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AC tarot-content

Generate tarot × astrology content for social media — weekly horoscope scripts, tarot spreads, video scripts, and cover art. Use when asked to "create tarot content", "weekly horoscope", "tarot reading script", "zodiac video", "astrology content calendar", "tarot spread design", or "horoscope video script". Supports 12-sign weekly readings, event-driven specials (retrogrades, eclipses, conjunctions), and multi-platform formatting (YouTube Shorts, TikTok, Instagram, blog).

modbender/skill-library-mcp Agent Skills author: modbender MIT 3 files body ≈ 1 611 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Generate tarot × astrology content for social media — weekly horoscope scripts, tarot spreads, video scripts, and cover art.

As a process C 52/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches

GeneratorYouTubeMedia and videoMarketingPersonal productivitytype 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
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
82
Run on models
none yet
Process rating
C
52/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
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: 3. 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 52/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 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
    • 85Steps. 27 steps, 1 vague phrases
    • 100Tools and files. No external tools needed
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1611 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

    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • -41 reference files, but SKILL.md never points to them: the model will not open them
    • -31 of 1 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 7 example trigger phrases
    • +3Description length 476: enough signal without eating the budget
    • +4Structure: 19 headings
    • +3Step-by-step instructions: 27 items
    • +4Has examples (6 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.