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/xspoonai/spoon-bot/researchnpx skills add XSpoonAi/spoon-bot --skill researchgit clone --depth 1 https://github.com/XSpoonAi/spoon-botWrote 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/xspoonai/spoon-bot/research)<a href="https://agentmods.dev/skills/xspoonai/spoon-bot/research"><img src="https://agentmods.dev/badge/skills/xspoonai/spoon-bot/research.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 5.1 | $0.00012 | $0.00488 |
| Opus 5 | $0.00006 | $0.00244 |
| Sonnet 5 | $0.00002 | $0.00098 |
| Haiku 4.5 | $0.00001 | $0.00049 |
Grade A, and why
research 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 5d 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.
The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
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.
- 5d ago First seen · 82 lines · 12 tokens per session scan A a3a59706406c
research is a skill published in the GitHub repository XSpoonAi/spoon-bot (5 stars, last pushed 11d ago), with no licence file. It adds 12 tokens to every session and 488 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 2026-08-31.
Other skills, from other repositories
knowledge-absorber
深度解析链接/文档/代码，生成导师级教学笔记 + Wan 2.7 知识海报。 Use when user asks to: 学习、分析、解读、整理、吸收、读懂 任何文档或代码 Trigger keywords: 学习、分析、知识卡片、知识海报、解读文档、整理笔记、知识库、存入知识库 支持 PDF/Word/Markdown/代码/图片，自动真理锚定验证，国学内容自动水墨风格。 When user provides: URL链接、文件路径、代码文件、图片 → 生成知识卡片 When user says: "生成海报"、"知识海报" → 额外生成 Wan 2.7 信息图.
knowledge-absorber
深度解析链接、文档或代码，生成”全能导师级”的教学笔记（零基础直达精通）。具备”真理锚定”校验能力，自动识别幻觉与过时信息。支持国学风格渲染，自动清理广告和无用内容。.
reading-metaskill
当用户想养成阅读习惯、问「读什么书/怎么读」「如何学习新领域/怎么入门某学科」时调用。 核心理念: 阅读是终极元技能; 读你所爱直到爱上阅读, 没有读完义务; 读原著与经典优先; 以教促学; 每天1-2小时即可进入极少数人行列。 不适用于: 具体某本书的书评、考试备考资料选择。 Triggers: 阅读/读书/怎么学习/入门/原著/书单/reading/how to learn.
coach
Learning telemetry, strategy, and schedule — retention stats, calibration, grader audit, n-of-1 experiments, HTML dashboard. Use for "how am I doing", weekly check-ins, strategy questions, auditing the grader, or adjusting how Engram teaches.
textbook-distillation
Turn a textbook or long-form source into a self-paced learning track: intake the material, build a chapter map, draft a lesson plan, then generate self-contained HTML lecture notes in a style the human specifies (layout, palette, emphasis), each lesson carrying worked examples, exercises, and checkpoint questions.…
classify-interview-questions
将批量面经或零散面试题逐题去重并分发：Agent/LLM/AI工程题写入 zero2Agent 的 learn-agent-interview，传统后端八股写入相邻 zero2Leetcode 的夏季八股。大批量输入使用 gpt-5.6-luna API 逐篇并发抽题和语义召回，再审查、去重和写答案；不新建面经实录文章。.