fabric-forecasting skillA
fabric-forecasting is agent-read markdown (skill) from jnpiyush/agentx: Build time-series forecasting pipelines on Microsoft Fabric - data preparation, profiling, clustering, feature engineering, and model training. Use when implementing demand forecasting, training LightGBM/Prophet models, engineering time-series features, or deploying prediction pipelines on Fabric..
Indexed from public GitHub and served as immutable, content-addressed versions. Install it pinned to an exact SHA-256 with the mdr CLI, and every file is verified against the hash recorded here before it reaches your agent. The deterministic audit below grades the latest version, and the same file always earns the same grade.
What the file says
# Fabric Forecasting > Time-series forecasting pipelines on Fabric - from raw data to trained models with profiling, clustering, and feature engineering. ## When to Use - Building demand forecasting models (retail, supply chain, finance) - Forecasting across many series (products, stores, regions) - Classifying time-series patterns (regular, intermittent, lumpy, erratic) - Creating feature-engineered datasets for ML models - Training and tuning LightGBM, Prophet, or ensemble models on Fabric ## Decision Tree ``` Need time-series forecasting on Fabric? +- Have historical data in Lakehouse? | +- Yes -> Start at Phase 1 (Intake & Discovery) | - No -> Use fabric-analytics skill to ingest data first +- Know the forecasting scenario? | +- Clear requirements -> Start at Phase 2 (Scenario Interpretation) | - Need discovery -> Start at Phase 1 +- Have a customization plan? | - Yes -> Start at Phase 4 (Notebook Generation) +- Which model? | +- Many series + external features -> LightGBM [PASS] | +- Few series + strong seasonality -> Prophet [PASS] | +- Intermittent demand -> Specialized methods (Croston, SBA) | - Unsure -> Profile data first (Phase 1-2), then decide - Not forecasting? …
Read the whole file at its exact version.
How to install
mdr add jnpiyush/agentx/fabric-forecasting@v1.0.0mdr add jnpiyush/agentx/fabric-forecasting@sha256:efa3a90a5de399baPin to a label to follow the author's releases, or to a sha256 to freeze the exact bytes forever. Either way the resolved hash is written to mdr.lock, and mdr install reproduces it on any machine.
[](https://markdownregistry.com/a/art_zxm7mnifoljehl7r)
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Versions
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| v1.0.0 latest | 2026-09-15 | f17c505 | 13,396 B | A | view · diff |
| v1.0.0 | 2026-03-01 | 969f335 | 13,394 B | A | view · diff |
| v1.0.0 | 2026-02-28 | aa03a2e | 11,946 B | A | view |
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (13396 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
jnpiyush/agentx · 16 stars · license Apache-2.0 · pushed 2026-09-21 · branch master
API
GET https://markdownregistry.com/api/v1/artifacts/art_zxm7mnifoljehl7r GET https://markdownregistry.com/api/v1/resolve?ref=jnpiyush/agentx/fabric-forecasting GET https://markdownregistry.com/api/v1/blob/efa3a90a5de399ba36c85a6037c56314ff8a8cb037df998a012ba573ea18fa1a
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