TokenEconomizer: Experience-Driven Black-Box Cost Control for Coding Agents

by microsoft · Codex Skill · ★ 4.8k

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

How to install TokenEconomizer

git clone https://github.com/microsoft/TokenEconomizer.git
claude plugin install tokeneconomizer@tokeneconomizer

Taken from the project's README on GitHub. Check the security audit before running it.

Security audit verdict: SAFE · quality 66/100

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-aiagentic-codingai-codingai-coding-assistantanthropicartificial-intelligenceclaude-codecodexcoding-agentcoding-agents

Quick Facts

Stars4,796
Forks289
LanguagePython
CategoryCodex Skill
LicenseApache-2.0
Quality Score66/100
Open Issues11
Last Updated2026-11-04
Created2026-10-13
Platformsclaude-code, codex, python
Est. Tokens~18k

About TokenEconomizer

TokenEconomizer: Experience-Driven Black-Box Cost Control for Coding Agents Aaron J. Li1, Weijia Xu2, Chandan Singh2, Zixiao Chen3, Jeevana Priya Inala2, Charlotte Siska3, Acar Tamersoy3, Bin Yu1, Ion Stoica1, Michel Galley2, Jianfeng Gao2 1UC Berkeley   2Microsoft Research   3Microsoft Security Our measurement on SWE-Bench tasks: 18.4–30.9% lower mean cost across five agent–model...

Compatible Skills

These tools work well together with TokenEconomizer for enhanced workflows:

  • Fuxi — semantic(0.28)+complementary+same_lang+similar_pop+shared_platform (60%)
  • claude-code-ultimate-guide — semantic(0.26)+complementary+same_lang+similar_pop+shared_platform (59%)
  • entroly — semantic(0.26)+complementary+same_lang+similar_pop+shared_platform (59%)
  • Gito — semantic(0.24)+complementary+same_lang+similar_pop+shared_platform (58%)
  • CoreCoder — semantic(0.24)+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:

View on GitHub → Browse Codex Skill tools