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AD pywayne-cross-comm

WebSocket-based cross-language communication service with support for multiple message types (text, JSON, dict, bytes, images, files, folders) and client management. Use when building real-time communication applications between Python and other languages, needing multi-device messaging, or requiring file transfer via Aliyun OSS. Supports both server and client roles with heartbeat, client list management, and message filtering by type or sender.

modbender/skill-library-mcp Agent Skills author: modbender MIT 1 file body ≈ 1 702 tokens Open the sourcegithub.com↗ analyzed 2 d ago

WebSocket-based cross-language communication service with support for multiple message types (text, JSON, dict, bytes, images, files, folders) and client…

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

GeneratorSoftware developmentDesigntype 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
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
84
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

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 · 0

    ✓ No critical or high findings

    Files scanned: 1. 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 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. 3 mutating operations with no state check
    • 40Consistency. Frontmatter name (pywayne-cross-comm) differs from the folder (cross-comm)
    • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 8 steps
    • 100Execution cost. Instruction body is 1702 tokens
    • low 11 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 450: enough signal without eating the budget
    • +4Structure: 21 headings
    • +3Step-by-step instructions: 8 items
    • +4Has examples (15 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.