timesfm-forecasting
timesfm-forecasting is a Time series & forecasting skill for AI agents, published by mkurman in the zorai repository on GitHub. Skillspace rates timesfm-forecasting B (80/100) and it passed the safety scan; a judge model has also reviewed it.
Zero-shot time series forecasting with Google's TimesFM foundation model. Use for any univariate time series (sales, sensors, energy, vitals, weather) without training a custom model. Supports CSV/DataFrame/array inputs with point forecasts and prediction intervals. Includes a preflight system checker script to verify RAM/GPU before first use.
- Grade
- B
- Quality
- 80 / 100
- Safety
- Passed
- GitHub stars
- 320
Placed #2 of 2 when judged against its near-duplicates in Time series & forecasting.
Not launch-quality. It is graded B with a clean safety verdict, but our quality filters exclude it: it is a near-duplicate variant of another skill. So it is not in our launch-quality set, the skills our MCP server searches and recommends. How launch-quality is decided
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 88 / 100
- Maturity
- Packaged: licensed, with a version or references
- Security scan
- No issues found
- License
- MIT
- Adoption
- Adopted by 0 other repositories, in 1 collections
- Scope
- Vendor-specific: built around one product. Forecasting with Google's public TimesFM foundation model
Judge review
Placed #2 of 2 when judged against its near-duplicates in Time series & forecasting.
- Trigger
78 78 - Instructions
85 85 - Specificity
88 88 - Safety
76 76 - Disclosure
78 78 - Maintenance
76 76
Strong runnable quick-start, CSV, and covariate examples, but no visible routing guidance and a less-specified safety gate.
Not checked yet
- No blind task eval yet
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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