awesome-ai-tokenomics: A curated list on AI token economics: what tokens cost, where they get wasted, and how to cut the bill

by QuesmaOrg · Agent Tool · ★ 204

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Security audit verdict: SAFE · quality 58/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

agentic-codingaiai-agentsawesomeawesome-listclaude-codecost-optimizationcost-trackingfinopsllm

Quick Facts

Stars204
Forks37
LanguagePython
CategoryAgent Tool
LicenseCC0-1.0
Quality Score58/100
Open Issues1
Last Updated2026-10-20
Created2026-07-17
Platformsclaude-code, python
Est. Tokens~25k

About awesome-ai-tokenomics

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: 172 entries across five areas, 8 practices, 10 concepts, 8 claims, and 7 copy-paste setups.

Compatible Skills

These tools work well together with awesome-ai-tokenomics for enhanced workflows:

  • awesome-ai-tools — semantic(0.25)+complementary+same_lang+similar_pop+shared_platform (59%)
  • awesome-ai-plugins — semantic(0.24)+complementary+same_lang+similar_pop+shared_platform (59%)
  • awesome-legal-ai-zh — semantic(0.23)+complementary+same_lang+similar_pop+shared_platform (58%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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:

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