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DC voice-devotional

(no description)

Not recommendedlow grade D
modbender/skill-library-mcp Agent Skills author: modbender MIT 25 files body ≈ 2 479 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Generate scripture readings and lessons in the user's voice using ElevenLabs TTS.

As a process C 54/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, running it twice

ProcedureLearningtype 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
D
55/100
safety, quality, tests
Safety 60%
92
Quality 40%
0
Run on models
none yet
Process rating
C
54/100
Has gaps
When it triggers w 12
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: voice-devotional (modbender/skill-library-mcp)

How to improve

  1. Add a description to the frontmatter: without it the skill never triggers.
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 · 8

✓ No critical or high findings

Medium and low: 8
  • low Exfiltration read-dotenv BUILD_SUMMARY.txt:297
    Reads a .env file
    2. cp .env.example .env
  • low Exfiltration read-dotenv DEPLOYMENT.md:105
    Reads a .env file
    cp .env.example .env
  • low Exfiltration read-dotenv IMPLEMENTATION.md:129
    Reads a .env file
    cp .env.example .env
  • low Exfiltration read-dotenv IMPLEMENTATION.md:339
    Reads a .env file
    cp .env.example .env
  • low Exfiltration read-dotenv README.md:12
    Reads a .env file
    cp .env.example .env
  • low Exfiltration read-dotenv README.md:263
    Reads a .env file
    cp .env.example .env
  • low Exfiltration read-dotenv README.md:336
    Reads a .env file
    cat .env | grep ELEVEN_LABS_API_KEY
  • low Exfiltration read-dotenv SKILL.md:29
    Reads a .env file
    cp .env.example .env

Files scanned: 24. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • error frontmatter SKILL.md: no YAML frontmatter block found
  • error name-missing SKILL.md: frontmatter has no `name`
  • error description-missing SKILL.md: no `description` — the skill can never trigger

Process rating: all ten parameters 54/100

  • 0When it triggers. No condition that starts the skill
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 5 mutating operations with no state check
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 100Steps. 69 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2479 tokens
  • low 17 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)
  • +3Description length 0: 120–800 characters recommended
  • -32 of 6 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +4Structure: 38 headings
  • +3Step-by-step instructions: 69 items
  • +3Output format is stated explicitly
  • +4Has examples (14 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 0.