Workflow for transforming prose into semantic OCTAVE structures. Covers tier selection, transformation phases, loss accounting, and decision rules. REQUIRES octave-literacy.
Control interactive terminal applications like vim, git rebase -i, git add -i, git add -p, apt, rclone config, sudo, w3m, and TUI apps. Can also supervise another CLI LLM (cursor-agent, codex, etc.) - approve or reject its actions by pressing y/n at confirmation prompts. Use when you need to interact with applications that require keyboard input, show prompts, menus, or have full-screen interfaces. Also use when commands fail or hang with errors like "Input is not a terminal" or "Output is not a terminal". Better than application specific hacks such as GIT_SEQUENCE_EDITOR or bypassing interactivity through file use.
DNA 记忆系统 - 让 AI Agent 像人脑一样学习和成长。 三层记忆架构（工作/短期/长期）+ 主动遗忘 + 自动归纳 + 反思循环 + 记忆关联。 激活场景：用户提到"记忆"、"学习"、"进化"、"成长"、"记住"、"回顾"、"反思"。 --- # DNA Memory - DNA 记忆系统 > 让 Agent 不只是记住，而是真正学会。 ## 核心理念 人脑不是硬盘，不会无差别存储所有信息。人脑会： - **遗忘**不重要的 - **强化**反复出现的 - **归纳**零散信息为模式 - **反思**过去的成功和失败 DNA Memory 模拟这个过程，让 Agent 真正"进化"。 --- ## 三层记忆架构 ``` ┌─────────────────────────────────────────────────┐ │ 工作记忆 (Working Memory) │ │ - 当前会话的临时信息 │ │ - 会话结束后自动筛选 │ │ - 文件：memory/working.json │ └─────────────────────────────────────────────────┘ ↓ 筛选 ┌─────────────────────────────────────────────────┐ │ 短期记忆 (Short-term Memory) │ │ - 近7天的重要信息 │ │ - 带衰减权重，不访问会逐渐遗忘 │ │ - 文件：memory/short_term.json │ └─────────────────────────────────────────────────┘ ↓ 巩固 ┌─────────────────────────────────────────────────┐ │ 长期记忆 (Long-term Memory) │ │ - 经过验证的持久知识 │ │ - 归纳后的认知模式 │ │ - 文件：memory/long_term.json + patterns.md │ └─────────────────────────────────────────────────┘ ``` --- ## 记忆类型 | 类型 | 说明 | 示例 | |------|------|------| | `fact` | 事实信息 | "Andy 的微信是 AIPMAndy" | | `preference` | 用户偏好 | "Andy 喜欢简洁直接的回复" | | `skill` | 学到的技能 | "飞书 API 限流时要分段请求" | | `error` | 犯过的错误 | "不要用 rm，用 trash" | | `pattern` | 归纳的模式 | "推送 GitHub 前先检查网络" | | `insight` | 深层洞察 | "Andy 更看重效率而非完美" | --- ## 核心操作 ### 1. 记录 (Remember) ```bash python3 scripts/evolve.py remember \ --type fact \ --content "Andy 的 GitHub 账号是 AIPMAndy" \ --source "用户告知" \ --importance 0.8 ``` ### 2. 回忆 (Recall) ```bash python3 scripts/evolve.py recall "GitHub 账号" ``` 返回相关记忆，按相关度和重要性排序。 ### 3. 反思 (Reflect) ```bash python3 scripts/evolve.py reflect ``` 触发反思循环： 1. 回顾近期记忆 2. 识别重复模式 3. 归纳成认知模式 4. 更新长期记忆 ### 4. 遗忘 (Forget) ```bash python3 scripts/evolve.py decay ``` 执行遗忘机制： - 7天未访问的短期记忆权重衰减 - 权重低于阈值的记忆被清理 - 重要记忆不会被遗忘
v0.11.6 — 通过 ACP Bridge HTTP API 调用远程 CLI agent，支持多 agent pipeline。Usage: /cli <prompt> | /cli ko <prompt> (kiro) | /cli cc <prompt> (claude) | /chat ko (进入对话模式)
面向中国本科生的毕业论文全流程写作辅助系统。支持从选题到交稿的端到端工作流，包含降重优化、AIGC 降低和本地检测功能。用户说「帮我写论文」或「帮我降重」等时触发。
Fleet infrastructure mechanics — member management, permissions, onboarding, provider awareness, and tool usage patterns
Capture a session's repeatable process into a reusable SKILL.md file following the agentskills.io standard. Use when the user says "skillify this", "turn this into a skill", "capture this as a skill", "make this repeatable", "save this workflow", or "create a skill from this session". Works at end of any workflow worth repeating.
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Use this skill when the user makes a technical decision, establishes a new pattern, defines business rules, or explicitly asks to remember or save a guideline. Also use this skill when you are about to implement a feature, write code, plan an architecture, or make a technical decision - you MUST retrieve contextual memory first to follow established patterns. Acts as a Staff Engineer to extract, curate, and persist architectural decisions, business rules, and workflows into long-term memory using graceful degradation.
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Use this workflow when filling an existing public-form or administrative HWPX with controlled placeholder replacement, fixed table-cell updates, and structure validation on the current MCP surface.
skill-sample/ ├─ SKILL.md ⭐ Required: skill entry doc (purpose / usage / examples / deps) ├─ manifest.sample.json ⭐ Recommended: machine-readable metadata (index / validation / autofill) ├─ LICENSE.sample ⭐ Recommended: license & scope (open source / restriction / commercial) ├─ scripts/ │ └─ example-run.py ✅ Runnable example script for quick verification ├─ assets/ │ ├─ example-formatting-guide.md 🧩 Output conventions: layout / structure / style │ └─ example-template.tex 🧩 Templates: quickly generate standardized output └─ references/ 🧩 Knowledge base: methods / guides / best practices ├─ example-ref-structure.md 🧩 Structure reference ├─ example-ref-analysis.md 🧩 Analysis reference └─ example-ref-visuals.md 🧩 Visual reference
More Agent Skills specs Anthropic docs: https://agentskills.io/home
├─ ⭐ Required: YAML Frontmatter (must be at top) │ ├─ ⭐ name : unique skill name, follow naming convention │ └─ ⭐ description : include trigger keywords for matching │ ├─ ✅ Optional: Frontmatter extension fields │ ├─ ✅ license : license identifier │ ├─ ✅ compatibility : runtime constraints when needed │ ├─ ✅ metadata : key-value fields (author/version/source_url...) │ └─ 🧩 allowed-tools : tool whitelist (experimental) │ └─ ✅ Recommended: Markdown body (progressive disclosure) ├─ ✅ Overview / Purpose ├─ ✅ When to use ├─ ✅ Step-by-step ├─ ✅ Inputs / Outputs ├─ ✅ Examples ├─ 🧩 Files & References ├─ 🧩 Edge cases ├─ 🧩 Troubleshooting └─ 🧩 Safety notes
Skill files are scattered across GitHub and communities, difficult to search, and hard to evaluate. SkillWink organizes open-source skills into a searchable, filterable library you can directly download and use.
We provide keyword search, version updates, multi-metric ranking (downloads / likes / comments / updates), and open SKILL.md standards. You can also discuss usage and improvements on skill detail pages.
Quick Start:
Import/download skills (.zip/.skill), then place locally:
~/.claude/skills/ (Claude Code)
~/.codex/skills/ (Codex CLI)
One SKILL.md can be reused across tools.
Everything you need to know: what skills are, how they work, how to find/import them, and how to contribute.
A skill is a reusable capability package, usually including SKILL.md (purpose/IO/how-to) and optional scripts/templates/examples.
Think of it as a plugin playbook + resource bundle for AI assistants/toolchains.
Skills use progressive disclosure: load brief metadata first, load full docs only when needed, then execute by guidance.
This keeps agents lightweight while preserving enough context for complex tasks.
Use these three together:
Note: file size for all methods should be within 10MB.
Typical paths (may vary by local setup):
One SKILL.md can usually be reused across tools.
Yes. Most skills are standardized docs + assets, so they can be reused where format is supported.
Example: retrieval + writing + automation scripts as one workflow.
Some skills come from public GitHub repositories and some are uploaded by SkillWink creators. Always review code before installing and own your security decisions.
Most common reasons:
We try to avoid that. Use ranking + comments to surface better skills:
