Clayton Young
a83dbf3f16
Replace AGENTS.md / claude-skill/ with a proper agentskills.io-compliant
skill directory. Any AI agent that supports the open Agent Skills standard
(Claude Code, OpenCode, Cursor, Codex, etc.) can now install and use this
skill generically.
Changes:
- Remove AGENTS.md (was Claude-specific convention)
- Remove claude-skill/ directory (was Claude-specific naming)
- Add timesfm-forecasting/SKILL.md with compliant frontmatter:
name: timesfm-forecasting
description: ...
license: Apache-2.0
metadata: author, version
- Rename claude-skill/examples/ → timesfm-forecasting/examples/
- Rename claude-skill/scripts/ → timesfm-forecasting/scripts/
- Rename claude-skill/references/ → timesfm-forecasting/references/
- Update .gitattributes paths to match new directory
Skill installs via:
cp -r timesfm-forecasting/ ~/.claude/skills/
cp -r timesfm-forecasting/ ~/.cursor/skills/
# or any agent that supports agentskills.io
Spec: https://agentskills.io/specification
188 lines
4.4 KiB
JSON
188 lines
4.4 KiB
JSON
{
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"model": "TimesFM 1.0 (200M) PyTorch",
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"input": {
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"source": "NOAA GISTEMP Global Temperature Anomaly",
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"n_observations": 36,
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"date_range": "2022-01 to 2024-12",
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"mean_anomaly_c": 1.09
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},
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"forecast": {
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"horizon": 12,
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"dates": [
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"2025-01",
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"2025-02",
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"2025-03",
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"2025-04",
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"2025-05",
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"2025-06",
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"2025-07",
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"2025-08",
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"2025-09",
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"2025-10",
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"2025-11",
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"2025-12"
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],
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"point": [
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1.25933837890625,
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1.285666823387146,
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1.2950127124786377,
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1.2207623720169067,
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1.170255422592163,
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1.1455552577972412,
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1.1702347993850708,
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1.2026824951171875,
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1.1909748315811157,
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1.1490840911865234,
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1.080478549003601,
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1.0613453388214111
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],
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"quantiles": {
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"10%": [
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1.2481880187988281,
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1.2773758172988892,
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1.286991834640503,
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1.2084007263183594,
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1.1533130407333374,
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1.1275498867034912,
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1.1510555744171143,
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1.1859495639801025,
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1.0624356269836426,
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1.036609172821045
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],
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"20%": [
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1.1407020092010498,
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1.1406043767929077,
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1.0352504253387451,
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0.9503718018531799,
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0.9079477190971375,
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0.8361266255378723,
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],
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"30%": [
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1.1880751848220825,
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1.1960833072662354,
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1.187617301940918,
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1.104191780090332,
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1.0594383478164673,
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0.9952926635742188,
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0.9259791970252991,
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"40%": [
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1.1036059856414795,
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1.0548235177993774,
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0.9882403016090393,
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0.9593706727027893
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],
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"50%": [
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1.2394564151763916,
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1.2593891620635986,
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1.267505168914795,
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1.1853008270263672,
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1.127617597579956,
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1.128767728805542,
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1.1579902172088623,
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1.1511956453323364,
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1.1052223443984985,
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1.03863525390625,
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1.0152238607406616
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],
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"60%": [
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1.25933837890625,
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1.285666823387146,
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1.2950127124786377,
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1.2207623720169067,
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1.170255422592163,
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1.1455552577972412,
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1.1702347993850708,
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1.2026824951171875,
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1.1909748315811157,
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1.1490840911865234,
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1.080478549003601,
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1.0613453388214111
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],
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"70%": [
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1.27677321434021,
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1.3110136985778809,
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1.3284480571746826,
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1.2566629648208618,
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1.2019660472869873,
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1.2114834785461426,
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1.2399210929870605,
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1.1897773742675781,
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1.1281580924987793,
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1.1032856702804565
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],
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"80%": [
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1.2971320152282715,
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1.3400218486785889,
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1.3547290563583374,
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1.2898554801940918,
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1.2390310764312744,
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1.2180578708648682,
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1.248227596282959,
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1.2842004299163818,
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1.2832940816879272,
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1.240414023399353,
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1.175971508026123,
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1.153149962425232
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],
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"90%": [
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1.3239599466323853,
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1.3751201629638672,
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1.403548240661621,
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1.3310348987579346,
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1.2891905307769775,
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1.2702757120132446,
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1.2997852563858032,
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1.3408125638961792,
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1.3354730606079102,
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1.286876916885376,
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1.2283769845962524,
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1.2169079780578613
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],
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"99%": [
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1.3678879737854004,
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1.4253658056259155,
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1.4642648696899414,
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1.40165376663208,
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1.3632389307022095,
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1.3453660011291504,
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1.380732536315918,
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1.4195259809494019,
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1.416972041130066,
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1.3775466680526733,
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1.3122477531433105,
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1.2959520816802979
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]
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}
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},
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"summary": {
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"forecast_mean_c": 1.186,
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"forecast_max_c": 1.295,
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"forecast_min_c": 1.061,
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"vs_last_year_mean": -0.067
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}
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} |