scripts-and-tooling

Creating Scripts and Tooling

Scripts abstract away multi-step sequences, complex computation, and ceremony that the agent would otherwise do manually each time. They're deterministic, token-efficient, and reusable across sessions.

Why Scripts Beat Agent Reasoning

• Deterministic — same result every time. No forgotten flags or format drift.
• Token-efficient — only the script's output enters context. Complex logic costs zero tokens.
• Fast — milliseconds vs seconds of LLM reasoning.
• Composable — callable from hooks, commands, skills, CI/CD, and plain shell.
• Persistent — survives context resets and session boundaries.

When to Create a Script

• Agent repeats the same 3+ step sequence across sessions
• Logic is purely computational (parsing, transforming, validating)
• Correctness matters more than flexibility (deploy scripts, release workflows)
• The agent regularly constructs complex shell pipelines for the same task
• An existing tool's output needs reformatting for agent consumption

Where Scripts Live

Design Principles

Self-documenting — --help is the canonical source of truth, not external docs. Make it comprehensive enough that an agent never needs to look elsewhere. Add a comment block at the top of the script explaining purpose, assumptions, and usage.

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. If it looks like pytest, docker, or kubectl, the agent can infer behavior without reading docs. Reuse conventions: --dry-run, --format json, --verbose.

Idempotent — safe to run multiple times. Guard against duplicate operations.

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.

Common Categories

Git workflow — commit formatting, branch creation, PR automation, extract-commits, changelog generation.

Quality checks — lint wrappers, type-check runners, coverage reporters that produce structured output.

Environment setup — dependency installation, tool configuration, env var bootstrapping.

Code generation — scaffolding (components, tests, migrations) from templates.

Data transformation — log parsing, JSON/CSV processing, token counting, dependency graphing.

API wrappers — MCP servers or shell scripts that handle auth, pagination, rate limiting, and return clean data.

Deployment — release scripts with dry-run, version bumping, deployment pipelines with approval gates.

Session tooling — transcript search, usage tracking, cross-session memory persistence.