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Subagent

Cast

Builds time series forecasting models for demand, revenue, and usage signals. Use when you need demand prediction, trend analysis, or seasonal decomposition. Trigger with \"forecast this time series\", \"build a demand model\".

Type
Subagent
GitHub stars
2.8k
License
MIT
Repo last updated
Sep 27, 2026
Model
sonnet
Version
1.0.0
Author
Jeremy Longshore <[email protected]>

What Cast is

Cast is a subagent published in the jeremylongshore/tons-of-skills-marketplace repository on GitHub, which has about 2.8k stars. The repository describes itself as: “Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com.”

A subagent is a specialist assistant that Claude can hand part of a task to. It is a markdown file whose frontmatter sets a name, a description that tells Claude when to delegate, and optionally the tools and model it may use; the body becomes the subagent's own system prompt.

Because a subagent works in its own context, it keeps the main conversation focused: Claude can send a narrow job, such as a review or a specialised analysis, to Cast and get back a compact result.

How to install Cast

Claude Code

  1. Download cast.md from the repository.
  2. Save it to ~/.claude/agents/ to use it in every project, or to .claude/agents/ inside one project to share it through version control.
  3. Claude Code watches these folders, so the subagent is usually available right away. Ask Claude to use it by name, or @-mention it to make sure it runs.

Claude Cowork

  1. Cowork loads subagents through plugins. If the repository is packaged as a plugin marketplace, add it under Customize → Plugins → Add marketplace and install the plugin that contains this subagent.
  2. Otherwise, bundle the file into your own plugin's agents/ folder and upload it from Customize → Plugins.

New to extending Cowork? Our plugins guide and Customize guide explain how skills, plugins, and connectors fit together.

Inside the source file

An excerpt from plugins/ai-agency/tonone/agents/cast.md, shared under the repository's MIT license. Read the full file on GitHub.

You are Cast — Forecasting Engineer on the Data Science Team. Builds forecasting models for demand, revenue, usage, and any time-varying signal.

Think in data, experiments, and statistical rigor. Every claim needs a number. Every model needs a baseline. Every experiment needs a power analysis.

Communication

Respond terse. All technical substance stays — only filler dies. Follow output-kit protocol: compressed prose, no filler, fragments OK. Documents: normal prose. See docs/output-kit.md for CLI skeleton, severity indicators, 40-line rule.

Operating Principle

Every forecast has a confidence interval — a point estimate alone is a lie. Forecasting is iterative: baseline (naive/seasonal), then classical (ARIMA/ETS), then ML (LightGBM/Prophet), then deep learning (N-BEATS) only when data volume justifies it. More complexity rarely beats a well-tuned simple model.

What you skip: Real-time streaming predictions — that's Cortex/Drift territory.

What you never skip: Never report a forecast without confidence intervals. Never skip baseline comparison. Never use a complex model without validating it beats naive seasonal.

Scope

Owns: Time series forecasting, demand prediction, trend analysis, seasonal decomposition

Skills

  • Cast Forecast: Build a forecasting model for a time series — demand, revenue, or usage prediction.
  • Cast Validate: Validate and benchmark a forecasting model — walk-forward CV, error metrics, baseline comparison.
  • Cast Recon: Survey existing forecasting code or models in a codebase — find gaps, stale models, and missing validation.

Key Rules

  • Baseline first: seasonal naive beats 80% of ML models on short horizons
  • Cross-validation: time-series CV (walk-forward), never random split
  • Metrics: MAPE for symmetric, RMSE for large-error sensitivity, sMAPE for zero-values
  • Decompose first: trend + seasonality + residual before modeling
  • Prophet for business forecasting with holidays; N-BEATS for pure ML accuracy

Process Disciplines

When performing Cast work, follow these superpowers process skills:

Iron rule: No completion claims without fresh verification.

Before you install

  • Read the whole file first. Skills, commands, and subagents are instructions Claude will follow, so make sure they match what you want.
  • Check which tools, scripts, or MCP servers it uses. Local servers and scripts run with your permissions.
  • Try it in a test project or a copy of your files before pointing it at real work.
  • Pin the version you tested, and review changes before updating.
  • Watch for instructions that fetch web content or run shell commands; those are where prompt injection risks start. See our prompt injection guide.

FAQ

What is Cast?

Cast is a subagent for Claude Code and Claude Cowork from the jeremylongshore/tons-of-skills-marketplace repository on GitHub. Builds time series forecasting models for demand, revenue, and usage signals. Use when you need demand prediction, trend analysis, or seasonal decomposition. Trigger with \"forecast this time series\", \"build a demand model\".

How do I install Cast in Claude Code?

Download cast.md from the repository. Save it to ~/.claude/agents/ to use it in every project, or to .claude/agents/ inside one project to share it through version control. Claude Code watches these folders, so the subagent is usually available right away. Ask Claude to use it by name, or @-mention it to make sure it runs.

Can I use Cast in Claude Cowork?

Cowork loads subagents through plugins. If the repository is packaged as a plugin marketplace, add it under Customize → Plugins → Add marketplace and install the plugin that contains this subagent. Otherwise, bundle the file into your own plugin's agents/ folder and upload it from Customize → Plugins.

Is Cast safe to install?

It is a third-party community resource, not reviewed by Anthropic or this site. Read the source file first, check which tools and connectors it uses, and install only from sources you trust.

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Listing data comes from the public GitHub repository and was last checked in September 2026. Excerpts are © their authors and shared under MIT. This directory is independent and not affiliated with Anthropic or the resource's authors.