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BD python3-cli

Use when building CLI applications with Typer and Rich — creating commands with Annotated parameter syntax, defining arguments and options, composing subcommands, async concurrent CLI tasks with semaphores, testing with CliRunner, PEP 723 shebang scripts, progress bars, Rich terminal output, or non-TTY display width handling.

Jamie-BitFlight/claude_skills Agent Skills author: Jamie-BitFlight MIT 38 files · 7 scripts body ≈ 1 140 tokens Open the sourcegithub.com↗ analyzed 8 d ago

Use when building CLI applications with Typer and Rich — creating commands with Annotated parameter syntax, defining arguments and options, composing…

As a process D 47/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches

IntegrationSoftware developmentMarketingtype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
B
94/100
safety, quality, tests
Safety 60%
98
Quality 40%
88
Run on models
none yet
Process rating
D
47/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 2

    ✓ No critical or high findings

    Medium and low: 2
    • low Dangerous commands cmd-pipe-to-shell-known-host assets/nested-typer-exceptions/nested-typer-exception-explosion_corrected_rich_console.py:48
      Pipe-to-shell installer from a well-known host (still executes remote code) (string literal in code, not executed)
      curl -fsSL https://astral.sh/uv/install.sh | bash
      code literal
    • low Dangerous commands cmd-pipe-to-shell-known-host assets/nested-typer-exceptions/nested-typer-exception-explosion_corrected_typer_echo.py:47
      Pipe-to-shell installer from a well-known host (still executes remote code) (string literal in code, not executed)
      curl -fsSL https://astral.sh/uv/install.sh | bash
      code literal

    Files scanned: 37. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 47/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 1 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
    • 100Steps. 15 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1140 tokens
    • low 10 top-level sections: this looks like several domains in one skill

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 327: enough signal without eating the budget
    • +4Structure: 11 headings
    • +3Step-by-step instructions: 15 items
    • +4Has examples (5 code blocks)
    • +4Reference files are cited in the instructions (16 of 22)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.