scripts-and-tooling

name	scripts-and-tooling
description	Guide to creating CLI tools and scripts that augment Claude Code. Use when building bin/ executables, automation scripts, hook handlers, or tooling that abstracts repeated agent workflows.
user-invocable	false

Creating Scripts and Tooling

Scripts abstract away multi-step sequences, complex computation, and ceremony that the agent would otherwise do manually each time. Only the script's output enters context — complex logic costs zero tokens, and the script persists across sessions and context resets.

When to Create a Script

• Agent repeats the same 3+ step sequence across sessions
• The agent regularly constructs complex shell pipelines for the same task
• An existing tool's output needs reformatting for agent consumption

Where Scripts Live

Location	Mechanism	Use Case
bin/ on PATH	Agent calls via Bash	General-purpose CLI tools
scripts/	Agent calls via Bash	Project-specific automation
hooks/ scripts	Called by hooks.json	Lifecycle handlers (guards, formatters, loggers)
skills/*/scripts/	Bundled with SKILL.md	Deterministic computation a skill needs
.mcp.json + MCP servers	Claude calls as native tools	API wrappers, database access

Design Principles for Agent-Facing CLIs

Self-documenting for agents — --help must be comprehensive enough that an agent never needs to look elsewhere. It's the canonical source of truth, not external docs.

Every output is a prompt — the agent's next action depends on your script's output. Design both success and failure output to guide the agent forward, not just report status.

Familiar interfaces — model your CLI after tools the agent already knows (pytest, docker, kubectl). The agent can infer behavior from convention without reading docs.

No interactive input — agents can't respond to prompts. Use flags/args/env vars instead.

Output Design

Script output is the primary interface between your tool and the agent. A silent success or cryptic error is a dead end.

Success output — confirm what happened, echo IDs/paths the agent needs next, suggest next steps:

# bad
OK

# good
Created deployment deploy-a1b2c3d4 (env: staging, 3 services)

Next:
  Check status:  deploy status deploy-a1b2c3d4
  View logs:     deploy logs deploy-a1b2c3d4
  Roll back:     deploy rollback deploy-a1b2c3d4

Error output — three parts: what went wrong, how to fix it, what to do next:

# bad
Error: connection refused

# good
ERROR: Cannot connect to database at localhost:5432 (connection refused)

Fix: Ensure postgres is running:
  brew services start postgresql
  # or: docker start postgres-dev

Then retry: ./migrate --target production

Structured output — emit JSON when the agent will consume the result programmatically. Raw human-readable text is fine for notification scripts or when the agent just needs to relay information to the user.