weekly-review
Generates weekly review reports in Markdown format based on AI session data and optional user annotations.
README
weekly-review-skill (Weekly Review Skill)
一句话简介：跨平台周度复盘 skill——Agent 采集本平台数据，Skill 固化复盘结构与可选渲染。
目录
项目介绍
为什么做？ 周度复盘缺统一结构；各平台会话存储格式各异，不应由 skill 去猜。
做什么？ 宿主 Agent 用自己的能力读本机会话/用量，整理成 review-input；本 skill 规定六章复盘与根因框架，并可把 JSON 渲染成 Markdown。
功能特性
- Agent 采数 + Skill 定结构（真正跨平台）
- 标准
review-inputJSON（skills/weekly-review/schema/） - CLI / MCP 可选渲染（Python ≥ 3.10，仅标准库）
- ClawHub 可发布，slug：
weekly-review
快速上手
git clone https://github.com/testman2025/weekly-review-skill.git
cd weekly-review-skill/skills/weekly-review
python -m cli --input schema/review-input.example.json -o weekly-report.md
或从 ClawHub：openclaw skills install weekly-review
使用说明
- Agent 汇总本周会话事实 → 填入
review-input - 对话按六章输出，或 CLI：
python -m cli -i review-input.json -o 周度复盘.md - 对开放会话当场拍板
字段说明见 skills/weekly-review/schema/README.md。
旧版「直接读 SQLite」代码已移至 skills/weekly-review/legacy/，非公开主路径。
发布到 ClawHub
clawhub login
clawhub skill publish ./skills/weekly-review \
--slug weekly-review \
--name "Weekly Review" \
--version 1.2.3 \
--changelog "Agent-fed review: host Agent collects facts; skill renders structure."
许可证
- 本仓库：MIT
- ClawHub 分发：MIT-0
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.