Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/vikasudasi/skill-vault/python-cli-typernpx skills add vikasudasi/skill-vault --skill python-cli-typergit clone --depth 1 https://github.com/vikasudasi/skill-vaultWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00034 | $0.00509 |
| Opus 5 | $0.00017 | $0.00254 |
| Sonnet 5 | $0.00007 | $0.00102 |
| Haiku 4.5 | $0.00003 | $0.00051 |
Grade A, and why
python-cli-typer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Building Python CLIs with Typer
Use when scaffoldng or extending a Python command-line tool and you want a maintainable argument surface, clean help, and a testable entrypoint.
Scaffold
from __future__ import annotations
import typer
app = typer.Typer(add_completion=False)
@app.command()
def scan(path: str, recursive: bool = typer.Option(False, "--recursive", "-r")) -> None:
"""Scan a directory."""
...
if __name__ == "__main__":
app()
Conventions that matter
- Use
--flagfor booleans,--opt VALUEfor options; put required positional args first. Never mix a required option with a positional that can be omitted. - Read config from env with a small settings dataclass instead of scattering
os.getenvthrough commands (mirrors Skill Vault'sget_settings). - Exit codes:
0success,1runtime/API error,2invalid args (click does this by default),3"no valid results". Raise a typed exception and map it in a top-level handler rather thansys.exitinline.
Testing
Expose a main(argv: list[str]) -> None that calls app(args=argv, standalone_mode=False).
This lets you invoke commands in-process without spawning a process.
from cli import main
def test_scan_ok():
with pytest.raises(SystemExit) as e:
main(["scan", "src"])
# assert stdout via capsys
Pitfalls
- Typer's
--helpis auto-generated — keep docstrings accurate, they become help. - For streaming/progress use
rich(Typer integrates viarich.markup), not print. - Don't make the CLI print secrets or API keys on the happy path.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 68 lines · 34 tokens per session scan A 11ab62596fcb
python-cli-typer is a skill published in the GitHub repository vikasudasi/skill-vault (0 stars, last pushed 16d ago), licensed Apache-2.0. It adds 34 tokens to every session and 509 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
cli-tool-design
Design command-line interfaces with clear argument parsing, subcommands, help text, output formatting, and exit codes. Covers Click, Typer, argparse, and shell completion. Triggers on CLI tool development, argument parsing, or terminal UX design requests.
cli-sdk-guide
Guide for implementing Backend.AI client SDK and CLI (Session, BaseFunction, @apifunction, Click commands, Pydantic models, FieldSpec, output handlers, APIConfig, testing).
compose-python-stack
Design and generate a compatible project from the Python Template component layers or presets. Use when choosing a workload, framework, AI providers, data engines, interfaces, training and serving tools, deployment target, or IaC option; also use when a user wants a simple library, CLI, or API without AI.
maintain-python-template
Extend or repair the Python Template repository while keeping its catalog, Copier questions, templates, generated references, and tests synchronized. Use for adding a framework, provider, database, interface, ML tool, deployment target, preset, compatibility rule, or generated-project capability.
validate-python-stack
Validate the Python Template repository or a project generated from it. Use before committing, publishing, deploying, or reviewing a stack to check catalog compilation, rendering, dependencies, formatting, tests, security workflows, skills, and representative runtime behavior.
operate-ai-stack
Develop, test, and troubleshoot this generated AI or ML workload across its framework, model and embedding providers, retrieval stores, interfaces, training, serving, and observability layers. Use when changing prompts, tools, agents, RAG, MCP, inference, fine-tuning, evaluations, or provider integration.