timesfm-forecasting

timesfm-forecasting is a skill for Claude Code, Codex from JZKK720/cubecloud-skills-bundle-kit. It costs 140 tokens per session (5,190 once invoked), scanned A, original, no licence file.

A skill for forecasting future values in a time series, meaning measurements recorded over time, with Google's TimesFM model. It supports forecasts such as sales, sensor readings, prices, energy demand, health measurements, weather, and scientific data without training a custom model.

In plain words
What is it for?
It is for predicting upcoming values in a single time series, such as future demand, readings, prices, or weather measurements.
Why use it?
It provides a way to make forecasts without building and training a separate model for each time-based dataset.

Skill for Claude CodeCodex

Which agent this was written for is unclear — body not stored (licence); the path alone says nothing.

Good fit It is for predicting upcoming values in a single time series, such as future demand, readings, prices, or weather measurements.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/jzkk720/cubecloud-skills-bundle-kit/timesfm-forecasting
Install

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.

Any agent
npx skills add JZKK720/cubecloud-skills-bundle-kit --skill timesfm-forecasting
Clone the repo
git clone --depth 1 https://github.com/JZKK720/cubecloud-skills-bundle-kit

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for timesfm-forecasting

README.md
[![agentmods](https://agentmods.dev/badge/skills/jzkk720/cubecloud-skills-bundle-kit/timesfm-forecasting/github.svg)](https://agentmods.dev/skills/jzkk720/cubecloud-skills-bundle-kit/timesfm-forecasting)
Your own site
<a href="https://agentmods.dev/skills/jzkk720/cubecloud-skills-bundle-kit/timesfm-forecasting"><img src="https://agentmods.dev/badge/skills/jzkk720/cubecloud-skills-bundle-kit/timesfm-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.

agentmods 80×15 button for timesfm-forecasting

Your own site · 80×15
<a href="https://agentmods.dev/skills/jzkk720/cubecloud-skills-bundle-kit/timesfm-forecasting"><img src="https://agentmods.dev/badge/skills/jzkk720/cubecloud-skills-bundle-kit/timesfm-forecasting.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 140 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,190 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin unknown No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00140 $0.05190
Opus 5.5 $0.00056 $0.02076
Sonnet 5.5 $0.00028 $0.01038
Haiku 4.5 $0.00014 $0.00519

Measured 1mo ago against content hash cb83b741010d, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

timesfm-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.

The scan reads SKILL.md. This mod also ships 11 executable files (examples/anomaly-detection/detect_anomalies.py, examples/covariates-forecasting/demo_covariates.py, examples/finetuning/finetune_lora.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

upstream/timesfm-forecasting/SKILL.md · 512 lines

The source is not reproduced here

A licence we could not identify

The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.

Read it on GitHub

Files

What ships with it

28 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.

Changes

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.

  1. 1mo ago First seen · 512 lines · 140 tokens per session scan A cb83b741010d

Subscribe to this mod's changes

timesfm-forecasting is a skill published in the GitHub repository JZKK720/cubecloud-skills-bundle-kit (4 stars, last pushed 9d ago), with no licence file. It adds 140 tokens to every session and 5,190 once invoked, about $0.0006 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 2026-09-16.

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