Is Smoke Monkey an AI model?

No. Smoke Monkey is not an LLM. It is an embeddable, framework-agnostic runtime that turns LLMs (NVIDIA, Gemini, OpenAI, Claude, Ollama) into autonomous engineering agents capable of multi-step planning, calling tools, editing files, and running test loops with zero runtime dependencies.

Do I need a database (Postgres, SQLite, or Redis)?

No database is required. State is persisted through lean session files on disk, or you can plug in your own custom storage adapter. You can deploy it as a CLI, a desktop app, or inside a serverless function without provisioning a database.

How does human-in-the-loop permission gating work?

Smoke Monkey comes with three permission policies: allow-all, deny-all, and ask-default (or a custom function resolver). When a sensitive tool like edit_file or run_command is called under "ask", the runtime emits a permission.required event, pausing execution until your code or UI calls agent.resolvePermission(id, "allow" | "deny").

How does the Model Context Protocol (MCP) server work?

Smoke Monkey ships an official stdio MCP server in smoke-monkey-harness-mcp. Point Claude Code, Cursor, Codex, or opencode at it using npx -y smoke-monkey-harness-mcp to let other AI tools plan, scaffold, verify, and run agents using Smoke Monkey automatically.

What are SKILL.md skills and how do they prevent context bloat?

Skills are directories containing a SKILL.md workflow file. The system prompt only includes a one-line catalog of available skills. When a task requires specialized expertise (e.g. frontend-ui-engineering, test-driven-development, or security-hardening), the agent calls use_skill to load instructions just-in-time, saving thousands of tokens.

What is an AI Agent Harness?

An AI agent harness is the execution environment that bridges raw Large Language Models (LLMs) to real-world software engineering capabilities. While an LLM generates tokens, an AI harness manages deterministic phase loops (explore, plan, edit, verify, recover), tool execution sandboxing, token budget compaction, and safety gates needed for autonomous problem-solving.

What makes Smoke Monkey an Agentic AI runtime rather than a simple wrapper?

Unlike basic prompt-response chains, Smoke Monkey implements true agentic AI architecture: a deterministic 6-phase state machine with automatic failure recovery, runaway loop guards, 24 built-in developer tools, dynamic subcontext isolation, and interactive human-in-the-loop permission pauses.

Is Smoke Monkey Harness 100% Open Source AI?

Yes. Smoke Monkey Harness is 100% open source under the permissive MIT license with zero runtime dependencies. There is no telemetry lock-in, no mandatory cloud backend, and no database requirement—giving engineering teams complete sovereignty over their agent code.

What Agent APIs does Smoke Monkey Harness provide?

Smoke Monkey provides composable TypeScript Agent APIs: createAgent() for initialization, agent.run() for multi-turn execution, agent.on() for fine-grained lifecycle and streaming events, agent.resolvePermission() for interactive human authorization, subcontext switching APIs, and stdio Model Context Protocol (MCP) server endpoints.

Can I run completely offline without API keys?

Yes! Smoke Monkey natively supports Ollama out of the box. Set provider: "ollama" and model: "qwen3:8b" with workspacePath, and the agent will run locally on your machine with zero external network requests.

Just-in-Time Knowledge

Just-in-Time Skills (SKILL.md)

Stuffing dozens of instruction documents into the system prompt wastes thousands of tokens and degrades LLM reasoning. Smoke Monkey introduces a Two-Tier Just-in-Time (JIT) Skill Pattern that cuts context overhead by up to 94%.

Tier 1

~150 tokens total

1-Line Catalog Index

The agent sees only a compact index of available skill names and 1-sentence descriptions in the initial prompt.

Tier 2

On-Demand Injection

Full Body on use_skill

When the task requires it, the agent autonomously calls use_skill. Only then is the complete SKILL.md body loaded for that turn.

Writing a Standard SKILL.md Playbook

.agents/skills/database-migrations/SKILL.mdmarkdown

1---
2name: database-migrations
3description: Production checklist for safe PostgreSQL schema migrations without downtime.
4category: backend
5---
6
7# PostgreSQL Safe Migration Playbook
8
91. Never rename columns directly in one release.
102. Set lock timeouts: SET lock_timeout = '2s';
113. Always create indexes concurrently (CREATE INDEX CONCURRENTLY).

25 Bundled Production Skills

Backend

8 Skills

api-design, database-migrations, redis-caching, auth-standards

Frontend

6 Skills

react-performance, tailwind-styling, state-management, a11y

DevOps

5 Skills

docker-deployment, ci-cd-pipelines, kubernetes-specs, terraform

QA & Reliability

6 Skills

test-driven-development, e2e-playwright, security-audit

Check Out Permissions & Safety

See how interactive pauses protect your codebase during autonomous runs.