# tokencast

> Use this tool when you need to estimate the computational costs of large language model (LLM) workflows before execution, to optimize resource allocation and reduce unexpected expenses. Tokencast solves the problem of uncertain cost estimation by providing pre-execution cost estimates with calibration learning. It takes in workflow specifications as input and outputs estimated costs, making it ideal for use cases where cost predictability is crucial.

Canonical page: https://skillsregistry.net/skills/io-github-krulewis-tokencast  
JSON: https://api.skillsregistry.net/v1/skills/io-github-krulewis-tokencast

## Description

Pre-execution cost estimation for LLM agent workflows with calibration learning

## Trust

- **Trust score (0–1):** 0.59
- **Verification tier:** scanned
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 0.1.1
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-09-28

## Source

- **Source listing:** [MCP Registry](https://registry.modelcontextprotocol.io/v0/servers/io.github.krulewis%2Ftokencast)
- **Repository:** <https://github.com/krulewis/tokencast>

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "io-github-krulewis-tokencast"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/io-github-krulewis-tokencast` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/io-github-krulewis-tokencast/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt