Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/aayushostwal/nexus/token-optimizernpx skills add aayushostwal/nexus --skill token-optimizergit clone --depth 1 https://github.com/aayushostwal/nexusWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/aayushostwal/nexus/token-optimizer)<a href="https://agentmods.dev/skills/aayushostwal/nexus/token-optimizer"><img src="https://agentmods.dev/badge/skills/aayushostwal/nexus/token-optimizer.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00015 | $0.00159 |
| Opus 5 | $0.00008 | $0.00079 |
| Sonnet 5 | $0.00003 | $0.00032 |
| Haiku 4.5 | $0.00002 | $0.00016 |
Grade A, and why
token-optimizer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Token Optimization Rules
Context gathering
- Run Grep or Glob before Read — never load full files to find a symbol
- Read only the relevant lines using view_range, not entire files
- Cache findings in your reasoning; don't re-read files you already saw
Responses
- Output results directly — no preamble, no restating the task
- Skip end-of-turn summaries unless explicitly asked
- Prefer code over prose for technical answers
Tool calls
- Batch related reads into one turn where possible
- Prefer Grep over Bash grep for in-repo searches
- Never run a tool call just to confirm something already in context
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 22 lines · 15 tokens per session scan A 4a6ad700b36d
token-optimizer is a skill published in the GitHub repository aayushostwal/nexus (18 stars, last pushed 24d ago), licensed MIT. It adds 15 tokens to every session and 159 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
fireworks-tech-graph
Create technical diagrams such as software architecture, data flow, flowcharts, sequence diagrams, C4 reviews, cloud deployments, event streams, observability investigations, agent/memory systems, UML, ER, network topology, timelines, and technical concept maps, then export SVG, PNG, focused semantic SVG-to-GIF…
decision-heuristics
当用户在重大选择上纠结（换工作/买房/搬城/合伙/结婚）、列了利弊表还是拿不定主意时调用。 核心理念: 无法决定就答否; 三个重大决定(住哪/和谁/做什么)值得花一两年; 两个均等选择选短期更痛苦的路。 不适用于: 日常琐碎选择（晚饭吃什么）、信息查询。 Triggers: 纠结/拿不定主意/要不要/该不该/利弊/decision/hesitate/should I.
screen-detox
当用户刷手机/短视频/社交媒体上瘾、感觉空虚、想戒断多巴胺零食时调用。 核心理念: 所有屏幕活动与更少幸福相关(作者断言无例外); 屏幕=用长期后果换短期快感的多巴胺零食; 用习惯替换五步戒断。 不适用于: 工作需要屏幕的职业场景(区分工作屏幕与消费屏幕)。 Triggers: 刷手机/上瘾/短视频/社交媒体/多巴胺/戒断/屏幕时间/screen time/dopamine/addiction.
wealth-structure
当用户问「怎么赚钱/如何致富/该不该要期权/如何判断一个生意的结构」时调用。 核心理念: 财富=睡觉时仍在赚钱的资产; 靠「责任(押上声誉)→产权(股权/知识产权)→杠杆(无需许可的代码媒体)→避免出局」构建, 无法靠出租时间致富。 不适用于: 具体投资标的分析、理财产品的选股建议。 Triggers: 致富/赚钱/股权/期权/杠杆/被动收入/ownership/leverage/equity.
acceptance
当用户困在无法改变的处境（坏关系/讨厌的同事/不如意的事）里反复内耗、问「该忍还是该走」时调用。 核心理念: 面对任何情境只有三选项——改变它/接受它/离开它; 三者都不做是痛苦之源; 接受=快速适应, 用「积极方面是什么」重释训练。 不适用于: 可以改变的处境(应先改变), 以及涉及违法/人身安全的场景。 Triggers: 接受/忍/内耗/放不下/该走该留/acceptance/let go/change-accept-leave.
happiness-skill
当用户问「怎么才能更幸福/为什么得到了还不满足/怎么减少焦虑」时调用。 核心理念: 幸福是缺憾感清空的默认状态, 是可训练的技能; 欲望是与自己的契约(得到前不快乐), 同时只留一个重大欲望; 活在当下。 不适用于: 临床抑郁等需要专业治疗的场景(本书方法不能替代医疗)。 Triggers: 幸福/不快乐/欲望/焦虑/知足/活在当下/happiness/desire/anxiety.