, then reread the entry point before using it. Record the source commit and skill version in working state; keep the task on that coherent snapshot rather than mixing revisions or repeatedly polling. 2. **Persist the refresh when permitted.** A clean checkout on `main` with the verified canonical remote may update by fast-forward only. Refresh a copied installation's owned Markdown package together only when its prior baseline proves there are no local customizations; otherwise use an isolated upstream snapshot for this task. Update installer-managed packages through their supported installer, not by editing caches. Never overwrite dirty, divergent, customized, or actively maintained source trees, delete unrelated files, or bypass filesystem approval. 3. **Handle unavailable updates honestly.** If installation writes are unavailable, use the retrieved snapshot without replacing the installation. If upstream cannot be checked or retrieved, continue with the available local copy and briefly disclose that freshness is unverified, unless the user requires verified-latest guidance. Do not claim an offline or failed check made the skill current.

This updates skill knowledge only, not runtime policy or permissions. Preserve [skill governance](references/skills-and-connectors.md#skill-governance) and higher-priority instructions; do not execute downloaded code or recursively invoke self-update. When maintaining this repository, inspect the baseline and upstream revision without automatically replacing the working tree under edit.

Core stance

An agent harness is the control plane around a model. The model proposes actions; the harness validates, authorizes, executes, records, summarizes, and returns observations. Keep the loop simple and make the runtime rigorous.

Default architecture:

user/task
  -> instruction and context builder
  -> model call
  -> tool/action proposal
  -> schema validation
  -> permission decision
  -> execution or approval pause
  -> structured observation
  -> context update
  -> repeat within budget or finish

When to activate this skill

Use this skill for prompts involving any of these intents:

• build an agent, agentic workflow, AI worker, autonomous assistant, or harness;
• create a domain-specific MVP agent design, starter harness, implementation blueprint, or first production-safe version;
• choose between OpenAI, Anthropic, OpenAI-compatible APIs, direct tool loops, hosted tools, or SDKs;
• route model requests within a session, replay instruction/tool configuration changes, or evaluate paid cache warming;
• integrate or evaluate a backend whose generation state includes nontext latent positions and cannot be reconstructed from a text transcript alone;
• design tools, permissions, guardrails, approval flows, or sandboxing;
• design agent-rendered interfaces, record provenance, cumulative business limits, or user-memory lifecycles;
• design an agent for a partially known or changing environment using capability discovery, safe probing, runtime binding, schema verification, or drift invalidation;
• reduce code-mode or programmatic-tool latency through speculative execution, partial-program analysis, futures, exact claim semantics, or cancellation of unused work;
• create planning mode, workflow orchestration, goal mode, todo tracking, or long-running task behavior;
• build an always-on agent service or durable session with accepted-input receipts, task ownership, application state, restart recovery, reconnecting observers, or recurring source collection and delivery bookkeeping;
• design collaborating agent teams with distinct approaches, shared work awareness, finding exchange, or evidence-driven reassignment;
• add context compaction, memory, retrieval, scoped instructions, or prompt hierarchies;
• design a recursive language model (RLM), programmable-context runtime, self-refining or continual harness, task-local harness search, retained child agents, or executable skills;
• build or i