Fable 5 Agent Builder

Fable 5 Agent Builder scaffolds production-grade autonomous agents on the claude-fable-5 model using the Claude Agent SDK. It wires up the parts that are tedious to get right by hand — session lifecycle, persistent memory, sub-agent delegation, MCP server connections, and token-budget guardrails — so an autonomous loop runs without silently draining your API quota. It's aimed at backend and ML engineers shipping agents that run unattended rather than chat one-off prompts.

When to use it

• You're moving past a single query() call into a multi-turn agent that needs to remember state across sessions.
• You want one orchestrator agent to spawn specialized sub-agents (research, codegen, review) and merge their results.
• You need to connect external tools — a database, a search API, your internal services — over MCP.
• An earlier agent loop ran away and burned tokens; you want hard ceilings before the next one ships.

What it does

It generates a typed agent project against the Claude Agent SDK. The core is an agent loop built on the SDK's query() streaming interface with an AgentDefinition carrying your system prompt, allowed tools, and model (claude-fable-5). It sets up:

• Session management — resumable sessions so a run can pause and continue with full history.
• Persistent memory — a memory store (file or KV-backed) the agent reads at startup and writes facts back to between turns.
• Sub-agents — child agent definitions the orchestrator delegates to, each with scoped tool permissions.
• MCP integration — mcpServers config wiring stdio/HTTP servers and exposing their tools to the loop.
• Structured outputs — JSON-schema-validated final results instead of free text.
• Cost guardrails — a per-run token budget and max-turn cap that halt the loop and surface usage from the SDK's result message.

Usage

curl -fsSL --create-dirs -o ~/.claude/skills/fable-5-agent-builder/SKILL.md https://claudeskil.com/skills/fable-5-agent-builder/SKILL.md

# Scaffold an agent
claude fable5 init research-agent --model claude-fable-5 --memory kv

const agent = defineAgent({
  model: "claude-fable-5",
  systemPrompt: "Research and summarize with citations.",
  mcpServers: { search: { command: "npx", args: ["my-search-mcp"] } },
  budget: { maxTokens: 200_000, maxTurns: 12 },
  memory: "./.fable/memory.json",
});

Tips & gotchas

• Scope each sub-agent's allowedTools tightly — an orchestrator inheriting every tool is the usual cause of runaway loops.
• The budget cap counts input + output tokens including cached reads; set it above your largest expected context, not your average.
• Memory writes happen between turns, not mid-turn — if a run crashes inside a turn, that turn's facts are lost. Checkpoint important state explicitly.
• MCP stdio servers must be on PATH at runtime; missing binaries fail silently as "no tools available," not an error.

FAQ

Does it work with non-Fable Claude models? Yes — change the model field. The SDK wiring is model-agnostic; Fable 5 is just the default and what the guardrail defaults are tuned for.

How is memory persisted? Either a local JSON file or a KV backend you point at. The agent loads it as context at session start and appends after each turn.

Can I cap spend? Yes — budget.maxTokens and maxTurns halt the loop and the SDK result reports actual usage so you can log it per run.