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.
Build forecasting models with Meta's Prophet for business time series with holidays and changepoints.
Install Algo Forecast Prophet
$ npx skills add asgard-ai-platform/skills --skill algo-forecast-prophet -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install asgard-ai-platform/skills algo-forecast-prophet --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/asgard-ai-platform/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/algo-forecast-prophet .claude/skills/algo-forecast-prophet && 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 "algo-forecast-prophet" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-forecast-prophet into .claude/skills/algo-forecast-prophet/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-forecast-prophet", 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/asgard-ai-platform/skills/tree/main/algo-forecast-prophetType 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 asgard-ai-platform/skills --skill algo-forecast-prophet -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install asgard-ai-platform/skills algo-forecast-prophet --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/algo-forecast-prophet .agents/skills/algo-forecast-prophet && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "algo-forecast-prophet" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-forecast-prophet into .agents/skills/algo-forecast-prophet/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-forecast-prophet", 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 asgard-ai-platform/skills --skill algo-forecast-prophet -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install asgard-ai-platform/skills algo-forecast-prophet --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/algo-forecast-prophet .cursor/skills/algo-forecast-prophet && 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 "algo-forecast-prophet" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-forecast-prophet into .cursor/skills/algo-forecast-prophet/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-forecast-prophet", 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/asgard-ai-platform/skills.git --path algo-forecast-prophet--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 asgard-ai-platform/skills --skill algo-forecast-prophet -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install asgard-ai-platform/skills algo-forecast-prophet --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/algo-forecast-prophet .gemini/skills/algo-forecast-prophet && 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 "algo-forecast-prophet" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-forecast-prophet into .gemini/skills/algo-forecast-prophet/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-forecast-prophet", 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 asgard-ai-platform/skills algo-forecast-prophetInstalls 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 asgard-ai-platform/skills --skill algo-forecast-prophet -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/algo-forecast-prophet .github/skills/algo-forecast-prophet && 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 "algo-forecast-prophet" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-forecast-prophet into .github/skills/algo-forecast-prophet/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-forecast-prophet", 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 asgard-ai-platform/skills --skill algo-forecast-prophet -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install asgard-ai-platform/skills algo-forecast-prophet --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/algo-forecast-prophet .opencode/skills/algo-forecast-prophet && 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 "algo-forecast-prophet" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-forecast-prophet into .opencode/skills/algo-forecast-prophet/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-forecast-prophet", 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
algo-forecast-prophet- GitHub stars
- 242
- Token cost
- ~1.1k tokens
- SKILL.md length
- 400 words
- Files
- 4 (incl. references)
- Skills in repo
- 207
- Repo updated
- First seen
- Licence
- MIT
At a glance
Build forecasting models with Meta's Prophet for business time series with holidays and changepoints.
- Works in 4 steps: Input Validation → Core Algorithm → Verification → …
- The user needs user-friendly time series forecasting
- SKILL.md covers Overview, When to Use, Algorithm and Output Format, plus 3 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Algo Forecast Prophet is an agent skill from asgard-ai-platform/skills. Build forecasting models with Meta's Prophet for business time series with holidays and changepoints. Use this skill when the user needs user-friendly time series forecasting, handling of missing data and holidays, or automatic changepoint detection — even if they say 'forecast with Prophet', 'business forecast', or 'easy time series model'.
Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including reference files (for example `examples/sample_scenario.md`, `references/prophet-cv.md` and `references/prophet-tuning.md`).
It sits in Data & Analytics, covering Forecasting and time series. The repository describes itself as: 301 open-source coding agent skills across 22 domains — methodology, judgment & gotchas packaged as Claude Agent Skills for the Asgard AI Platform. The licence is MIT.
When your agent uses it
- The user needs user-friendly time series forecasting
- Handling of missing data and holidays
- Automatic changepoint detection — even if they say forecast with Prophet
- Business forecast
Example prompts
- “forecast with Prophet”
- “business forecast”
- “easy time series model”
- “/algo-forecast-prophet”
Workflow steps
4 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 4e7f4f8. 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
No scripts in the folder and no shell commands in SKILL.md (its code samples are json).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
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
Algo Forecast Prophet loads about 1.1k tokens when it runs, and up to ~5.7k if it reads all its reference files. Until then it costs about 91 tokens; SKILL.md has 400 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
The full file from asgard-ai-platform/skills at commit 4e7f4f8, republished under its MIT licence (© asgard-ai-platform). 400 words, ~1,139 tokens.
Download SKILL.mdSave it as
.claude/skills/algo-forecast-prophet/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.- name
- algo-forecast-prophet
- description
- Build forecasting models with Meta's Prophet for business time series with holidays and changepoints. Use this skill when the user needs user-friendly time series forecasting, handling of missing data and holidays, or automatic changepoint detection — even if they say 'forecast with Prophet', 'business forecast', or 'easy time series model'.
- metadata.category
- WP-47 時間序列預測
- metadata.tags
- forecasting, prophet, time-series, business-analytics
Prophet Forecasting
Overview
Prophet (Meta) decomposes time series into trend + seasonality + holidays + error. Uses an additive (or multiplicative) model fitted with Stan. Handles missing data, outliers, and holiday effects natively. Designed for business time series at daily/weekly granularity.
When to Use
Trigger conditions:
- Forecasting business metrics (sales, traffic, engagement) at daily/weekly frequency
- Data with strong seasonal patterns and known holiday effects
- Need quick, reasonable forecasts without deep time series expertise
When NOT to use:
- For high-frequency data (sub-hourly) — Prophet is designed for daily+
- When you need causal/explanatory models (Prophet is descriptive)
- For very short time series (< 2 seasonal cycles)
Algorithm
IRON LAW: Prophet Is an Additive Regression Model, NOT Classical Time Series
y(t) = g(t) + s(t) + h(t) + ε(t)
- g(t): piecewise linear or logistic trend with automatic changepoints
- s(t): Fourier series for yearly/weekly/daily seasonality
- h(t): user-specified holiday effects
Prophet does NOT model autocorrelation in residuals. If residuals are
autocorrelated, the uncertainty intervals will be too narrow.Phase 1: Input Validation
Prepare DataFrame with columns: ds (datestamp), y (metric). Add regressor columns if available. Specify: country holidays, custom holidays, growth type. Gate: Data formatted, minimum 2 full seasonal cycles.
Phase 2: Core Algorithm
- Choose growth model: 'linear' (default) or 'logistic' (with cap and floor)
- Set seasonality: yearly (default), weekly (default), custom (e.g., monthly)
- Add holidays: country built-ins + custom events (promotions, launches)
- Fit model:
m = Prophet(); m.fit(df) - Generate future DataFrame and predict:
m.predict(future)
Phase 3: Verification
Check: forecast components (trend, seasonality, holidays) are intuitive. Cross-validate: use Prophet's built-in cross_validation() with rolling windows. Evaluate MAPE, RMSE.
Gate: MAPE acceptable for use case, components pass visual inspection.
Phase 4: Output
Return forecast with decomposed components.
Output Format
{
"forecasts": [{"ds": "2025-04-15", "yhat": 1200, "yhat_lower": 1050, "yhat_upper": 1350}],
"components": {"trend": "upward_3pct", "yearly_seasonality": "peak_in_december", "weekly_seasonality": "low_on_weekends"},
"metadata": {"mape": 0.08, "training_days": 730, "forecast_days": 90}
}Examples
Sample I/O
Input: 2 years of daily website traffic with Christmas spike and summer dip Expected: Forecast captures: upward trend, weekly pattern (weekday > weekend), annual pattern (Christmas spike, summer dip).
Show full SKILL.md (147 more words)Show less
Edge Cases
| Input | Expected | Why |
|---|---|---|
| Many missing days | Prophet handles natively | Unlike ARIMA, no imputation needed |
| Sudden trend change | Changepoint detected automatically | Prophet's key feature vs ARIMA |
| Multiplicative seasonality | Set seasonality_mode='multiplicative' | When seasonal amplitude grows with trend |
Gotchas
- Default changepoint sensitivity: Prophet may over/under-detect trend changes. Tune
changepoint_prior_scale(default 0.05): higher = more flexible, lower = smoother. - Flat forecasts: If trend changepoints are too conservative, long-range forecasts can be unrealistically flat. Increase flexibility or specify growth cap.
- Holiday effects require specification: Prophet doesn't discover holidays automatically. You must provide a holiday DataFrame — missing holidays will not be modeled.
- Not for causal inference: Prophet finds patterns but doesn't explain why. Adding a regressor shows correlation, not causation.
- Uncertainty intervals: Based on historical trend change variance, not residual autocorrelation. May be too narrow if residuals are structured.
References
- For Prophet hyperparameter tuning guide, see
references/prophet-tuning.md - For cross-validation best practices, see
references/prophet-cv.md
© asgard-ai-platform, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 3 other files (references) in algo-forecast-prophet of asgard-ai-platform/skills.
- SKILL.md
- examples/sample_scenario.md
- references/prophet-cv.md
- references/prophet-tuning.md
Open the folder on GitHubat commit 4e7f4f8
Compare with similar skills
Algo Forecast Prophet 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 |
|---|---|---|---|---|---|---|
| Algo Forecast Prophet this skillasgard-ai-platform/skills | 242 | — | ~1.1k | Automated safety check: Pass | MIT | |
| TimesFM Forecastinggoogle-research/timesfm | 34k | — | ~4.7k | Automated safety check: Pass | Apache-2.0 | |
| StatsmodelszLanqing/codex-claude-academic-skills | 4.7k | 15 repos | ~4.9k | Automated safety check: Pass | BSD-3-Clause | |
| Timesfm ForecastingzLanqing/codex-claude-academic-skills | 4.7k | 3 repos | ~7.5k | Automated safety check: Notes | Apache-2.0 | |
| Find Hypertable Candidatestimescale/pg-aiguide | 1.9k | 1 repos | ~2.6k | Automated safety check: Pass | Apache-2.0 | |
| Pensieve Searcharkohut/pensieve | 1.4k | — | ~8.2k | Automated safety check: Pass | Apache-2.0 |
Similar skills
Statsmodels
zLanqing/codex-claude-academic-skills
Statistical models library for Python. An agent skill from zLanqing/codex-claude-academic-skills.
Timesfm Forecasting
zLanqing/codex-claude-academic-skills
Zero-shot time series forecasting with Google's TimesFM foundation model.
Find Hypertable Candidates
timescale/pg-aiguide
A skill your agent uses to analyze an existing PostgreSQL database and identify which tables should be converted to Timescale/TimescaleDB hypertables.
Pensieve Search
arkohut/pensieve
Search the user's local Pensieve screenshot archive by text, app, or time range.
Alphaear Predictor
ninehills/skills
Market prediction skill using Kronos. An agent skill from ninehills/skills.
More from asgard-ai-platform/skills
All 207 skills in this repoAlgo Ecom Bm25
asgard-ai-platform/skills
Implement BM25 ranking function for e-commerce product search relevance scoring.
Algo Mfg Cpk
asgard-ai-platform/skills
Calculate Cpk process capability index to assess whether a process meets specification requirements.
Algo Price Elasticity
asgard-ai-platform/skills
Calculate price elasticity of demand to quantify how price changes affect sales volume.
Algo Rank Bayesian
asgard-ai-platform/skills
Apply Bayesian averaging to rank items by combining observed ratings with prior expectations.
Algo Rank Elo
asgard-ai-platform/skills
Implement Elo rating system to rank items or players from pairwise comparison outcomes.
Algo Rank Wilson
asgard-ai-platform/skills
Calculate Wilson Score confidence intervals for ranking items by positive proportion with sample size correction.
Related
Categories
Questions about Algo Forecast Prophet
What does Algo Forecast Prophet do?
Build forecasting models with Meta's Prophet for business time series with holidays and changepoints. Algo Forecast Prophet is an agent skill from asgard-ai-platform/skills. Build forecasting models with Meta's Prophet for business time series with holidays and changepoints.
When should I use Algo Forecast Prophet?
Algo Forecast Prophet fits situations like: the user needs user-friendly time series forecasting; handling of missing data and holidays; automatic changepoint detection — even if they say forecast with Prophet; business forecast.
How do I install Algo Forecast Prophet in Claude Code?
Run `npx skills add asgard-ai-platform/skills --skill algo-forecast-prophet -a claude-code`. Or copy the skill folder (algo-forecast-prophet in asgard-ai-platform/skills) into .claude/skills/algo-forecast-prophet in your project. Claude Code loads it when a task matches its description.
How do I install Algo Forecast Prophet in Codex?
Run `npx skills add asgard-ai-platform/skills --skill algo-forecast-prophet -a codex`. Or copy the skill folder (algo-forecast-prophet in asgard-ai-platform/skills) into .agents/skills/algo-forecast-prophet in your project. Codex loads it when a task matches its description.
Can I use Algo Forecast Prophet 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 asgard-ai-platform/skills --skill algo-forecast-prophet -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/algo-forecast-prophet, .gemini/skills/algo-forecast-prophet, .github/skills/algo-forecast-prophet and .opencode/skills/algo-forecast-prophet in your project.
What does Algo Forecast Prophet need to run?
SKILL.md names no scripts, command-line tools or credentials: Algo Forecast Prophet is instructions for the agent only.
Does Algo Forecast Prophet access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Algo Forecast Prophet 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 Algo Forecast Prophet use?
Algo Forecast Prophet is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Algo Forecast Prophet use?
About 1.1k tokens (SKILL.md is roughly 4.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 4.6k tokens, read only when the agent opens those files.
What are the alternatives to Algo Forecast Prophet?
Skills that share tags, products or a category with Algo Forecast Prophet: TimesFM Forecasting (google-research/timesfm, 34k stars), Statsmodels (zLanqing/codex-claude-academic-skills, 4.7k stars), Timesfm Forecasting (zLanqing/codex-claude-academic-skills, 4.7k stars) and Find Hypertable Candidates (timescale/pg-aiguide, 1.9k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Algo Forecast Prophet?
asgard-ai-platform (a GitHub organization) maintains it in asgard-ai-platform/skills, which has 242 GitHub stars. The repository holds 207 skills in this directory. The repository was last updated on June 6, 2026.
Source: asgard-ai-platform/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.