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Skills can instruct agents to run shell commands and bundle reusable scripts in a scripts/ directory. This guide covers one-off commands, self-contained scripts with their own dependencies, and how to design script interfaces for agentic use.

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One-off commands

When an existing package already does what you need, you can reference it directly in your SKILL.md instructions without a scripts/ directory. Many ecosystems provide tools that auto-resolve dependencies at runtime.
uvx runs Python packages in isolated environments with aggressive caching. It ships with uv.
uvx ruff@0.8.0 check .
uvx black@24.10.0 .
  • Not bundled with Python — requires a separate install.
  • Fast. Caches aggressively so repeat runs are near-instant.
Tips for one-off commands in skills:
  • Pin versions (e.g., npx eslint@9.0.0) so the command behaves the same over time.
  • State prerequisites in your SKILL.md (e.g., “Requires Node.js 18+”) rather than assuming the agent’s environment has them. For runtime-level requirements, use the compatibility frontmatter field.
  • Move complex commands into scripts. A one-off command works well when you’re invoking a tool with a few flags. When a command grows complex enough that it’s hard to get right on the first try, a tested script in scripts/ is more reliable.

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Referencing scripts from SKILL.md

Use relative paths from the skill directory root to reference bundled files. The agent resolves these paths automatically — no absolute paths needed. List available scripts in your SKILL.md so the agent knows they exist:
SKILL.md
## Available scripts

- **`scripts/validate.sh`** — Validates configuration files
- **`scripts/process.py`** — Processes input data
Then instruct the agent to run them:
SKILL.md
## Workflow

1. Run the validation script:
   ```bash
   bash scripts/validate.sh "$INPUT_FILE"
   ```

2. Process the results:
   ```bash
   python3 scripts/process.py --input results.json
   ```
The same relative-path convention works in support files like references/*.md — script execution paths (in code blocks) are relative to the skill directory root, because the agent runs commands from there.

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Self-contained scripts

When you need reusable logic, bundle a script in scripts/ that declares its own dependencies inline. The agent can run the script with a single command — no separate manifest file or install step required. Several languages support inline dependency declarations:
PEP 723 defines a standard format for inline script metadata. Declare dependencies in a TOML block inside # /// markers:
scripts/extract.py
# /// script
# dependencies = [
#   "beautifulsoup4",
# ]
# ///

from bs4 import BeautifulSoup

html = '<html><body><h1>Welcome</h1><p class="info">This is a test.</p></body></html>'
print(BeautifulSoup(html, "html.parser").select_one("p.info").get_text())
Run with uv (recommended):
uv run scripts/extract.py
uv run creates an isolated environment, installs the declared dependencies, and runs the script. pipx (pipx run scripts/extract.py) also supports PEP 723.
  • Pin versions with PEP 508 specifiers: "beautifulsoup4>=4.12,<5".
  • Use requires-python to constrain the Python version.
  • Use uv lock --script to create a lockfile for full reproducibility.

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Designing scripts for agentic use

When an agent runs your script, it reads stdout and stderr to decide what to do next. A few design choices make scripts dramatically easier for agents to use.

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Avoid interactive prompts

This is a hard requirement of the agent execution environment. Agents operate in non-interactive shells — they cannot respond to TTY prompts, password dialogs, or confirmation menus. A script that blocks on interactive input will hang indefinitely. Accept all input via command-line flags, environment variables, or stdin: 
# Bad: hangs waiting for input
$ python scripts/deploy.py
Target environment: _

# Good: clear error with guidance
$ python scripts/deploy.py
Error: --env is required. Options: development, staging, production.
Usage: python scripts/deploy.py --env staging --tag v1.2.3

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Document usage with --help

--help output is the primary way an agent learns your script’s interface. Include a brief description, available flags, and usage examples: 
Usage: scripts/process.py [OPTIONS] INPUT_FILE

Process input data and produce a summary report.

Options:
  --format FORMAT    Output format: json, csv, table (default: json)
  --output FILE      Write output to FILE instead of stdout
  --verbose          Print progress to stderr

Examples:
  scripts/process.py data.csv
  scripts/process.py --format csv --output report.csv data.csv
Keep it concise — the output enters the agent’s context window alongside everything else it’s working with.

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Write helpful error messages

When an agent gets an error, the message directly shapes its next attempt. An opaque “Error: invalid input” wastes a turn. Instead, say what went wrong, what was expected, and what to try: 
Error: --format must be one of: json, csv, table.
       Received: "xml"

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Use structured output

Prefer structured formats — JSON, CSV, TSV — over free-form text. Structured formats can be consumed by both the agent and standard tools (jq, cut, awk), making your script composable in pipelines. 
# Whitespace-aligned — hard to parse programmatically
NAME          STATUS    CREATED
my-service    running   2025-01-15

# Delimited — unambiguous field boundaries
{"name": "my-service", "status": "running", "created": "2025-01-15"}
Separate data from diagnostics: send structured data to stdout and progress messages, warnings, and other diagnostics to stderr. This lets the agent capture clean, parseable output while still having access to diagnostic information when needed.

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Further considerations

  • Idempotency. Agents may retry commands. “Create if not exists” is safer than “create and fail on duplicate.”
  • Input constraints. Reject ambiguous input with a clear error rather than guessing. Use enums and closed sets where possible.
  • Dry-run support. For destructive or stateful operations, a --dry-run flag lets the agent preview what will happen.
  • Meaningful exit codes. Use distinct exit codes for different failure types (not found, invalid arguments, auth failure) and document them in your --help output so the agent knows what each code means.
  • Safe defaults. Consider whether destructive operations should require explicit confirmation flags (--confirm, --force) or other safeguards appropriate to the risk level.
  • Predictable output size. Many agent harnesses automatically truncate tool output beyond a threshold (e.g., 10-30K characters), potentially losing critical information. If your script might produce large output, default to a summary or a reasonable limit, and support flags like --offset so the agent can request more information when needed. Alternatively, if output is large and not amenable to pagination, require agents to pass an --output flag that specifies either an output file or - to explicitly opt in to stdout.