agents-best-practices

"The model proposes actions; the harness validates, authorizes, executes, records, and returns observations."

A provider-neutral Agent Skill for designing, generating MVP blueprints for, auditing, refactoring, and explaining agentic harnesses.

It applies beyond coding agents: research, support, operations, sales, finance, data analysis, procurement, legal workflows, healthcare workflows, education, and workflow automation agents all need the same core runtime discipline.

Install - pick one:

A. With skills (any compatible agent):

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

The -g flag installs globally at user level so every project can discover it.

B. Or paste this prompt to your AI agent:

Install the agents-best-practices skill for me:

1. Clone https://github.com/DenisSergeevitch/agents-best-practices into my
   user-level skills directory as `agents-best-practices/`.
   Use the skill directory my agent reads on this machine, for example:
   - Codex: ~/.codex/skills/
   - Claude Code: ~/.claude/skills/
2. Verify that SKILL.md, icon.jpeg, and the references/ directory are present.
3. Confirm the install path when done.

C. Manual install paths:

# Codex
mkdir -p "${CODEX_HOME:-$HOME/.codex}/skills"
git clone https://github.com/DenisSergeevitch/agents-best-practices.git \
  "${CODEX_HOME:-$HOME/.codex}/skills/agents-best-practices"

# Claude Code, user-level
mkdir -p "$HOME/.claude/skills"
git clone https://github.com/DenisSergeevitch/agents-best-practices.git \
  "$HOME/.claude/skills/agents-best-practices"

# Claude Code, project-level
mkdir -p .claude/skills
git clone https://github.com/DenisSergeevitch/agents-best-practices.git \
  .claude/skills/agents-best-practices

Keeping the skill current: On each new task, the skill checks this repository's main revision and uses a consistent upstream snapshot when the installed copy is stale. Its self-update instructions preserve local changes and installer-managed copies, respect write permissions, and disclose when freshness cannot be verified.

Use cases · What this is · Philosophy · Layout · Sources

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Use cases

Once installed, the skill activates when a conversation touches agent architecture, harness design, tool permissions, environment-adaptive tools, speculative tool execution, planning mode, workflow orchestration, adaptive agent teams, context and memory, skills, connectors, public-board communication, troubleshooting, observability, evals, prompt caching, or production readiness. Ten common ways to use it:

Case 1 - Generate an MVP agent blueprint

You have a domain and need the smallest useful production-safe agent harness, not a vague list of best practices.

You    > Build an agent for account renewal risk. It should read CRM,
         support tickets, and usage data, then draft renewal actions.

Agent  > Start with an approval-gated Level 2 harness.
         The MVP has one job: produce a renewal-risk brief plus draft
         next actions for a human account owner.

         Core loop:
           user/task -> context builder -> model call -> typed tool call
           -> schema validation -> permission check -> execution or pause
           -> structured observation -> next step or final brief

         Minimal tools:
           - read_account_profile      read_private_data
           - list_support_tickets      read_private_data
           - fetch_usage_summary       read_private_data
           - draft_customer_email      draft_external_message
           - request_approval          approval_gate

         Launch gate:
           20 historical accounts, trace review, no unapproved external
           sends, and human acceptance on at least 80% of draft actions.

The primary reference is references/mvp-agent-blueprint.md.

Case 2 - Audit an existing agent harness

You already have a working agent, but it is brittle, expensive, too broad, or hard to debug.

You    > Our research agent sometimes runs tools forever and forgets why
         it made a decision after context compaction. Audit the harness.

Agent  > The failure points are runtime-level, not prompt-level:
           - no hard step/tool/time/cost budget
           - compaction preserves prose but loses active approvals
           - tool results are unbounded and mix trusted/untrusted data
           - no event trace for model output -> tool call -> observation

         Fix order:
           1. Add loop budgets and termination reasons.
           2. Store plan, approvals, todos, and artifacts outside the prompt.
           3. Make compaction rehydrate active state, not chat history.
           4. Add evals for injection, missing tool result, timeout, and
              budget exhaustion.

Start with references/agentic-loop.md, references/context-memory-compaction.md, references/security-observability.md, and references/evals.md.

Case 3 - Design tools, permissions, and connectors

You need an agent to work with real systems without turning every API into an unsafe broad tool.

You    > The ops agent needs Slack, Linear, Google Drive, and our internal
         deploy API. How should tools and permissions work?

Agent  > Split by risk class. Reads can be autonomous when scoped.
         Drafts can be autonomous when labeled. External writes, deploys,
         destructive actions, privileged access, and financial operations
         require an approval record outside the model.

         Do not expose generic send_message, write_database, or run_command.
         Wrap each actio