REPL Handoff

The REPL Handoff pattern bridges interactive planning in a coding assistant (like Claude Code) with autonomous execution in Fabro. You collaborate with an AI in your terminal to produce a plan, then hand it off to a workflow that implements the plan, runs it through multi-model simplification, and verifies the result — all on cloud resources. This pattern is useful when a task is well-understood enough to plan interactively but large enough that you don’t want to babysit the implementation. The human stays in the loop for the creative part (planning) and delegates the mechanical part (coding, testing, reviewing) to an automated pipeline.

When to use this

You’ve scoped a feature or fix and can describe the steps in a plan
The implementation involves writing code, running tests, and iterating on lint/build failures
You want multi-model review (not just the model that wrote the code) before calling it done
You’d rather not wait at your terminal while the agent codes — let it run in the background on cloud resources

The workflow

implement.fabro

digraph ImplementAndSimplify {
    graph [
        goal="Implement and simplify",
        model_stylesheet="
            * { backend: api; model: claude-opus-4-6;}
        "
    ]
    rankdir=LR

    start [shape=Mdiamond, label="Start"]
    exit  [shape=Msquare, label="Exit"]

    toolchain         [label="Toolchain", shape=parallelogram, script="command -v cargo >/dev/null || { curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y && sudo ln -sf $HOME/.cargo/bin/* /usr/local/bin/; }; cargo --version 2>&1", max_retries=0]
    preflight_compile [label="Preflight Compile", shape=parallelogram, script="cargo check -q --workspace 2>&1", max_retries=0]
    preflight_lint    [label="Preflight Lint", shape=parallelogram, script="cargo clippy -q --workspace -- -D warnings 2>&1", max_retries=0]
    fix_lints         [label="Fix Lints", prompt="The preflight lint step failed. Read the build output from context and fix all clippy lint warnings.", max_visits=3]
    implement         [label="Implement", prompt="Read the plan file referenced in the goal and implement every step. Make all the code changes described in the plan. Use red/green TDD."]
    simplify_opus     [label="Simplify (Opus)", prompt="@prompts/simplify.md"]
    simplify_gemini   [label="Simplify (Gemini)", prompt="@prompts/simplify.md", model="gemini-3.1-pro-preview-customtools"]
    simplify_gpt      [label="Simplify (GPT-54)", prompt="@prompts/simplify.md", model="gpt-54"]
    verify            [label="Verify", shape=parallelogram, script="cargo clippy -q --workspace -- -D warnings 2>&1 && cargo nextest run --cargo-quiet --workspace --status-level fail 2>&1", goal_gate=true, retry_target="fixup"]
    fixup             [label="Fixup", prompt="The verify step failed. Read the build output from context and fix all clippy lint warnings and test failures.", max_visits=3]
    fmt               [label="Format", shape=parallelogram, script="cargo fmt --all 2>&1", goal_gate=true, max_retries=0]

    start -> toolchain
    toolchain -> preflight_compile [condition="outcome=success"]
    toolchain -> exit
    preflight_compile -> preflight_lint [condition="outcome=success"]
    preflight_compile -> exit
    preflight_lint -> implement [condition="outcome=success"]
    preflight_lint -> fix_lints
    fix_lints -> preflight_lint
    implement -> simplify_opus -> simplify_gemini -> simplify_gpt -> verify
    verify -> fmt   [condition="outcome=success"]
    verify -> fixup
    fixup -> verify
    fmt -> exit
}

How the handoff works

The key insight is that planning happens interactively and implementation happens autonomously. The workflow’s implement node reads a plan file that you created in your REPL session — it doesn’t generate its own plan.

Step 1: Plan in Claude Code

Use Claude Code’s plan mode to collaborate on an implementation plan. Go back and forth until you’re happy with the steps, file changes, and test strategy:

> /plan Add retry logic to the webhook delivery system

Planning...
1. Add RetryPolicy struct to lib/crates/fabro-webhooks/src/policy.rs
2. Implement exponential backoff with jitter
3. Add max_retries field to WebhookConfig
4. Write tests for retry timing, max attempts, and jitter bounds
5. Update webhook delivery loop to use RetryPolicy

Shall I proceed with this plan?

Step 2: Hand off to Fabro

Once the plan is ready, run the /fabro-implement slash command. This reads your Claude Code plan file and launches a Fabro workflow run:

> /fabro-implement

The command runs:

fabro run --detach --goal-file <plan-file-path> implement

The --detach flag returns immediately so you get your terminal back. Fabro executes the workflow on cloud resources in the background.

The Claude Code command

To set up the /fabro-implement slash command, create this file:

~/.claude/commands/fabro-implement.md

Get the path to the current Claude Code plan file.

Run this command from the root of this repository:

\`\`\`sh
fabro run --detach --goal-file CLAUDE_CODE_PLAN_FILE_PATH implement
\`\`\`

Respond with the plan file path and the output from the `fabro run` command, for example:

\`\`\`
Plan file: ~/.claude/plans/FILENAME.md
Fabro Run: OUTPUT_FROM_FABRO_RUN
\`\`\`

Claude Code resolves CLAUDE_CODE_PLAN_FILE_PATH to the actual plan file, passes it as the --goal-file to Fabro, and the workflow’s implement node reads that file to know what to build.

How it works

Preflight checks

Before touching any code, the workflow validates the environment:

Toolchain — ensures Rust is installed (installs via rustup if not). Exits immediately if this fails.
Preflight Compile — cargo check --workspace confirms the codebase compiles. Exits if it doesn’t — no point implementing against a broken baseline.
Preflight Lint — cargo clippy --workspace -D warnings checks for lint warnings. If lints fail, the fix_lints agent fixes them (up to 3 attempts) before proceeding.

This guarantees the implement node starts from a clean, compiling, lint-free codebase.

Implementation

The implement node is the core agent stage. Its prompt is simple:

Read the plan file referenced in the goal and implement every step.
Make all the code changes described in the plan. Use red/green TDD.

The agent reads your plan file (passed via --goal-file), then writes code, creates tests, and iterates until all plan steps are complete. The “red/green TDD” instruction means: write a failing test first, then make it pass.

Multi-model simplification

After implementation, the code passes through three independent simplification stages:

implement → Simplify (Opus) → Simplify (Gemini) → Simplify (GPT-54) → verify

Each model runs the same @prompts/simplify.md prompt but brings different strengths. Running the same review prompt across Claude, Gemini, and GPT catches different classes of issues — verbose code one model wrote that another would simplify, edge cases one model spots that others miss, naming improvements that vary by model preference. The models override the graph-level default via per-node model attributes:

simplify_opus     [prompt="@prompts/simplify.md"]
simplify_gemini   [prompt="@prompts/simplify.md", model="gemini-3.1-pro-preview-customtools"]
simplify_gpt      [prompt="@prompts/simplify.md", model="gpt-54"]

Verification and fixup

The verify node runs clippy and the full test suite:

cargo clippy -q --workspace -- -D warnings 2>&1 && \
  cargo nextest run --cargo-quiet --workspace --status-level fail 2>&1

This node has two important attributes:

goal_gate=true — the workflow cannot succeed unless verification passes
retry_target="fixup" — if verification fails at the exit node, route back to the fixup agent instead of failing immediately

The fixup agent reads the build output and fixes lint warnings and test failures. It loops back to verify, with max_visits=3 as a circuit breaker.

Format

A final cargo fmt --all ensures consistent formatting. This is also a goal gate — unformatted code means the workflow fails.

Adapting for other languages

This workflow is Rust-specific, but the pattern generalizes. Replace the shell commands for your language:

Stage	Rust	TypeScript	Python
Toolchain	`rustup`	`bun --version`	`python3 --version`
Compile	`cargo check`	`bun run typecheck`	`mypy .`
Lint	`cargo clippy`	`bun run lint`	`ruff check .`
Test	`cargo nextest run`	`bun test`	`pytest`
Format	`cargo fmt`	`bun run format`	`ruff format .`

The agent stages (implement, simplify, fixup) work with any language — they use the Fabro agent’s standard tools (Read, Write, Edit, Bash) and follow the plan file regardless of language.

Multi-Model Workflows

Assigning different models to different workflow nodes.

Failures & Retries

Goal gates, retry targets, max visits, and circuit breakers.

Run Configuration

TOML configs, goal files, and detached runs.

Environments

Running workflows on cloud resources with sandboxed execution.

Workflow Tutorials

Example Workflows

Deployment

REPL Handoff

When to use this

The workflow

How the handoff works

Step 1: Plan in Claude Code

Step 2: Hand off to Fabro

The Claude Code command

How it works

Preflight checks

Implementation

Multi-model simplification

Verification and fixup

Format

Adapting for other languages

Further reading

Multi-Model Workflows

Failures & Retries

Run Configuration

Environments

Workflow Tutorials

Example Workflows

Deployment

​When to use this

​The workflow

​How the handoff works

​Step 1: Plan in Claude Code

​Step 2: Hand off to Fabro

​The Claude Code command

​How it works

​Preflight checks

​Implementation

​Multi-model simplification

​Verification and fixup

​Format

​Adapting for other languages

​Further reading

Multi-Model Workflows

Failures & Retries

Run Configuration

Environments

When to use this

The workflow

How the handoff works

Step 1: Plan in Claude Code

Step 2: Hand off to Fabro

The Claude Code command

How it works

Preflight checks

Implementation

Multi-model simplification

Verification and fixup

Format

Adapting for other languages

Further reading