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CC polymarket-temperature-event-follower

Automated trader for Polymarket weather highest temperature markets. Scans global weather markets and executes buys during local morning window (9-10 AM) when YES price is favorable. Built for SkillPay billing integration with robust error handling and state persistence.

Not recommendedcritical or high security findings
LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 9 files body ≈ 3 440 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Automated trader for Polymarket weather highest temperature markets.

As a process C 61/100 · Has gaps — weak spots: result and completion, when it triggers

IntegrationSoftware developmentCustomer supporttype 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
C
73/100
safety, quality, tests
Safety 60%
69
Quality 40%
78
Run on models
none yet
Process rating
C
61/100
Has gaps
Result and completion w 14
0
When it triggers w 12
20
Failures and branches w 10
50
the three weakest of ten parameters · all ten

What is at stake

The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.

Secrets in code
If you install

The files contain someone else's key or token. If it is live, your agent will call third-party services under a stranger's identity; if it was revoked, the skill's scripts simply fail. Such a key often arrives with the author's whole workspace, personal data included.

For the author

The key is visible to everyone who downloaded the skill and has likely been copied by catalog-scanning bots already. Revoke it now, check bills and access logs, then reissue.

Risky intent medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The purpose itself is risky: wallets, browser password stores, offensive security. Even an honest implementation gives the agent access to things that cost money.

For the author

Explain in the description why the access is needed and how it is limited; add tests that show refusals on dangerous requests.

How to improve

  1. Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
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 · 6

  • high Secrets in code meta-credential-files .env.txt
    Credential / dotenv files bundled with the skill (1)
    .env.txt
Medium and low: 5
  • medium Risky intent intent-wallet-secrets SKILL-en.md:146
    Handles crypto-wallet secrets (seed / mnemonic / private key) — a classic stealer target
    | `PRIVATE_KEY` | Your EOA wallet private key (**SECRET!**) | MetaMask Export Private Key (NOT seed phrase) |
  • medium Risky intent intent-wallet-secrets SKILL-en.md:299
    Handles crypto-wallet secrets (seed / mnemonic / private key) — a classic stealer target
    | `PRIVATE_KEY` | string | ✅ | EOA wallet private key (hexadecimal, `0x` prefix) | `0xabc123...` | Use **dedicated wallet**, not main wallet |
  • low Exfiltration read-dotenv SKILL-en.md:74
    Reads a .env file
    copy .env.example .env
  • low Exfiltration read-dotenv SKILL.md:85
    Reads a .env file
    copy .env.example .env
  • low Risky intent intent-wallet-secrets sniper-en.py:29
    Handles crypto-wallet secrets (seed / mnemonic / private key) — a classic stealer target (quoted — discussed, not commanded)
    export PRIVATE_KEY="0x..."           # EOA wallet private key
    quoted

Files scanned: 9. 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 61/100

  • 0Result and completion. Does not say what the result is
  • 20When it triggers. No condition that starts the skill
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Steps. 74 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 3440 tokens
  • 100Running it twice. No mutating operations
  • 100Progress reporting. Reports progress
  • low 12 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
  • -4Absolute local paths (C:\Users, /home/…): not portable
  • -233 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 271: enough signal without eating the budget
  • +4Structure: 62 headings
  • +3Step-by-step instructions: 74 items
  • +4Has examples (21 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.