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 skills add Shy2593666979/AgentChat --skill research-reportgit clone --depth 1 https://github.com/Shy2593666979/AgentChatWrote 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/shy2593666979/agentchat/research-report)<a href="https://agentmods.dev/skills/shy2593666979/agentchat/research-report"><img src="https://agentmods.dev/badge/skills/shy2593666979/agentchat/research-report/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/shy2593666979/agentchat/research-report"><img src="https://agentmods.dev/badge/skills/shy2593666979/agentchat/research-report.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00035 | $0.00362 |
| Opus 5.5 | $0.00014 | $0.00145 |
| Sonnet 5.5 | $0.00007 | $0.00072 |
| Haiku 4.5 | $0.00003 | $0.00036 |
Grade A, and why
research-report 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 yesterday.
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
研究报告
用于需要明确研究问题、比较资料并形成有依据结论的任务。直接解释一个概念时，不必套用完整报告。
工作方法
- 从请求中提取研究问题、读者、时间范围与比较维度。缺少决定结论的关键信息时，只询问必要内容。
- 将提供的材料按问题归类，记录作者或机构、日期、来源链接或材料编号。来源内容相互矛盾时，保留分歧并说明可能原因。
- 分别标记材料中的事实、作者观点和自己的推断。引用必须能定位到实际提供的材料；没有材料支持的说法不能写成确定结论。
- 围绕研究问题组织分析，解释各结论的依据、适用范围及不确定性。对比方案时使用一致维度。
- 给出能够回答原问题的结论，并列出仍需补充的证据。
组织报告时读取 /research-report/reference/output-template.md；交付前读取 /research-report/reference/review-checklist.md。
能力边界
当前可以读取技能文件与用户在对话中提供的资料。没有联网检索工具时，不声称检索过网页；用户只给链接而未提供正文时，说明该来源尚未核实。需要最新信息时，让主智能体补充检索材料。不要编造文献、数据或引文。
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- yesterday First seen · 23 lines · 35 tokens per session scan A b57117206908
research-report is a skill published in the GitHub repository Shy2593666979/AgentChat (921 stars, last pushed yesterday), licensed MIT. It adds 35 tokens to every session and 362 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-10-18.
Other skills, from other repositories
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reward-design
A guide to defining the scores that an AI agent receives during trial-and-error training. It covers checks based on rules, human preferences, or another model, and distinguishes rewarding the final result from rewarding the steps taken.
agent-evolution
A guide to making an AI agent improve from its past runs. It covers how to judge results, extract reusable lessons, and decide whether to store them as knowledge, instructions, code, or model training.
bad-case-to-dpo
A guide to turning failed production agent runs into training and evaluation data. It explains how to find the first mistake, create preference examples for DPO (Direct Preference Optimization), and check that a fix does not damage other tasks.
knowledge-org
A guide to organising large bodies of knowledge so an AI system can navigate relationships, summaries, and layers instead of searching only flat text chunks. It covers tree indexes, knowledge graphs, file-like structures, incremental updates, and long-term memory layouts.
memory-system
A guide to giving an AI agent persistent, user-specific memory across conversations. It covers what to remember, how to store it, how to update or compress it, and when to use memory instead of a shared knowledge base.