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AD world-cup-predictor-enhanced

Use when predicting World Cup match results and generating China Sports Lottery betting strategies (胜平负/让球/半全场/总进球/比分), including injury factor analysis, weather impact assessment, market price calibration with Polymarket, and **3 differentiated strategies** with hidden odds estimation. **All times must be 北京时间** - Polymarket endDate is UTC, requires +8h conversion.

ClawHub Agent Skills author: DeloYong v1.0.6 MIT-0 29 files body ≈ 1 248 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

Use when predicting World Cup match results and generating China Sports Lottery betting strategies (胜平负/让球/半全场/总进球/比分), including injury factor analysis…

As a process D 49/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

GeneratorSoftware 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
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
80
Run on models
none yet
Process rating
D
49/100
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: 1. 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 49/100

    • 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
    • 20When it triggers. No condition that starts the skill
    • 40Consistency. Frontmatter name (world-cup-predictor-enhanced) differs from the folder (yy-world-cup)
    • 100Tools and files. No external tools needed
    • 100Steps. 32 steps
    • 100Execution cost. Instruction body is 1248 tokens
    • 100Running it twice. No mutating operations
    • low 11 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)
    • +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
    • +1No license
    • +2Single-language instructions
    • +3Description length 368: enough signal without eating the budget
    • +4Structure: 27 headings
    • +3Step-by-step instructions: 32 items
    • +4Has examples (11 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.

    External checks

    ClawHub: suspicious
    The skill mostly matches a World Cup prediction tool, but it also includes under-disclosed maintenance code that can rewrite its own source files and create broad ZIP archives.
    LLM: suspicious (high) · VirusTotal: · 17 Jun 2026