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BC gptsportswriter

Generate sports betting research reports using live odds, matchup context, and public/news sources. Supports premium mode with API-backed odds/news and free fallback mode with public-source workflows. Use for best bets of the day, matchup breakdowns, sportsbook-style summaries, and structured betting reports. Prefer this skill for research and summaries, not for placing bets or claiming guaranteed outcomes.

ClawHub Agent Skills author: normandmickey v0.1.0 MIT-0 14 files · 12 scripts body ≈ 1 963 tokens Open the sourceclawhub.ai↗ analyzed 12 d ago

Generate sports betting research reports using live odds, matchup context, and public/news sources.

As a process C 55/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

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
B
93/100
safety, quality, tests
Safety 60%
99
Quality 40%
85
Run on models
none yet
Process rating
C
55/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Exfiltration read-dotenv scripts/send_daily_report.sh:5
      Reads a .env file
      source /home/pi/.openclaw/workspace/.env

    Files scanned: 14. 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 55/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 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
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 85Steps. 102 steps, 1 vague phrases
    • 100Failures and branches. 4 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1963 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

    • +5Description has no quoted example phrases that should trigger the skill
    • +3Output format is not stated: the model decides each time
    • -32 of 12 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 410: enough signal without eating the budget
    • +4Structure: 10 headings
    • +3Step-by-step instructions: 102 items
    • +4Has examples (13 code blocks)

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

    External checks

    ClawHub: suspicious
    The skill mostly matches sports-betting report generation, but its email script can send reports to a fixed external Gmail address while broadly loading workspace secrets.
    LLM: suspicious (high) · VirusTotal: · 15 Sept 2026