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CD predictclaw

Predict.fun skill with a PolyClaw-style CLI for markets, wallet funding, trading, positions, and hedging.

ClawHub Agent Skills author: walioo v0.1.11 MIT-0 66 files body ≈ 3 507 tokens Open the sourceclawhub.ai↗ analyzed 12 h ago

Predict.fun skill with a PolyClaw-style CLI for markets, wallet funding, trading, positions, and hedging.

As a process D 39/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationInfrastructuretype 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%
78
Quality 40%
66
Run on models
none yet
Process rating
D
39/100
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

The same skill appears in 1 more place: ClawHub

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Exfiltration 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 instructions or scripts send data out: environment variables, keys, file contents, chat history. You may never notice, because the agent performs the upload as "part of the task".

For the author

If the upload is not needed for the task, remove it: catalogs flag such skills and delist them. If it is needed, name the destination explicitly, say what leaves the machine, and give the user a switch.

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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 · 18

✓ No critical or high findings

Medium and low: 18
  • medium Exfiltration net-redirectable-api-key tests/smoke/test_testnet_smoke.py:28
    Helper sends the API key to a host configured by an environment variable — the key can be redirected to another server
    API key + configurable base URL from environment
  • low Exfiltration read-dotenv README.md:29
    Reads a .env file
    cp .env.example .env
  • low Exfiltration read-dotenv README.md:41
    Reads a .env file
    cd {baseDir} && cp .env.example .env
  • low Secrets in code secret-high-entropy-token README.md:229
    High-entropy token-like string (may be an id, hash or a credential)
    uv run python scripts/predictclaw.py wallet withdraw usdt 1 0xb3…310 --json
  • low Secrets in code secret-high-entropy-token README.md:230
    High-entropy token-like string (may be an id, hash or a credential)
    uv run python scripts/predictclaw.py wallet withdraw bnb 0.1 0xb3…310 --json
  • low Exfiltration read-dotenv README.zh-CN.md:29
    Reads a .env file
    cp .env.example .env
  • low Exfiltration read-dotenv README.zh-CN.md:41
    Reads a .env file
    cd {baseDir} && cp .env.example .env
  • low Secrets in code secret-high-entropy-token README.zh-CN.md:231
    High-entropy token-like string (may be an id, hash or a credential)
    uv run python scripts/predictclaw.py wallet withdraw usdt 1 0xb3…310 --json
  • low Secrets in code secret-high-entropy-token README.zh-CN.md:232
    High-entropy token-like string (may be an id, hash or a credential)
    uv run python scripts/predictclaw.py wallet withdraw bnb 0.1 0xb3…310 --json
  • low Secrets in code secret-high-entropy-token scripts/poc_session_event_compatibility.py:627
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "bind…sk9": False,
    quoted
  • low Secrets in code secret-high-entropy-token scripts/poc_session_event_compatibility.py:740
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    evidence["bind…sk9"] = False
    quoted
  • low Secrets in code secret-high-entropy-token scripts/poc_session_event_compatibility.py:796
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    evidence["bind…sk9"] = not binding_gaps
    quoted
  • low Secrets in code secret-high-entropy-token scripts/poc_session_event_compatibility.py:800
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    and evidence["bind…sk9"]
    quoted
  • low Exfiltration read-dotenv SKILL.md:19
    Reads a .env file
    cp .env.example .env
  • low Exfiltration read-dotenv SKILL.md:31
    Reads a .env file
    cd {baseDir} && cp .env.example .env
  • low Secrets in code secret-high-entropy-token SKILL.md:182
    High-entropy token-like string (may be an id, hash or a credential)
    cd {baseDir} && uv run python scripts/predictclaw.py wallet withdraw usdt 1 0xb3…310 --json
  • low Secrets in code secret-high-entropy-token SKILL.md:194
    High-entropy token-like string (may be an id, hash or a credential)
    cd {baseDir} && uv run python scripts/predictclaw.py wallet withdraw usdt 1 0xb3…310 --json
  • low Secrets in code secret-high-entropy-token SKILL.md:195
    High-entropy token-like string (may be an id, hash or a credential)
    cd {baseDir} && uv run python scripts/predictclaw.py wallet withdraw bnb 0.1 0xb3…310 --json

Files scanned: 38. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 39/100

  • 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
  • 30Running it twice. 10 mutating operations with no state check
  • 40Consistency. Frontmatter name (predictclaw) differs from the folder (predictclaw-publish-docs-20260320)
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 100Steps. 38 steps
  • 100Execution cost. Instruction body is 3507 tokens
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 10 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)
  • +3Description length 105: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • -310 of 11 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +4Structure: 19 headings
  • +3Step-by-step instructions: 38 items
  • +4Has examples (12 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 66.

External checks

ClawHub: suspicious
PredictClaw is a disclosed trading and wallet skill, but it gives sensitive keys and authenticated requests to configurable external targets in ways users should review carefully.
LLM: suspicious (high) · VirusTotal: · 12 Sept 2026