awesome-ai-tokenomics

Name: awesome-ai-tokenomics
Author: QuesmaOrg

Pending

A map of what AI tokens actually cost, and where they're wasted vs. well spent. Tools, research, practices, and copy-paste setups for token-efficient agentic coding.

52stars

4forks

Python

Installation

# Add to your Claude Code skills
git clone https://github.com/QuesmaOrg/awesome-ai-tokenomics

Getting Started

Guides for using ai agents skills like awesome-ai-tokenomics.

Caveman: Cut Claude Token Use by 65%
How agent-side prompt compression works, when to use it, and when not to.
What is an AI Skills Marketplace?
Definitions, how marketplaces work, and how to choose between them in 2026.
Getting Started with AI Skills

README.md

Frequently Asked Questions

What is awesome-ai-tokenomics?

awesome-ai-tokenomics is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by QuesmaOrg. A map of what AI tokens actually cost, and where they're wasted vs. well spent. Tools, research, practices, and copy-paste setups for token-efficient agentic coding. It has 52 GitHub stars.

Is awesome-ai-tokenomics safe to use?

awesome-ai-tokenomics's catalog security scan is still queued. You can run an instant dependency and prompt-injection check now with the "Scan for vulnerabilities" button above.

How do I install awesome-ai-tokenomics?

Clone the repository with "git clone https://github.com/QuesmaOrg/awesome-ai-tokenomics" and add it to your Claude Code skills directory (see the Installation section above).

What programming language is awesome-ai-tokenomics written in?

awesome-ai-tokenomics is primarily written in Python. It is open-source under QuesmaOrg on GitHub, so you can review or fork the full source.

Are there alternatives to awesome-ai-tokenomics?

Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh awesome-ai-tokenomics against similar tools.

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Awesome AI Tokenomics

A map of what AI tokens actually cost, and where they're wasted vs. well spent.

"I feel nervous when I have subscription left over. That just means I haven't maximized my token throughput." Andrej Karpathy, No Priors (2026)

As of 2026-07: 197 entries across five areas, 8 practices, 10 concepts, 8 claims, and 7 copy-paste setups.

Topics: Caching · Compression · Context engineering · Memory · Routing · Multi-agent systems · Gateways · Observability · Benchmarks · Cache accounting · Budgets · Pricing models · Energy

A hybrid by design: a curated awesome list at the core, plus a field-guide layer on top (practices, concepts, claims) that carries an editorial point of view.

Where to start
Legend
Monitor
Optimize
Govern
Understand
Measure
Practices
Concepts
Claims
Setups and skills

Where to start

Just want the numbers: the five area sections below hold every entry. Want the method: read the practices first, then the concepts behind them. Building something: setups and skills holds runnable configurations.

Legend

Each entry ends with a kind badge: (blue, with the license when known), or a gray badge for , , , for companies, and . Plain entries are articles. GitHub-hosted tools also carry live star and last-commit badges.

Monitor

Dashboards

ccusage - An open-source CLI that reads local agent logs to report token usage and cost across 15 coding-agent sources, with caching-aware pricing.
Claude Code Usage Monitor - A live terminal dashboard for Claude Code usage, with burn-rate analytics, P90 limit detection, and session-expiry forecasts.
claude-usage - A local dashboard for Claude Code token usage, costs, and session history; Pro and Max subscribers get a quota progress bar.
ClaudeBar - A macOS menu-bar app that monitors AI coding quotas across 11 providers; the README declares MIT but ships no license file, so the OSS grant is unconfirmed.
CodeBurn - An open-source tracker for 36 coding tools whose optimize command flags named harness-waste patterns with dollar estimates it later checks against actuals.
Codex Usage Tracker - A local-first dashboard, CLI, and MCP tools indexing Codex CLI logs into SQLite to show where tokens, credits, and cost go, including cache ratios.
CodexBar - A free, open-source macOS menu-bar app that shows limits and reset timers at a glance across dozens of AI providers, plus credit balances and spending when…
CodeZeno Usage Monitor - A Windows-taskbar widget showing real-time Claude Code quota and usage at a glance, without opening a terminal.
Datadog LLM Observability - Cost - Datadog's LLM Observability estimates per-request cost across 800+ models from token counts and public pricing; invoice reconciliation is a separate product.
gh-aw (GitHub Agentic Workflows) - GitHub's agentic-workflows runtime with first-party per-run token and cost metering, plus budget caps that stop a workflow mid-run.
Grafana Cloud GenAI Observability - Grafana Cloud's GenAI Observability ships a prebuilt dashboard for LLM cost, token usage, and latency, built on top of the OpenLIT SDK.
OpenLIT - An open-source (Apache-2.0), OpenTelemetry-native platform with a self-hosted dashboard for LLM cost, token, and latency observability.
OpenUsage - A native Swift macOS menu-bar meter for 10 AI coding subscriptions, showing session and weekly limits, credits, and estimated spend from local credentials.
TokenTracker - A local-first token and cost dashboard for 27 coding tools, with a desktop pet, native widgets, and achievements as a distinct gamified take on usage metering.

Ebpf Kernel Capture

AgentSight - Uses eBPF to watch an AI agent from the kernel boundary, correlating what it said it would do against what it did, at under 3% overhead.
OpenTelemetry eBPF Instrumentation (OBI) - GenAI / MCP - OBI is OpenTelemetry's zero-code eBPF instrumentation (formerly Grafana Beyla) that captures GenAI and MCP traces at the kernel layer with no SDK.

Observability

Langfuse - An open-source platform for tracing, evaluating, and analyzing LLM and agent transcripts, with a prompt-management layer on top.