Market prediction skill using Kronos. Use when user needs finance market time-series forecasting or news-aware finance market adjustments.

What to consider before installing/using this skill: 1. Declare and verify required environment/config: The registry lists no env vars but the code uses EMBEDDING_MODEL and optional KRONOS_MODEL_PATH and also reads a .env file. Ask the publisher to explicitly list required env vars and config paths, and do not place unrelated secrets in the .env used by this skill. 2. Check and pin dependencies and installation: The skill needs torch, transformers, sentence-transformers, etc. There is no install script or pinned versions. Install in a sandboxed environment (container/VM) and pin dependency versions before running. 3. Audit model checkpoints before loading: The skill will load local 'kronos_news_*.pt' files and may fall back to an unsafe torch.load. Only use trusted checkpoints. Prefer removing the legacy torch.load fallback or force weights_only loads and verify files (checksums/signatures) before use. 4. Expect network activity: If local models are missing, the code will download tokenizers/models from Hugging Face. If you require offline operation, pre-populate the expected artifacts and set local-only flags. Be prepared for large downloads and verify host trust. 5. Prompt/tool behavior: The bundled prompts instruct agents to call web_search, fetch_news_content and to retrieve live prices. Confirm what tool integrations are available and whether they require API keys (and where those keys are stored). Do not give the skill access to broad network or production credentials until audited. 6. Path mismatch & repository layout: SKILL.md says exports/models in project root but code looks under scripts/predictor/exports/models — confirm actual expected path to avoid accidentally placing files in the wrong location. 7. Run tests in isolation: Use the included tests (tests/test_predictor.py) in an isolated environment first. Consider a code review of the checkpoint-loading logic and remove unsafe fallbacks. 8. Ask for provenance: The skill has no homepage and unknown source. Prefer skills with a reputable source or request the author/publisher to provide a source repo, dependency lock, and signed model artifacts. If you cannot verify the model checkpoints or you cannot run the skill in an isolated sandbox, treat it as higher risk and avoid running it with any credentials or on sensitive data.

Type: OpenClaw Skill Name: alphaear-predictor Version: 1.0.0 The skill is a complex financial forecasting tool that utilizes the Kronos time-series model, but it contains critical security vulnerabilities. Specifically, `scripts/kronos_predictor.py` includes a fallback to unsafe `torch.load()` (Pickle deserialization) without `weights_only=True`, which allows for Arbitrary Code Execution (RCE) if a malicious model checkpoint is placed in the exports directory. Additionally, `scripts/utils/predictor/kline_generate.py` contains a SQL injection vulnerability where the `ticker` variable is directly interpolated into a query string. While these are high-risk flaws, they appear to be unintentional vulnerabilities rather than deliberate malware.