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AD zodiac-horoscope

Fetch personalized daily horoscope forecasts from zodiac-today.com API based on natal chart calculations. Use when a user wants: (1) daily guidance on what activities to pursue or avoid, (2) life planning help — best days for interviews, travel, romance, important decisions, (3) energy/focus/luck/romance forecasts to optimize their schedule, (4) lucky colors and numbers for the day, (5) future date analysis for planning events, trips, or milestones (paid tiers). Triggers: horoscope, zodiac, star sign forecast, daily guidance, lucky day, best day to, astrology advice, what should I do today, is today a good day for, plan my week astrology. Required env: ZODIAC_API_KEY (hsk_ API key), ZODIAC_PROFILE_ID (birth chart profile ID). Collects sensitive PII (email, birth date, birth city) for natal chart — requires user consent.

modbender/skill-library-mcp Agent Skills author: modbender MIT 2 files body ≈ 1 224 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Fetch personalized daily horoscope forecasts from zodiac-today.com API based on natal chart calculations. Use when a user wants: (1) daily guidance on what…

As a process D 48/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationSoftware developmentData and analyticstype 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%
85
Run on models
none yet
Process rating
D
48/100
Unfinished process
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: 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 48/100

    • 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
    • 20When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 100Steps. 28 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1224 tokens
    • 100Running it twice. Mutating operations check current state
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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 831: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +4Structure: 14 headings
    • +3Step-by-step instructions: 28 items
    • +4Has examples (3 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.