Testing Core Processors
mastra-ai/mastra
A skill your agent uses when writing or debugging integration tests for error processors in packages/core/src/processors/.
“对大量原始聊天记录进行结构化整理，进行知识提取或生成任何主题分析文档”
Apache-2.0Auto-check passed
Install Long Chat Task Processor
$ npx skills add cafe3310/public-agent-skills --skill long-chat-task-processor -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install cafe3310/public-agent-skills long-chat-task-processor --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/cafe3310/public-agent-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills_parked/long-chat-task-processor .claude/skills/long-chat-task-processor && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "long-chat-task-processor" agent skill from https://github.com/cafe3310/public-agent-skills/tree/main/skills_parked/long-chat-task-processor into .claude/skills/long-chat-task-processor/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "long-chat-task-processor", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/cafe3310/public-agent-skills/tree/main/skills_parked/long-chat-task-processorType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add cafe3310/public-agent-skills --skill long-chat-task-processor -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install cafe3310/public-agent-skills long-chat-task-processor --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/cafe3310/public-agent-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills_parked/long-chat-task-processor .agents/skills/long-chat-task-processor && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "long-chat-task-processor" agent skill from https://github.com/cafe3310/public-agent-skills/tree/main/skills_parked/long-chat-task-processor into .agents/skills/long-chat-task-processor/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "long-chat-task-processor", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add cafe3310/public-agent-skills --skill long-chat-task-processor -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install cafe3310/public-agent-skills long-chat-task-processor --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/cafe3310/public-agent-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills_parked/long-chat-task-processor .cursor/skills/long-chat-task-processor && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "long-chat-task-processor" agent skill from https://github.com/cafe3310/public-agent-skills/tree/main/skills_parked/long-chat-task-processor into .cursor/skills/long-chat-task-processor/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "long-chat-task-processor", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/cafe3310/public-agent-skills.git --path skills_parked/long-chat-task-processor--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add cafe3310/public-agent-skills --skill long-chat-task-processor -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install cafe3310/public-agent-skills long-chat-task-processor --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/cafe3310/public-agent-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills_parked/long-chat-task-processor .gemini/skills/long-chat-task-processor && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "long-chat-task-processor" agent skill from https://github.com/cafe3310/public-agent-skills/tree/main/skills_parked/long-chat-task-processor into .gemini/skills/long-chat-task-processor/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "long-chat-task-processor", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install cafe3310/public-agent-skills long-chat-task-processorInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add cafe3310/public-agent-skills --skill long-chat-task-processor -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/cafe3310/public-agent-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills_parked/long-chat-task-processor .github/skills/long-chat-task-processor && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "long-chat-task-processor" agent skill from https://github.com/cafe3310/public-agent-skills/tree/main/skills_parked/long-chat-task-processor into .github/skills/long-chat-task-processor/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "long-chat-task-processor", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add cafe3310/public-agent-skills --skill long-chat-task-processor -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install cafe3310/public-agent-skills long-chat-task-processor --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/cafe3310/public-agent-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills_parked/long-chat-task-processor .opencode/skills/long-chat-task-processor && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "long-chat-task-processor" agent skill from https://github.com/cafe3310/public-agent-skills/tree/main/skills_parked/long-chat-task-processor into .opencode/skills/long-chat-task-processor/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "long-chat-task-processor", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
long-chat-task-processor- GitHub stars
- 255
- Token cost
- ~884 tokens
- SKILL.md length
- 204 words
- Files
- 3 (incl. scripts, references)
- Skills in repo
- 29
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
- Works in 3 steps: 准备阶段 (Initialization) → TOC 分段处理循环 (TOC Loop) → 整合与交付 (Synthesis)
- SKILL.md covers 使用时机, 工作流, 关键原则 and 注意事项, plus 1 more section
- Runs Python scripts from its folder
About this skill
Long Chat Task Processor is a skill in cafe3310/public-agent-skills (255 stars). Its SKILL.md is about 884 tokens, with 2 other files in the folder (scripts, references). Licence: Apache-2.0.
Workflow steps
3 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 6c45501. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 1 file in scripts/ (Python), which the agent can run.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Long Chat Task Processor loads about 884 tokens when it runs, and up to ~1.4k if it reads all its reference files. Until then it costs about 15 tokens; SKILL.md has 204 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
SKILL.md
The full file from cafe3310/public-agent-skills at commit 6c45501, republished under its Apache-2.0 licence (© cafe3310). 204 words, ~884 tokens.
Download SKILL.mdSave it as
.claude/skills/long-chat-task-processor/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.- name
- long-chat-task-processor
- description
- 对大量原始聊天记录进行结构化整理，进行知识提取或生成任何主题分析文档
- license
- Apache-2.0
- author
- github/cafe3310
- depends_on_binary
- python3
聊天记录项目化处理工作流 (Long Chat Task Processor)
此技能旨在将非结构化的、按标题组织的聊天记录（Markdown格式）转化为可执行的项目管理资产。它严格基于文档目录结构 (TOC) 进行分段处理，而非简单的行数切分，以确保对话上下文的完整性。
使用时机
当用户提供导出的聊天记录（Markdown），且记录使用标题（#, ##...）区分不同群聊或对话对象时。
用户通常要求：
- 项目化梳理：提取任务(Assigner/Assignee)、状态(Status)、决策(Decision)。
- 特定产出物：根据聊天内容撰写周报、Bug清单、特定事件的时间线复盘等。
- 背景对齐：处理过程中需要参考用户提供的背景文档（如 PRD、人员表）。
工作流
1. 准备阶段 (Initialization)
首先，必须初始化工作区并解析文档结构。
- 接收输入：确认源文件、背景文档、以及用户的额外诉求（例如：“帮我把所有关于 API 的讨论单独整理成一个文档”）。
- 执行初始化：
运行脚本扫描源文件标题结构，并生成工作区：
bash python3 .gemini/skills/long-chat-task-processor/scripts/setup_workspace.py "path/to/chat_log.md" "工作区目录名称"
工作区目录名称可使用 YYYY-MM-DD-HH 沟通记录整理 格式。
初始化后，工作区结构如下：
Chat_Projectization_YYYY-MM-DD-HH-MM/
├── 0-工作日志.md # [核心] 基于 TOC 生成的树状任务列表
├── 1-原始记录/ # 存放源文件
├── 2-项目背景/ # 存放用户提供的背景文档，以及用户的所有指示
├── 3-实体映射表.md # [动态] 自动积累的人名/概念术语表
├── 4-任务池.md # [动态] 累积提取的任务列表
├── 5-决策与里程碑.md # [动态] 累积提取的决策和时间点
└── 6-额外输出/ # 存放用户要求的额外的输出文档- 将用户的指示和背景文档复制到
2-项目背景/目录下，确保后续处理有据可依。
2. TOC 分段处理循环 (TOC Loop)
打开 0-工作日志.md，你将看到一个基于 Markdown 标题层级的任务树。
按顺序处理每一个标记为 [ ] 的 Section。
在处理每个 Section 前，务必读取：
0-工作日志.md(获取当前 Section 的行号范围、标题背景)2-项目背景/(理解业务上下文)3-实体映射表.md(确保人名对齐)- 用户的额外诉求 (确认本段对话是否涉及需要单独输出的主题)
处理步骤：
- 读取内容：根据日志中记录的
Line Start-End，读取1-原始记录/中对应的内容。 - 执行分析 (Analysis)：
- 通用提取：
- 任务：更新
4-任务池.md。格式：[ ] <Time> **Assigner** -> **Assignee**: <Task> (Status) - 决策：更新
5-决策与里程碑.md。 - 新实体：发现新人名/黑话，追加到
3-实体映射表.md。
- 任务：更新
- 特定主题提取 (Extra Requests)：
- 如果用户的诉求包含“整理 API 问题”、“输出周报素材”等，且当前段落包含相关信息：
- 在
6-最终输出/下创建或追加对应的文档（例如6-最终输出/API_Issue_Log.md）。
- 通用提取：
- 更新状态：
- 在
0-工作日志.md中将该 Section 标记为[x]。
- 在
3. 整合与交付 (Synthesis)
当所有 Section 处理完毕后：
- 整理任务池：检查
4-任务池.md，合并重复项，按人名或优先级归类。 - 生成最终交付物：
- 如果用户要的是一份完整的汇总报告，基于
4、5和6中的内容进行汇总。 - 如果用户要的是分散的文档（如“任务清单”+“会议纪要”），则分别整理输出。
- 如果用户要的是一份完整的汇总报告，基于
关键原则
- TOC 优先：不要跨标题合并处理，除非标题层级非常深且内容极少。保持“一个群聊/一个话题”的独立上下文。
- 时间标记：群聊记录中通常使用
-- 日期或-- 日期 时间(如-- 02-09 15:00) 来标记时间点，可用于参考。 - 上下文补全：聊天中常出现的“那个东西”、“昨天说的”，尽量根据同 Section 的上下文进行指代消解。
- 宁滥勿缺：聊天记录中模糊的任务先记录下来，标记为
Status: UNCONFIRMED。 - 增量原则：在处理每个 Section 时，对 映射表、任务池、决策文档、额外输出做「信息增量更新」：
- 如果概念、任务、决策、文档内容是全新的，进行追加。
- 如果可以对已有的内容进行修正、更新、增补，则更新已有内容，使信息更完善。
- 输出文档先读再编辑: 分段处理时，如果使用 write 工具，很容易导致已有内容丢失。在编辑上述输出文档时，必须遵循 先读、再考虑如何编辑、再写 的原则，使用 edit 工具。
注意事项
- 重点关注链接、包含关键信息的图片。
- 尤其重点关注其他人指派给我的事情，以及我是否认可。
- 也要关注我交给别人的事情，以及对方是否认可。
- 如果用户打断你的编辑并指导你的失误，务必遵照执行并修正，注意你要 重做被打断的编辑 ，不要漏掉编辑操作。
- 任务可能中断，如果用户告知「从已有任务继续工作」，无需创建工作区，但 必须 阅读已有的
2-项目背景/和3-实体映射表.md以恢复上下文。然后继续处理0-工作日志.md中未完成的 Section。
输出规范
关于 0-工作日志 ，
可以类似这种格式，在 Agent 处理完成后，将对应的 [ ] 改为 [x] 。
- [ ] **群聊：API 稳定性治理** (Line 100-167)
- [ ] **私聊：小张** (Line 168-600)关于 3-实体映射表 ，
可以包含人和人的角色、群、组织、项目、概念等的解释，作为多次 Agent 工作之间的上下文补充。 不要使用表格。
关于 4-任务池，
应该包含
- 我交给别人的事情和每件事情的跟进状态
- 别人交给我的事情和每件事情的跟进状态
- 完整的事件分派时间线（包括上述两种，和「别人分派给别人的」）。
- 关键文档链接和图片（如果缩略了，列出来源群聊/单聊和上下消息，我可以自己找。）
- 落单任务（无人应答的任务）
事项可以按如下格式记录：
<时间> 交代人 -> 接收人 <事项>
- 来源群或单聊：XXX
- 交付物
- 是否确认和认可
- 后续状态© cafe3310, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 2 other files (scripts, references) in skills_parked/long-chat-task-processor of cafe3310/public-agent-skills.
- SKILL.md
- references/extraction_checklist.md
- scripts/setup_workspace.py
Open the folder on GitHubat commit 6c45501
Compare with similar skills
Long Chat Task Processor next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Long Chat Task Processor this skillcafe3310/public-agent-skills | 255 | — | ~884 | Automated safety check: Pass | Apache-2.0 | |
| Testing Core Processorsmastra-ai/mastra | 29k | — | ~1.1k | Automated safety check: Pass | Custom licence | |
| Jq JSON Processorpenpot/penpot | 61k | — | ~663 | Automated safety check: Pass | MPL-2.0 | |
| CSV Processorjeremylongshore/tons-of-skills-marketplace | 2.8k | — | ~555 | Automated safety check: Pass | MIT | |
| Sample Text Processoralirezarezvani/claude-skills | 28k | — | ~1.5k | Automated safety check: Pass | MIT | |
| Batch File Processorjeremylongshore/tons-of-skills-marketplace | 2.8k | — | ~577 | Automated safety check: Pass | MIT |
Similar skills
Jq JSON Processor
penpot/penpot
Process, filter, and transform JSON data using jq - the lightweight and flexible command-line JSON processor.
CSV Processor
jeremylongshore/tons-of-skills-marketplace
Process csv processor operations. An agent skill from jeremylongshore/tons-of-skills-marketplace.
Sample Text Processor
alirezarezvani/claude-skills
Reference BASIC-tier skill used as a fixture by skill-tester.
Batch File Processor
jeremylongshore/tons-of-skills-marketplace
Process batch file processor operations. An agent skill from jeremylongshore/tons-of-skills-marketplace.
Kafka Stream Processor
jeremylongshore/tons-of-skills-marketplace
Process kafka stream processor operations. An agent skill from jeremylongshore/tons-of-skills-marketplace.
More from cafe3310/public-agent-skills
All 29 skills in this repoImpeccable
cafe3310/public-agent-skills
A skill your agent uses when the user wants to design, redesign, shape, critique, audit, polish, clarify, distill, harden, optimize, adapt, animate, colorize, extract, or otherwise improve a…
Text Watermark Fountain
cafe3310/public-agent-skills
A specialized skill for embedding and extracting resilient watermarks in text by manipulating sentence lengths and using Fountain Codes.
Obsidian Todo Collector
cafe3310/public-agent-skills
从 Obsidian 知识库中扫描指定时间范围内未完成事件，生成/更新未完成事件整理文档. An agent skill from cafe3310/public-agent-skills.
Deep Research
cafe3310/public-agent-skills
一个全面、自主的深度研究框架。当用户请求对复杂主题、市场调研、技术格局进行深入的多维度调查，或需要大量网页浏览、数据合成和结构化报告的任何任务时，使用此技能。它协调子代理（subagents）并使用基于文件系统的状态管理来防止上下文膨胀。
Long Audio To Obsidian
cafe3310/public-agent-skills
将语音转写项目输出的复杂文件结构整理合并为适合 Obsidian 归档的 Markdown 文档. An agent skill from cafe3310/public-agent-skills.
Markdown New
cafe3310/public-agent-skills
通过 markdown.new API 将网页、整站或搜索结果转换为干净的 Markdown. An agent skill from cafe3310/public-agent-skills.
Questions about Long Chat Task Processor
How do I install Long Chat Task Processor in Claude Code?
Run `npx skills add cafe3310/public-agent-skills --skill long-chat-task-processor -a claude-code`. Or copy the skill folder (skills_parked/long-chat-task-processor in cafe3310/public-agent-skills) into .claude/skills/long-chat-task-processor in your project. Claude Code loads it when a task matches its description.
How do I install Long Chat Task Processor in Codex?
Run `npx skills add cafe3310/public-agent-skills --skill long-chat-task-processor -a codex`. Or copy the skill folder (skills_parked/long-chat-task-processor in cafe3310/public-agent-skills) into .agents/skills/long-chat-task-processor in your project. Codex loads it when a task matches its description.
Can I use Long Chat Task Processor in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add cafe3310/public-agent-skills --skill long-chat-task-processor -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/long-chat-task-processor, .gemini/skills/long-chat-task-processor, .github/skills/long-chat-task-processor and .opencode/skills/long-chat-task-processor in your project.
What does Long Chat Task Processor need to run?
Going by SKILL.md and its folder, Long Chat Task Processor needs Python for the scripts in its folder.
Does Long Chat Task Processor access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Long Chat Task Processor safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Long Chat Task Processor use?
Long Chat Task Processor is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Long Chat Task Processor use?
About 884 tokens (SKILL.md is roughly 3.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 540 tokens, read only when the agent opens those files.
What are the alternatives to Long Chat Task Processor?
Skills that share tags, products or a category with Long Chat Task Processor: Testing Core Processors (mastra-ai/mastra, 29k stars), Jq JSON Processor (penpot/penpot, 61k stars), CSV Processor (jeremylongshore/tons-of-skills-marketplace, 2.8k stars) and Sample Text Processor (alirezarezvani/claude-skills, 28k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Long Chat Task Processor?
cafe3310 (a GitHub user) maintains it in cafe3310/public-agent-skills, which has 255 GitHub stars. The repository holds 29 skills in this directory. The repository was last updated on June 26, 2026.
Source: cafe3310/public-agent-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.