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 knowledge-organizinggit 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/knowledge-organizing)<a href="https://agentmods.dev/skills/shy2593666979/agentchat/knowledge-organizing"><img src="https://agentmods.dev/badge/skills/shy2593666979/agentchat/knowledge-organizing/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/knowledge-organizing"><img src="https://agentmods.dev/badge/skills/shy2593666979/agentchat/knowledge-organizing.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 | $0.00041 | $0.00306 |
| Opus 5.5 | $0.00016 | $0.00122 |
| Sonnet 5.5 | $0.00008 | $0.00061 |
| Haiku 4.5 | $0.00004 | $0.00031 |
Grade A, and why
knowledge-organizing 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
知识整理
根据材料用途选择产物：概念用知识卡片，重复问题用常见问题，操作经验用指南，选择过程用决策记录。
工作方法
- 确认材料实际覆盖的主题、读者与用途。先抽取核心概念和信息层级，避免按原文顺序机械缩写。
- 合并重复表达，保留相互矛盾的观点、适用条件与重要例外。区分材料中的事实、讨论建议和已经确认的决定。
- 把内容转成适合检索的短标题与小节，补充相关条目之间的联系。
- 记录来源位置。行动项只填写材料明确给出的负责人和时间，未明确的内容标为待确认。
- 检查整理后是否改变原意、丢失前提或把讨论写成结论。
格式选择见 /knowledge-organizing/reference/output-template.md；复核时读取 /knowledge-organizing/reference/review-checklist.md。
当前输出 Markdown 内容，不自动写入 Notion 或其他知识库。只引用已提供的材料，不访问外部系统补写事实。
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 · 21 lines · 41 tokens per session scan A 4ea6bf74d1b5
knowledge-organizing is a skill published in the GitHub repository Shy2593666979/AgentChat (921 stars, last pushed yesterday), licensed MIT. It adds 41 tokens to every session and 306 once invoked, about $0.0002 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
agent-state-bar
A design guide for an agent status bar: a short, automatically updated summary of progress, remaining tasks, tool counts, errors, and system state placed near the latest model input.
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.
context-compression
A guide to shortening the information an agent carries between steps. It explains how to replace large tool outputs and old conversation details with compact, useful summaries while preserving important decisions and constraints.
aatmf-t04-memory-manipulation
AATMF T4 — Multi-Turn & Memory Manipulation. Persistent memory injection, conversation-state poisoning, cross-session contamination, ghost-context leak.
openlore
Query and publish to an OpenLore knowledge repository over SSH using ordinary shell commands. Use when a task needs project documentation, runbooks, shared team knowledge, or a place to publish findings.