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AC edge-tts-unlimited

Free, unlimited text-to-speech using Microsoft Edge's neural voices via Python edge-tts. No API key, no credits, no character limits. Handles long-form content (tested 55+ minutes). Use when: (1) Converting text to speech for audio briefs, podcasts, or voice notes. (2) Generating long-form audio (news briefs, articles, stories) without hitting API limits. (3) Need TTS on a headless server (Fly.io, VPS, Docker) with zero setup cost. NOT for: Real-time streaming TTS, voice cloning, or premium voice acting (use ElevenLabs for that).

modbender/skill-library-mcp Agent Skills author: modbender MIT 2 files · 1 script body ≈ 795 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Free, unlimited text-to-speech using Microsoft Edge's neural voices via Python edge-tts.

As a process C 55/100 · Has gaps — weak spots: result and completion, failures and branches, running it twice

IntegrationDockerSoftware developmentInfrastructureWriting and documentstype 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
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
91
Run on models
none yet
Process rating
C
55/100
Has gaps
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: 2. 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 55/100

    • 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
    • 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
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 11 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 795 tokens

    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
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 535: enough signal without eating the budget
    • +4Structure: 9 headings
    • +3Step-by-step instructions: 11 items
    • +4Has examples (6 code blocks)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.