What does this skill do, and when should you use it?
This skill packages the open source Kronos foundation model (trained on 12B candlesticks from 45 exchanges) as a Claude skill. Say something like "run kronos on AAPL" in a conversation and it pulls recent OHLCV data via yfinance, runs Kronos for a forward prediction, and returns plain-English markdown with the predicted close, direction, percentage change, and a confidence interval. It is explicitly a research tool, not financial advice. First run auto-installs dependencies and downloads roughly 500MB of model weights.
Fetches historical OHLCV data for a stock symbol or crypto pair (e.g. AAPL, BTC-USD) via yfinance; runs the Kronos foundation model inside a Python virtual environment to produce a forward forecast; and returns markdown containing the last close, predicted close after pred_len periods, direction with percentage change, a confidence band (narrow = signal, wide = noise), and the full forecast trajectory table. History period (1mo to max), candle interval (1d/1h/4h/1wk/1mo), and forecast length are all configurable.
- A retail investor who wants a quick technical-side prediction on a stock like NVDA inside Claude without setting up any ML tooling themselves
- A crypto trader forecasting short-horizon BTC-USD moves on 4-hour candles
- A researcher running /kronos weekly across a watchlist as one input into a broader research process
- An advanced user pairing Kronos chart predictions with a Claude Project containing company filings — the two-layer stack for chart-side plus fundamentals analysis
- A conversational user who states a forecast horizon in plain English (e.g. "next 30 days") and lets Claude infer the arguments
How do you install this skill?
- First run automatically clones a third-party repo, installs unpinned dependencies (including large torch) and downloads ~500MB of model weights; review and confirm before running, ideally in an isolated environment.
- Core function fully depends on yfinance and HuggingFace, which may be unreachable from mainland-China networks without workarounds.
- Model output is a probability distribution, not a price target; do not use it as investment advice; 'hedge-fund-grade' claims are marketing language.
- The repo provides no license for the skill itself; confirm terms before commercial use or redistribution.
- Shell / CLI
- Network access
- Local filesystem
Python 3gittorchtransformersyfinancepandas~500MB Kronos model weights from HuggingFace
Place the skill folder at ~/.claude/skills/kronos/ (containing SKILL.md and scripts/run_kronos.py). No manual installation is required: on first invocation the script clones the Kronos repo, creates a Python venv, installs torch/transformers/yfinance/pandas, and downloads ~500MB of model weights from HuggingFace (3-7 minutes). The repository license is unknown — verify before adopting.
git clone --depth 1 https://github.com/coopersimson96/kronos-claude-skill.git ~/.claude/skills/kronos-claude-skillGenerated from the source repository and skill path; it copies only this skill's folder. If the author's install steps above differ, follow those first. To scope it to one project, replace ~/.claude/skills with that project's .claude/skills.
How do you use this skill?
Once installed, send your agent any of these to trigger it:
- run kronos on AAPL
- kronos BTC-USD 3mo 4h 48
- predict NVDA with kronos for the next 30 days
In any Claude conversation: "run kronos on AAPL" (defaults to 6 months of daily candles, 24-period forecast), "kronos BTC-USD 3mo 4h 48", or "predict NVDA with kronos for the next 30 days" (Claude infers args). You can also call the script directly: python3 ~/.claude/skills/kronos/scripts/run_kronos.py <TICKER> [period] [interval] [pred_len]. Subsequent runs take ~5-15 seconds.
What are this skill's strengths and limitations?
- Wraps a large-scale trained open source foundation model into a one-phrase skill — no ML background needed
- Fully automated first-run setup; fast (~5-15 seconds) on subsequent runs
- Output includes confidence bands and interpretation rules to distinguish signal from noise
- Supports both stocks and crypto with flexible period, interval, and forecast-length parameters
- Honestly scoped as a research tool, with built-in "not financial advice" guardrails and risk-management reminders
- First run downloads ~500MB of weights and heavy deps like torch, taking 3-7 minutes
- Data depends on yfinance (Yahoo Finance); some symbols lack data and intraday candles are limited to the last 60 days
- Repository license is unknown, leaving commercial-use risk unclear
- No test suite; issues like OOM or failed venv installs require manual troubleshooting
- Marketing framing like "Wall Street's billion dollar AI" is unverified — prediction accuracy has no independent benchmark in the source
How does this skill compare with similar options?
Side by side with related skills; every score comes from the same FSRS standard.
| Skill | FS score | Stars | Last updated | License |
|---|---|---|---|---|
| Kronos Technical Analysis Skill this page | 44 · Not recommended | ★ 21 | 5mo ago | — |
| Financial Analyst Skill | 52 · Use with care | ★ 62 | 7mo ago | MIT |
| TradingView MCP AI Trading Intelligence | 50 · Use with care | ★ 5.1k | 11d ago | MIT |
| Pattern Recognition Bot (2.1) | 42 · Not recommended | ★ 2.1k | 2mo ago | AGPL-3.0 |
| Technical TA Engine | 42 · Not recommended | ★ 2.1k | 2mo ago | AGPL-3.0 |
Built on the same author-ecosystem Kronos open source model (shiyu-coder/Kronos, MIT licensed); this skill is essentially a conversational Claude wrapper around that model. The author also recommends combining it with a manually curated Claude Project of company filings as a two-layer stack.
How did FollowSkills review this skill?
Data flow is largely transparent: public OHLCV via yfinance, first run auto-clones GitHub and downloads ~500MB weights from HuggingFace. Positives: no malicious instructions, no credential collection, explicit 'not financial advice'. Deducted for: automatic git clone + pip install + model download on first run with no user confirmation step; no hash verification of third-party model weights; unpinned dependencies.
Docs are self-consistent; parameters, examples and troubleshooting (missing git, no data, failed venv, OOM) are covered, with failure paths explained. Deducted for: the actual script is not present in evidence, so auto-install logic and error feedback cannot be verified; no tests; failure feedback relies on prose only.
Triggers, arguments, examples and 'What this is NOT' boundaries are fairly clear, including the crypto -USD suffix detail. Deducted for: complete dependence on yfinance (Yahoo) and HuggingFace downloads, which are poorly reachable from mainland China networks; no Chinese-language support stated; triggers are English-only.
Docs are reasonably layered (usage/output/troubleshooting in SKILL.md) and README credits the upstream MIT-licensed model. Deducted for: no license metadata for the skill itself, no version/changelog, maintenance responsibility is a personal attribution only, and first-run auto-install carries hidden assumptions.
Claims markdown prediction + confidence interval output that can be presented directly; framing is sensible. Deducted for: static review cannot verify output quality or usability; sample output uses placeholders ($XXX.XX); marginal value over calling Kronos directly is mostly packaging convenience.
Auditable links to the upstream model and data source; sources credited in README. Deducted for: no tests, no CI evidence, no real execution records; marketing claims like 'hedge-fund-grade' are unverifiable; facts and inference are not separated.
Open a dimension to read why it scored that way
Evidence confidence：Low — Mostly static review, author material or a limited demo; useful for discovery, not high-risk decisions.
See the full review method →