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AB horoscope-daily

Generates personalized horoscope readings for all twelve zodiac signs based on Western astrology. Supports any date (today, tomorrow, a specific date) and any time period (daily/weekly/monthly), covering five dimensions — Overall, Love, Career, Finance, and Health — with a Lucky Guide and Daily Tip. Can deliver personalized readings based on the user's birthday. Pure LLM generation, no external API required. Trigger words: horoscope, daily horoscope, zodiac forecast, what's my horoscope today, weekly horoscope, monthly horoscope, star sign reading, astrology forecast, what's my sign today. Not intended for: Ba Zi (Four Pillars), Zi Wei Dou Shu, Tarot, Numerology, or Feng Shui — use fortune-hub to find the right skill for those domains.

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 15 files body ≈ 4 050 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Generates personalized horoscope readings for all twelve zodiac signs based on Western astrology.

As a process B 76/100 · Nearly there — weak spots: running it twice, progress reporting

GeneratorData and analyticsAI and agentstype 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
B
76/100
Nearly there
Progress reporting w 2
0
Running it twice w 4
30
When it triggers w 12
50
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: 15. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "clawhub-slug"
    • note frontmatter-key unknown frontmatter key "clawhub-owner"
    • note frontmatter-key unknown frontmatter key "homepage"

    Process rating: all ten parameters 76/100

    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 6 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 60Result and completion. Output format stated, no completion criterion
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 70Execution cost. Instruction body is 4050 tokens
    • 85Steps. 51 steps, 3 vague phrases
    • 100Tools and files. No external tools needed
    • 100Failures and branches. 1 branches, has a failure section
    • 100Consistency. Name and required fields are in place

    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
    • -254 emoji in the instructions: noise for the model
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 745: enough signal without eating the budget
    • +4Structure: 18 headings
    • +3Step-by-step instructions: 51 items
    • +3Output format is stated explicitly
    • +4Has examples (6 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)
    • +1License stated

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