Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add jnPiyush/AgentX --skill fabric-forecastinggit clone --depth 1 https://github.com/jnPiyush/AgentXWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/jnpiyush/agentx/fabric-forecasting)<a href="https://agentmods.dev/skills/jnpiyush/agentx/fabric-forecasting"><img src="https://agentmods.dev/badge/skills/jnpiyush/agentx/fabric-forecasting/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/jnpiyush/agentx/fabric-forecasting"><img src="https://agentmods.dev/badge/skills/jnpiyush/agentx/fabric-forecasting.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 312 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00055 | $0.03116 |
| Opus 5.5 | $0.00022 | $0.01246 |
| Sonnet 5.5 | $0.00011 | $0.00623 |
| Haiku 4.5 | $0.00006 | $0.00312 |
Grade A, and why
fabric-forecasting scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 327 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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?
+- Ad-hoc analytics -> Use fabric-analytics skill
- Chat-based Q&A -> Use fabric-data-agent skill
Pipeline Overview
The forecasting pipeline follows a 5-phase workflow organized as a notebook pipeline:
Phase 1: Intake --> Phase 2: Interpret --> Phase 3: Plan --> Phase 4: Notebooks --> Phase 5: Finalize
|
--------+------------------------
NB01 NB02 NB03 NB04 NB05
Prep Profile Cluster Feature Train
Phase 1: Intake & Data Discovery
Goal: Understand the data and user's forecasting scenario.
| Step | Action | Output |
|---|---|---|
| 1 | Gather inputs (workspace, lakehouse, table) | User requirements |
| 2 | Discover table schema and date range | Data inventory |
| 3 | Identify time column, target, ID columns | Column mapping |
| 4 | Calculate basic statistics (seasonality, trend) | Data profile |
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago Changed efa3a90a5de3
- 1mo ago First seen · 327 lines · 55 tokens per session scan A 3079ad1588ed
fabric-forecasting is a skill published in the GitHub repository jnPiyush/AgentX (16 stars, last pushed 3d ago), licensed Apache-2.0. It adds 55 tokens to every session and 3,116 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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