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 →
by pleasedodisturb · LLM Plugin · ★ 72
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is awesome-llm-token-optimization safe to install? View the security audit →
Awesome LLM Token Optimization A curated list of strategies, tools, papers, and resources for reducing LLM token costs and improving efficiency in production. Building with LLMs is expensive. An agent processing 10 reasoning steps can consume 50K-100K tokens per task. This list collects everything you need to cut costs by 80-99% without sacrificing quality. Contents Quick Wins Prompt Caching Batch APIs Model Routing Prompt Compression Context Window Management KV Cache Optimization Browser Tool Efficiency Cost Tracking Tools Pricing Comparison Prompt Engineering for Efficiency Comprehensive Guides Academic Papers Community Resources Quick Wins The highest-impact strategies ranked by effort-to-savings ratio: [Anthropic](https://docs.anthropic.com/en/docs/agents-and
| Stars | 72 |
| Forks | 24 |
| Language | Shell |
| Category | LLM Plugin |
| Quality Score | 56.4446008988646/100 |
| Open Issues | 1 |
| Last Updated | 2026-09-14 |
| Created | 2026-04-15 |
| Platforms | claude-code, cli |
| Est. Tokens | ~18k |
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awesome-llm-token-optimization is A curated list of strategies, tools, papers, and resources for reducing LLM token costs and improving efficiency in production.. It is categorized as a LLM Plugin with 72 GitHub stars.
awesome-llm-token-optimization is primarily written in Shell. It covers topics such as ai, awesome, awesome-list.
You can find installation instructions and usage details in the awesome-llm-token-optimization GitHub repository at github.com/pleasedodisturb/awesome-llm-token-optimization. The project has 72 stars and 24 forks, indicating an active community.
The top alternatives to awesome-llm-token-optimization on Agent Skills Hub include llmtrim, NadirClaw, mcp-linker. 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: