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BC uv-package-manager

Comprehensive guide to using uv, an extremely fast Python package installer and resolver written in Rust, for modern Python project management and dependency workflows.

sickn33/agentic-awesome-skills Agent Skills author: sickn33 MIT 2 files body ≈ 358 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Comprehensive guide to using uv, an extremely fast Python package installer and resolver written in Rust, for modern Python project management and dependency…

As a process C 63/100 · Has gaps — weak spots: result and completion, failures and branches, progress reporting

ProcedureDockerSoftware developmenttype 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
87/100
safety, quality, tests
Safety 60%
94
Quality 40%
77
Run on models
none yet
Process rating
C
63/100
Has gaps
Failures and branches w 10
0
Progress reporting w 2
0
Result and completion w 14
40
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: uv-package-manager (sickn33/agentic-awesome-skills)

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Dangerous commands medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The skill contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.

For the author

Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.

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
    • medium Dangerous commands cmd-shell-rc resources/implementation-playbook.md:683
      Writes to a shell startup file
      echo 'export PATH="$HOME/.cargo/bin:$PATH"' >> ~/.bashrc
    • low Dangerous commands cmd-execpolicy-bypass resources/implementation-playbook.md:61
      Runs PowerShell with execution policy bypassed (quoted — discussed, not commanded)
      powershell -NoProfile -Command "Invoke-WebRequest https://astral.sh/uv/install.ps1 -OutFile $env:TEMP\\uv-i…ps1; Get-Content $env:TEMP\\uv-i…ps1 -TotalCount 120; powershell -ExecutionPolic
      quoted

    Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "risk"
    • note frontmatter-key unknown frontmatter key "source"
    • note frontmatter-key unknown frontmatter key "date_added"

    Process rating: all ten parameters 63/100

    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 40Result and completion. Does not say what the result is
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 20 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 358 tokens
    • 100Running it twice. No mutating operations

    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
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +3Description length 168: enough signal without eating the budget
    • +4Structure: 6 headings
    • +3Step-by-step instructions: 20 items

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.