No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
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🧮 tokentally — One tiny lib for LLM token + cost math Token usage in, dollar totals out. Small TypeScript library for: Normalizing token usage across providers Resolving per-token pricing (static maps, LiteLLM catalog, OpenRouter catalog) Estimating and aggregating USD cost Install Core usage (browser-safe) Node helpers (catalog sources) LiteLLM pricing + limits OpenRouter pricing (optional) ts import { fetchOpenRouterPricingMap, resolvePricingFromMap } from "tokentally/node"; const map = await fetchOpenRouterPricingMap({ apiKey: process.env.OPENROUTERAPIKEY!, fetchImpl: fetch, }); const pricing = resolvePricingFromMap(map, "openai/gpt-5.2
| Stars | 91 |
| Forks | 14 |
| Language | TypeScript |
| Category | AI Tool |
| License | MIT |
| Quality Score | 65.8482315042719/100 |
| Open Issues | 1 |
| Last Updated | 2026-10-02 |
| Created | 2025-12-19 |
| Platforms | node |
| Est. Tokens | ~14k |
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tokentally is One tiny lib for LLM token + cost math. It is categorized as a AI Tool with 91 GitHub stars.
tokentally is primarily written in TypeScript. It covers topics such as cost, token, typescript.
You can find installation instructions and usage details in the tokentally GitHub repository at github.com/steipete/tokentally. The project has 91 stars and 14 forks, indicating an active community.
tokentally is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to tokentally on Agent Skills Hub include mcp-server-simulator-ios-idb, langchainjs-mcp-adapters, openai-auto-register. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: