scripts-and-tooling

Included with Lifetime

$97 forever

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.

AI Agents

What this skill does

# 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.

Files: 1

Size: 3.0 KB

Complexity: 12/100

Category: AI Agents

Source: https://github.com/captaincrouton89/crouton-kit/tree/main/plugins/authoring/skills/scripts-and-tooling

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