TimesFM Forecasting
google-research/timesfm
Forecasts any univariate time series zero-shot with Google's TimesFM model, returning point forecasts and calibrated prediction intervals without training.
Agent skill
Many Model Forecasting
by databricks-industry-solutions in databricks-industry-solutions/many-model-forecasting
Kickstart Many Models Forecasting (MMF) projects on Databricks — explore data, profile series, configure clusters, run forecasting pipelines, and evaluate results.
Install Many Model Forecasting
$ npx skills add databricks-industry-solutions/many-model-forecasting --skill many-model-forecasting -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install databricks-industry-solutions/many-model-forecasting many-model-forecasting --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/databricks-industry-solutions/many-model-forecasting.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/databricks-skills/many-model-forecasting .claude/skills/many-model-forecasting && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "many-model-forecasting" agent skill from https://github.com/databricks-industry-solutions/many-model-forecasting/tree/main/skills/databricks-skills/many-model-forecasting into .claude/skills/many-model-forecasting/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "many-model-forecasting", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/databricks-industry-solutions/many-model-forecasting/tree/main/skills/databricks-skills/many-model-forecastingType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add databricks-industry-solutions/many-model-forecasting --skill many-model-forecasting -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install databricks-industry-solutions/many-model-forecasting many-model-forecasting --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/databricks-industry-solutions/many-model-forecasting.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/databricks-skills/many-model-forecasting .agents/skills/many-model-forecasting && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "many-model-forecasting" agent skill from https://github.com/databricks-industry-solutions/many-model-forecasting/tree/main/skills/databricks-skills/many-model-forecasting into .agents/skills/many-model-forecasting/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "many-model-forecasting", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add databricks-industry-solutions/many-model-forecasting --skill many-model-forecasting -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install databricks-industry-solutions/many-model-forecasting many-model-forecasting --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/databricks-industry-solutions/many-model-forecasting.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/databricks-skills/many-model-forecasting .cursor/skills/many-model-forecasting && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "many-model-forecasting" agent skill from https://github.com/databricks-industry-solutions/many-model-forecasting/tree/main/skills/databricks-skills/many-model-forecasting into .cursor/skills/many-model-forecasting/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "many-model-forecasting", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/databricks-industry-solutions/many-model-forecasting.git --path skills/databricks-skills/many-model-forecasting--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add databricks-industry-solutions/many-model-forecasting --skill many-model-forecasting -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install databricks-industry-solutions/many-model-forecasting many-model-forecasting --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/databricks-industry-solutions/many-model-forecasting.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/databricks-skills/many-model-forecasting .gemini/skills/many-model-forecasting && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "many-model-forecasting" agent skill from https://github.com/databricks-industry-solutions/many-model-forecasting/tree/main/skills/databricks-skills/many-model-forecasting into .gemini/skills/many-model-forecasting/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "many-model-forecasting", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install databricks-industry-solutions/many-model-forecasting many-model-forecastingInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add databricks-industry-solutions/many-model-forecasting --skill many-model-forecasting -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/databricks-industry-solutions/many-model-forecasting.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/databricks-skills/many-model-forecasting .github/skills/many-model-forecasting && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "many-model-forecasting" agent skill from https://github.com/databricks-industry-solutions/many-model-forecasting/tree/main/skills/databricks-skills/many-model-forecasting into .github/skills/many-model-forecasting/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "many-model-forecasting", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add databricks-industry-solutions/many-model-forecasting --skill many-model-forecasting -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install databricks-industry-solutions/many-model-forecasting many-model-forecasting --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/databricks-industry-solutions/many-model-forecasting.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/databricks-skills/many-model-forecasting .opencode/skills/many-model-forecasting && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "many-model-forecasting" agent skill from https://github.com/databricks-industry-solutions/many-model-forecasting/tree/main/skills/databricks-skills/many-model-forecasting into .opencode/skills/many-model-forecasting/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "many-model-forecasting", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
many-model-forecasting- GitHub stars
- 110
- Token cost
- ~6k tokens
- SKILL.md length
- 2,448 words
- Files
- 15
- Skills in repo
- 1
- Repo updated
- First seen
- Licence
- Custom licence
At a glance
Kickstart Many Models Forecasting (MMF) projects on Databricks — explore data, profile series, configure clusters, run forecasting pipelines, and evaluate results.
- Works in 3 steps: Run the precondition checks at the top… → If any precondition is missing, tell the… → Carry forward or reconfirm…
- Tasks that involve Forecasting and time series
- SKILL.md covers Overview, When to Use, Workflow Entry Point — Where… and Interaction Model — Mandatory…, plus 7 more sections
- Calls pip
What it does
Many Model Forecasting is an agent skill from databricks-industry-solutions/many-model-forecasting. Kickstart Many Models Forecasting (MMF) projects on Databricks — explore data, profile series, configure clusters, run forecasting pipelines, and evaluate results.
Its SKILL.md is about 6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 14 other files (for example `1-prep-and-clean-data.md`, `2-profile-and-classify-series.md` and `3-provision-forecasting-resources.md`).
It sits in Data & Analytics, covering Forecasting and time series. It works with Databricks. The repository describes itself as: Bootstrap your large scale forecasting solution on Databricks with Many Models Forecasting (MMF) Project.
When your agent uses it
- Tasks that involve Forecasting and time series
Example prompts
- “/many-model-forecasting”
Workflow steps
3 steps, taken from the first numbered list in SKILL.md.
- Run the precondition checks at the top of that skill file (e.g. verify {use_case}_train_data exists for Skills 2/3/4, or that…
- If any precondition is missing, tell the user exactly what's missing and offer to route back to the earliest unmet skill rather than…
- Carry forward or reconfirm {forecast_problem_brief} if it isn't in conversation context.
What it can do on your machine
Read from SKILL.md and the folder at commit 08cc77d. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
pip
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Many Model Forecasting loads about 6k tokens when it runs. Until then it costs about 47 tokens; SKILL.md has 2,448 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
Its licence (Custom licence) doesn't allow us to republish the file, so here is its outline and opening line. It has 2,448 words (~6,026 tokens).
“Automates the full Many Models Forecasting workflow on Databricks. Five skills walk you through data preparation, series profiling, cluster setup, pipeline execution, and post-evaluation interactively using Databricks MCP tools and AskUserQuestion.”
- name
- many-model-forecasting
Files
SKILL.md and 14 other files in skills/databricks-skills/many-model-forecasting of databricks-industry-solutions/many-model-forecasting.
- SKILL.md
- 1-prep-and-clean-data.md
- 2-profile-and-classify-series.md
- 3-provision-forecasting-resources.md
- 4-execute-mmf-forecast.md
- 5-post-process-and-evaluate.md
- 6-hierarchical-reconciliation.md
- mmf_global_ml_notebook_template.ipynb
- mmf_gpu_orchestrator_notebook_template.ipynb
- mmf_gpu_run_notebook_template.ipynb
- mmf_local_notebook_template.ipynb
- mmf_post_process_notebook_template.ipynb
- mmf_prep_notebook_template.ipynb
- mmf_profiling_notebook_template.ipynb
- mmf_reconciliation_notebook_template.ipynb
Open the folder on GitHubat commit 08cc77d
Compare with similar skills
Many Model Forecasting next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Many Model Forecasting this skilldatabricks-industry-solutions/many-model-forecasting | 110 | — | ~6k | Automated safety check: Pass | Custom licence | |
| TimesFM Forecastinggoogle-research/timesfm | 34k | — | ~4.7k | Automated safety check: Pass | Apache-2.0 | |
| Timesfm ForecastingzLanqing/codex-claude-academic-skills | 4.6k | 6 repos | ~7.5k | Automated safety check: Notes | Apache-2.0 | |
| StatsmodelszLanqing/codex-claude-academic-skills | 4.6k | 16 repos | ~4.9k | Automated safety check: Pass | BSD-3-Clause | |
| Chdb Datastorevemetric/vemetric | 394 | 2 repos | ~1.4k | Automated safety check: Pass | Apache-2.0 | |
| Alphaear Predictorninehills/skills | 281 | 2 repos | ~531 | Automated safety check: Pass | None |
Similar skills
Timesfm Forecasting
zLanqing/codex-claude-academic-skills
Zero-shot time series forecasting with Google's TimesFM foundation model.
Statsmodels
zLanqing/codex-claude-academic-skills
Statistical models library for Python. An agent skill from zLanqing/codex-claude-academic-skills.
Chdb Datastore
vemetric/vemetric
A skill your agent uses when the user has tabular data (pandas DataFrame, parquet, csv, Arrow, json) and wants to filter, group, aggregate, join, or speed up slow pandas.
Alphaear Predictor
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Find Hypertable Candidates
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A skill your agent uses to analyze an existing PostgreSQL database and identify which tables should be converted to Timescale/TimescaleDB hypertables.
Related
Works with
Categories
Questions about Many Model Forecasting
What does Many Model Forecasting do?
Kickstart Many Models Forecasting (MMF) projects on Databricks — explore data, profile series, configure clusters, run forecasting pipelines, and evaluate results. Many Model Forecasting is an agent skill from databricks-industry-solutions/many-model-forecasting. Kickstart Many Models Forecasting (MMF) projects on Databricks — explore data, profile series, configure clusters, run forecasting pipelines, and evaluate results.
When should I use Many Model Forecasting?
Many Model Forecasting fits situations like: tasks that involve Forecasting and time series.
How do I install Many Model Forecasting in Claude Code?
Run `npx skills add databricks-industry-solutions/many-model-forecasting --skill many-model-forecasting -a claude-code`. Or copy the skill folder (skills/databricks-skills/many-model-forecasting in databricks-industry-solutions/many-model-forecasting) into .claude/skills/many-model-forecasting in your project. Claude Code loads it when a task matches its description.
How do I install Many Model Forecasting in Codex?
Run `npx skills add databricks-industry-solutions/many-model-forecasting --skill many-model-forecasting -a codex`. Or copy the skill folder (skills/databricks-skills/many-model-forecasting in databricks-industry-solutions/many-model-forecasting) into .agents/skills/many-model-forecasting in your project. Codex loads it when a task matches its description.
Can I use Many Model Forecasting in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add databricks-industry-solutions/many-model-forecasting --skill many-model-forecasting -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/many-model-forecasting, .gemini/skills/many-model-forecasting, .github/skills/many-model-forecasting and .opencode/skills/many-model-forecasting in your project.
What does Many Model Forecasting need to run?
Going by SKILL.md and its folder, Many Model Forecasting needs the command-line tools its instructions call (pip).
Does Many Model Forecasting access the network?
SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Is Many Model Forecasting safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Many Model Forecasting use?
Many Model Forecasting has a licence file (the repository's licence) that doesn't match a standard licence. Read it on GitHub before reusing the skill.
How many tokens does Many Model Forecasting use?
About 6k tokens (SKILL.md is roughly 24k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Many Model Forecasting?
Skills that share tags, products or a category with Many Model Forecasting: TimesFM Forecasting (google-research/timesfm, 34k stars), Timesfm Forecasting (zLanqing/codex-claude-academic-skills, 4.6k stars), Statsmodels (zLanqing/codex-claude-academic-skills, 4.6k stars) and Chdb Datastore (vemetric/vemetric, 394 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Many Model Forecasting?
databricks-industry-solutions (a GitHub organization) maintains it in databricks-industry-solutions/many-model-forecasting, which has 110 GitHub stars. The repository was last updated on October 7, 2026.
Source: databricks-industry-solutions/many-model-forecasting on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.