name: rcc description: Expert RCC (Repeatable, Contained Code) automation - create, manage, and distribute Python-based self-contained automation packages with isolated environments, work items, and enterprise deployment patterns allowed-tools: Bash, Read, Write, Edit, Grep, Glob, Task model: inherit hooks: SessionStart: - hooks: - type: command command: "bash "$CLAUDE_PROJECT_DIR/.claude/skills/rcc/hooks/setup-skill-env.sh"" timeout: 120 PreToolUse: - matcher: "Bash" hooks: - type: command command: "rcc task script -r "$CLAUDE_PROJECT_DIR/.claude/skills/rcc/robot.yaml" --silent -- python hooks/validate-rcc-commands.py" timeout: 30 PostToolUse: - matcher: "Bash" hooks: - type: command command: "rcc task script -r "$CLAUDE_PROJECT_DIR/.claude/skills/rcc/robot.yaml" --silent -- bash hooks/post-rcc-run.sh" timeout: 30

RCC Skill - Repeatable, Contained Code

Skill Environment Setup

This skill includes a pre-built holotree environment (hololib.zip) for instant setup:

# Instant restore (~5 seconds) - RECOMMENDED
rcc ht import .claude/skills/rcc/hololib.zip --silent

# Or build from scratch (~5-10 minutes)
rcc ht vars -r .claude/skills/rcc/robot.yaml --silent

The skill's hooks run in this isolated environment via rcc task script.

Repository: https://github.com/joshyorko/rcc (Community Fork) Maintainer: JoshYorko Homebrew Tap: https://github.com/joshyorko/homebrew-tools

RCC is a Go CLI tool for creating, managing, and distributing Python-based self-contained automation packages. It provides isolated Python environments without requiring Python installation on target machines.

Installation (Homebrew Recommended)

# macOS & Linux (recommended)
brew install --cask joshyorko/tools/rcc

# Also install Action Server for MCP/AI actions
brew install --cask joshyorko/tools/action-server

# Verify
rcc version

See installation.md for alternative methods (direct download, Docker, Windows).

Quick Reference

Pro Tip: Use --silent Flag

Add --silent to suppress RCC progress output and only see task logs:

# Without --silent: Shows all RCC loading progress
rcc run --task Main
# ####  Progress: 01/15  v18.16.0 ...
# ####  Progress: 02/15  v18.16.0 ...
# (lots of output)

# With --silent: Clean output, only task logs
rcc run --task Main --silent

# Works with all commands
rcc ht vars --silent              # Just env vars, no progress
rcc task script --silent -- cmd   # Just command output
rcc configure diagnostics --silent # Just results

Creating Robots

IMPORTANT: After creating a robot, ALWAYS run rcc ht vars to pre-build the environment.

# 1. List available templates
rcc robot init --json

# 2. Create robot with template
rcc robot init -t 01-python -d my-robot

# 3. Pre-build environment (REQUIRED)
rcc ht vars -r my-robot/robot.yaml

# 4. Run the robot
rcc run -r my-robot/robot.yaml

Available Templates:

• 01-python - Python Minimal
• 02-python-browser - Browser automation with Playwright
• 03-python-workitems - Producer-Consumer pattern
• 04-python-assistant-ai - Assistant AI Chat

Best Practice: Always Use UV

CRITICAL: Always prefer uv over pip in conda.yaml for 10-100x faster builds:

channels:
  - conda-forge

dependencies:
  - python=3.12.11
  - uv=0.9.28  # Fast package installer
  - pip:
      - requests==2.32.5

robot.yaml Configuration

tasks:
  Main:
    shell: python main.py

  Producer:
    shell: python -m robocorp.tasks run tasks.py -t producer

  Consumer:
    shell: python -m robocorp.tasks run tasks.py -t consumer

devTasks:
  Test:
    shell: pytest tests/ -v

  Setup:
    shell: python scripts/setup.py

# Environment files (priority order - first match wins)
environmentConfigs:
  - environment_linux_amd64_freeze.yaml
  - environment_windows_amd64_freeze.yaml
  - conda.yaml  # Fallback

artifactsDir: output

PATH:
  - .
  - bin

PYTHONPATH:
  - .
  - libraries

ignoreFiles:
  - .gitignore

# Pre-run scripts for secrets/private packages
preRunScripts:
  - setup_linux.sh
  - setup_windows.bat

Environment Management

# Build/rebuild environment
rcc ht vars -r robot.yaml

# Get environment info as JSON
rcc ht vars -r robot.yaml --json

# List all holotree environments
rcc holotree list

# Delete specific space
rcc holotree delete --space my-space

# Interactive shell in environment
rcc task shell

# Run command in environment
rcc task script --silent -- python --version
rcc task script --silent -- pip list

Environment Variables (Automatic)

RCC automatically injects these environment variables when running tasks:

IMPORTANT: All relative paths in robot.yaml (PATH, PYTHONPATH, artifactsDir) are resolved relative to ROBOT_ROOT.

import os
from pathlib import Path

from robocorp.tasks import get_current_task, get_output_dir

def resolve_output_dir() -> Path:
    output_dir = get_output_dir()
    if output_dir is not None:
        return output_dir.resolve()
    # Fallback for code paths running outside robocorp.tasks runtime.
    return Path(os.environ.get("ROBOT_ARTIFACTS", "output")).resolve()

def current_task_name() -> str:
    current = get_current_task()
    return current.name if current is not None else "<outside-task>"

robot_root = Path(os.environ.get("ROBOT_ROOT", ".")).resolve()

Dependency Freezing for Production

CRITICAL: Freeze files are automatically generated to ROBOT_ARTIFACTS (output/) every time you run a robot. They are NOT meant to be committed to the repo.

# 1. Run robot - freeze file is generated automatically to output/
rcc run --silent

# 2. View the generated freeze file
ls output/environment_*_freeze.yaml
# Output: environment_linux_amd64_freeze.yaml (platform-specific)

# 3. OPTIONAL: Copy to project root ONLY if you want reproducible builds
cp output/environment_*_freeze.yaml .

# 4. Update robot.yaml to prefer freeze files (falls back to conda.yaml)

Freeze File Behavior:

• Generated automatically on every rcc run (since v10.3.0)
• Contains exact pinned versions of all dependencies
• Platform-specific (linux_amd64, windows_amd64, darwin_amd64)
• Used for reproducibility when placed in environmentConfigs
• DO NOT flag "missing freeze files" as an error - they're runtime artifacts

Holotree: Content-Addressed Environment Storage

RCC uses Holotree - a deduplication system that stores Python environments efficiently:

Key Concepts:

• Library (Hololib): Content-addressed file store organized by hash. Files are stored once, referenced by content hash (SipHash-128)
• Catalog: JSON manifests describing complete environments with byte offsets for path relocation
• Spaces: Live working environments populated from catalogs

Benefits:

• 50MB binary appearing in 20 environments occupies disk space exactly once
• Environment restoration from cache: ~2-10 seconds (vs 5-15 minutes fresh)
• Surgical path relocation - enables environments to work from any location

# List all holotree environments
rcc holotree list

# Delete specific space
rcc holotree delete --space my-space

# Enable shared holotree (multiple users, same machine)
sudo rcc holotree shared --enable  # Linux/macOS
rcc holotree shared --enable       # Windows (admin)

Robocorp Python Libraries

robocorp.tasks - Task Decorator + Runtime Helpers

from pathlib import Path
import os

from robocorp.tasks import get_current_task, get_output_dir, task

def resolve_output_dir() -> Path:
    output = get_output_dir()
    if output is not None:
        return output.resolve()
    # Fallback for execution outside robocorp.tasks runtime.
    return Path(os.environ.get("ROBOT_ARTIFACTS", "output")).resolve()

def current_task_name() -> str:
    current = get_current_task()
    return current.name if current is not None else "<outside-task>"

@task
def my_task():
    """Task entry point - discovered and run by RCC."""
    output = resolve_output_dir()
    output.mkdir(parents=True, exist_ok=True)
    print(f"Running {current_task_name()} with artifacts in {output}")
    process_data()

robocorp.log - Structured Logging

from robocorp import log

# Basic logging
log.info("Processing started")
log.warn("Rate limit approaching")
log.critical("Connection failed")
log.debug("Detailed info for debugging")

# Configure logging
log.setup_log(
    max_value_repr_size="200k",
    log_level="info",           # Minimum for log.html
    output_log_level="info",    # Minimum for console
)

# Protect sensitive data (auto-redacted)
log.add_sensitive_variable_name("password")
log.add_sensitive_variable_name("secret")
log.add_sensitive_variable_name("api_key")

robocorp.workitems - Producer-Consumer Pattern

from robocorp import workitems
from robocorp.tasks import task

@task
def producer():
    """Create work items for processing."""
    for data in get_data_to_process():
        workitems.outputs.create(payload=data)

@task
def consumer():
    """Process work items with proper error handling."""
    for item in workitems.inputs:
        try:
            process(item.payload)
            item.done()  # Mark successful
        except BusinessError as e:
            # Business error - don't retry
            item.fail(exception_type="BUSINESS", message=str(e))
        except Exception as e:
            # Application error - may retry
            item.fail(exception_type="APPLICATION", message=str(e))

Work Item Methods:

• item.payload - JSON payload data
• item.files - List of attached file paths
• item.done() - Mark as successfully processed
• item.fail(exception_type, message) - Mark as failed
• workitems.outputs.create(payload, files) - Create output item

Self-Contained Bundles

Create single-file executables for distribution:

# Create bundle
rcc robot bundle --robot robot.yaml --output my-robot.py

# Run bundle on another machine
rcc robot run-from-bundle my-robot.py --task Main

Debugging

# System diagnostics
rcc configure diagnostics --robot robot.yaml

# Debug output
rcc run --debug

# Trace output (very verbose)
rcc run --trace

# Timeline
rcc run --timeline

Custom Endpoints (Air-Gapped Environments)

export RCC_ENDPOINT_PYPI="https://pypi.internal.com/simple/"
export RCC_ENDPOINT_CONDA="https://conda.internal.com/"
rcc run

Files in This Skill

• installation.md - Installation guide (Homebrew, direct download, Docker)
• reference.md - Complete command reference
• examples.md - Practical recipes and patterns
• actions.md - Sema4ai Actions framework & MCP tools
• deployment.md - Docker, RCC Remote, CI/CD patterns
• workitems.md - Work items & custom adapters
• hooks.md - Claude Code hooks integration
• templates/ - Reference conda.yaml configs
• hooks/ - Ready-to-use hook scripts
• scripts/ - Validation and health check utilities

Examples

Create a data processing robot

User: Create an RCC robot for data processing
Assistant: [runs: rcc robot init -t 01-python -d data-processor]
          [runs: rcc ht vars -r data-processor/robot.yaml]
          [environment ready]

Debug environment issues

User: My RCC environment build is failing
Assistant: [runs: rcc configure diagnostics --robot robot.yaml]
          [runs: rcc task script --silent -- pip list]
          [checks conda.yaml for issues]

Freeze dependencies for production

User: Prepare my robot for production
Assistant: [runs: rcc run to generate freeze file]
          [copies environment_*_freeze.yaml to project]
          [updates robot.yaml environmentConfigs]