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BD AI Agent Tools - Python Utility Library for AI Agents

This library provides ready-to-use Python functions that AI agents can leverage to perform various tasks including file operations, text analysis, data transformation, memory management, and valida...

modbender/skill-library-mcp Agent Skills author: modbender MIT 6 files body ≈ 2 826 tokens Open the sourcegithub.com↗ analyzed 2 d ago

This library provides ready-to-use Python functions that AI agents can leverage to perform various tasks including file operations, text analysis, data…

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, failures and branches

ProcedureGitHubAI and agentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw 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) Goose OpenCode
JSON
Technical rating
B
88/100
safety, quality, tests
Safety 60%
95
Quality 40%
77
Run on models
none yet
Process rating
D
46/100
Unfinished process
Result and completion w 14
0
Failures and branches w 10
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • medium Dangerous commands cmd-privilege INSTALLATION.md:268
      Privilege escalation / world-writable permissions
      sudo python ai_agent_tools.py

    Files scanned: 6. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)

    Process rating: all ten parameters 46/100

    • 0Result and completion. Does not say what the result is
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 5 mutating operations with no state check
    • 40Consistency. Frontmatter name (AI Agent Tools - Python Utility Library for AI Agents) differs from the folder (ai-agent-tools)
    • 60Tools and files. Uses tools (git, python) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 39 steps
    • 100Execution cost. Instruction body is 2826 tokens
    • low 14 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
    • -215 emoji in the instructions: noise for the model
    • +1No license
    • +2Single-language instructions
    • +3Description length 200: enough signal without eating the budget
    • +4Structure: 35 headings
    • +3Step-by-step instructions: 39 items
    • +4Has examples (19 code blocks)

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