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Agents best practices

Skill markoblogo/abvx-agent-skills/skills/agents-best-practices

Reviewable capability layer for coding agents. Portable skills, delivery gates, workflow patterns, and verification-first engineering.

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npx -y skills add markoblogo/abvx-agent-skills --skill agents-best-practices

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Design, audit, or improve agentic systems and agent harnesses. Use for agent MVP blueprints, tool design, permission models, planning and goal loops, context compaction, memory, skills, connectors, observability, evals, safety, and provider-neutral agent architecture.

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SKILL.md

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Agents Best Practices

An agent harness is the control plane around a model. The model proposes; the harness validates, authorizes, executes, records, and returns observations.

Start With The Boundary

Identify:

  • domain and user;
  • autonomy level: answer-only, draft-only, approval-gated action, or autonomous within policy;
  • risk level: read-only, internal write, external communication, financial, legal, healthcare, security, destructive, or privileged;
  • state duration: single turn, session, resumable workflow, or long-running goal;
  • tool surface;
  • validation signal.

Minimal Harness Shape

task
  -> context builder
  -> model call
  -> proposed tool/action
  -> schema validation
  -> permission decision
  -> execution or approval pause
  -> structured observation
  -> state update
  -> finish or continue within budget

Harness Checklist

Before recommending implementation, cover these surfaces:

  • agentic loop: turn budget, stop rules, loop detection, checkpoint/resume;
  • tool registry: static vs dynamic tools, schema quality, denial/error shape, MCP boundaries;
  • context assembly: priority order, just-in-time loading, compaction, source attribution;
  • memory: session state vs durable memory, write policy, contradiction handling;
  • guardrails: trust boundaries, prompt-injection exposure, sandbox/network/filesystem policy;
  • permissions: read/write/send/delete/pay/deploy classes, approval gates, classifier-assisted routing only with deterministic backstops;
  • observability: event log, tool outcomes, user-visible state, no hidden-reasoning leakage;
  • evals: success fixtures, safety fixtures, eval noise budget, floor/ceiling checks;
  • managed-agent architecture: separate brain, hands, credentials, session state, and worker lifecycle when the agent becomes long-running.

Design Rules

  • Application code enforces safety; prompts only describe desired behavior.
  • Every tool call returns a structured result, including denials and errors.
  • Risky side effects require explicit policy and usually human approval.
  • Draft and commit/send/pay/delete are separate actions.
  • Tool schemas should be narrow, typed, validated, and auditable.
  • Context should be tight, source-aware, and loaded just in time.
  • Skills and connectors use progressive disclosure; do not expose every capability up front.
  • Long-running goals need budgets, checkpoints, resumable state, and a measurable done condition.
  • Observability records events and outcomes without exposing hidden reasoning.
  • Sandboxes and permission gates belong in the harness, not in the agent's self-restraint.
  • Treat classifier-based permission helpers as advisory unless a deterministic policy layer can still deny unsafe actions.

MVP Blueprint Output

When asked to design an agent, produce:

  • objective and MVP boundary;
  • autonomy and risk model;
  • core loop;
  • instruction architecture;
  • tool registry and permissions;
  • context, memory, retrieval, and compaction;
  • skills/connectors;
  • safety and approvals;
  • sandbox, scheduling, and managed-agent boundaries when relevant;
  • observability and evals;
  • minimal implementation path.

Avoid multi-agent systems until a single-agent harness has measurable failure cases that require decomposition.

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