- 📁 references/
- 📁 scripts/
- 📄 .clawhubignore
- 📄 .gitignore
- 📄 LICENSE
Search, browse, and read the user's local documents indexed by Linkly AI. This skill should be used when the user asks to 'search my documents', 'find files about a topic', 'read a local document', 'search my knowledge base', 'browse document outlines', 'list knowledge libraries', 'explore my documents', or any task involving searching, browsing, or reading locally stored documents (PDF, Markdown, DOCX, TXT, HTML). Also triggered by: 'linkly not working', 'can not connect to linkly', '搜索我的文档', '查找文件', '知识库搜索', '浏览文档大纲', '列出知识库', '连接不上', '故障排查'. Provides full-text search, structural outlines, and paginated reading via CLI or MCP tools.
Manage Apple Notes via the memo CLI on macOS (create, view, search, edit).
- 📁 .claude-plugin/
- 📁 .cursor/
- 📁 .github/
- 📄 .gitignore
- 📄 AGENTS.md
- 📄 CHANGELOG.md
Audits and optimizes websites for search engine visibility (SEO) and AI search citation (GEO), covering technical health, E-E-A-T content scoring, domain authority, structured data, rich results, and entity signals. Use when running SEO audits, diagnosing traffic drops or ranking losses, generating Schema.org JSON-LD, checking Core Web Vitals, crawlability, robots.txt, sitemaps, hreflang, backlinks, planning content strategy or site migrations, fixing indexing issues, or optimizing for AI Overviews, ChatGPT, and Perplexity. NOT for paid ads (PPC/SEM), social media strategy, email marketing, or general web development unrelated to search.
Operate SiYuan notes via 7 aggregated MCP tools (notebook/document/block/file/search/tag/system). Covers path semantics, permissions, block editing, search, tags, and export.
Unified RAG pipeline skill for Rust/MCP projects. Covers architecture, local embeddings via Candle ML framework, chunking strategies, retrieval patterns, hybrid search, reranking, and evaluation (NDCG/MRR). Use when: building RAG, vector search, semantic search, document retrieval, embeddings in Rust.
- 📄 .gitignore
- 📄 LICENSE
- 📄 README.md
Better Google search. Type a query like you would in Google and get 3 synthesized mini-briefings with 'best for' verdicts, adaptive ratings, and source citations. Searches from multiple angles (general, Reddit/forums, reviews) to surface what a single Google search misses.
Manage Apple Notes via the `memo` CLI on macOS (create, view, edit, delete, search, move, and export notes). Use when a user asks to add a note, list notes, search notes, or manage note folders.
- 📁 assets/
- 📁 references/
- 📁 scripts/
- 📄 .gitignore
- 📄 AGENTS.md
- 📄 BENCHMARK_REPORT.md
Hybrid SQLite + Vector persona memory system for Zo Computer. Episodic memory with temporal queries, graph-boosted search, BFS path finding, knowledge gap analysis, auto-capture pipeline. Gives personas persistent memory with semantic search (nomic-embed-text), HyDE query expansion (qwen2.5:1.5b), Ollama-powered memory gate, 5-tier decay, and swarm integration. Requires Ollama for embeddings.
- 📁 references/
- 📁 scripts/
- 📄 SKILL.md
Search and retrieve knowledge from agentic_kb knowledge base. Use when the user requests to search the KB, asks "How do I..." questions that should consult the KB, wants to document new knowledge, or at session start to update the KB submodule. Also use when User wants to udpate the knowledge base with new knowledge. Knowledge Capture when you learn new, reusable knowledge during tasks. Supports Typesense (fast full-text search), FAISS (semantic vector search), and ripgrep (exact pattern matching). All KB is Obsidian formatted and can be browsed easily and visually with network maps in Obsidian.
This skill should be used when the user asks to "search code", "find in files", "grep for", "look for pattern", "search the codebase", "find references to", "find usages of", "search for function", "find where X is defined", or needs to search file contents across a directory tree. Provides guidance on using the search_code MCP tool for fast indexed code search.
空投项目评估 — 基于 v3 门槛+加权模型（发币意愿/风险 门槛检查 → 筹码/链上/竞争/成本 加权评分） 百分制 × 系数，输出档位判定（Sprint/中等维护/低保维护）。 输出格式对齐 P-xxx 空投评估模板。Triggers on "空投评估", "airdrop evaluation", "项目评分", "airdrop scoring", "空投分析", "evaluate airdrop", or "P-xxx". --- # Airdrop Evaluation (v3) 基于门槛+加权评分框架对空投项目进行综合评估，输出 P-xxx 格式报告。 ## Data Source Priority ### Layer 1: MCP - **coingecko** — 代币信息（如已发币） - **dune** — 链上数据（交易指标、用户增长、手续费、供需分析、KPI 汇总） ### Layer 2: Chrome CDP - `defillama.com/protocol/{protocol}` — TVL 趋势、协议数据 - 官网、文档、Discord ### Layer 3: Web Search - 融资背景、团队信息、社区规模、积分机制、官方公告、竞品信息 ## Workflow ### Step 1: Project Identification + Document Collection - 解析项目名称 - 查找官网、文档、社交媒体链接 - 确认项目状态（是否已发币、是否有积分系统） - **主动询问用户是否有项目相关文档**（白皮书、tokenomics、积分规则等） - 用户提供 → 优先作为评分依据，按文档性质标注置信度 - 官方公告/白皮书/合约文档 → ◆ - 多源交叉验证的分析 → ◇ - 单一来源未验证 → ○ - 用户没有 → 继续自动拉取 ### Step 2: Auto-Fetch Data 自动拉取可获取的数据： - coingecko: 代币信息（如已发币） - dune: 链上数据 - 日度交易指标（交易次数、交易量 USD、手续费 USD、Unique Takers/Makers） - 用户增长（新增用户、7日均值、累计用户） - 协议收入/手续费趋势 - 供需背离分析（供给侧 vs 需求侧指标趋势对比） - 汇总 KPI（总交易量、总交易数、总手续费、总用户数、峰值日、WoW 变化） - defillama: TVL 趋势（Chrome CDP） - Web Search: 融资轮次、估值、团队背景、积分机制细节、社区规模、竞品信息 （URL 未知时先 Web Search 取 URL 再 Chrome CDP 访问，Web Search 无法找到 URL 则直接 Web Search 摘要兜底） ### Step 3: Gate Check (门槛检查) 预填"发币意愿"和"风险等级"评分 + 依据 + 置信度标注： | 门槛维度 | 建议分数 | 系数 | 依据 | 置信度 | |---------|---------|------|------|-------| | 发币意愿 | X | ×Y | [data] | ◆/◇/○ | | 风险等级 | X | ×Y | [data] | ◆/◇/○ | **明确标注为建议评分，等待用户确认或调整。** - 用户确认后： - 任一维度 < 3 → 输出"放弃"精简报告，**流程终止** - 两项都 ≥ 3 → 记录系数，进入 Step 4 ### Step 4: Weighted Scoring (加权评分预填 + 用户确认) 预填四个加权维度评分建议： | 维度 | 权重 | 建议分数 | 依据 | 不确定性 | 置信度 | |------|------|---------|------|---------|-------| | 筹码获取 | 30% | X | [data] | [unknowns] | ◆/◇/○ | | 链上健康度 | 25% | X | [data] | [unknowns] | ◆/◇/○ | | 竞争定位 | 25% | X | [data] | [unknowns] | ◆/◇/○ | | 单位成本 | 20% | X | [data] | [unknowns] | ◆/◇/○ | **明确标注为建议评分，等待用户确认或调整。** 用户可以补充自己的判断依据。 ### Step 5: Calculate + Report (计算 + 生成报告) - 计算最终分 - 档位判定（含降档规则） - 催化剂表格（如有） - 按模板输出报告 ## Output Template — Gate
Search, recover, and analyze AI session histories across Claude Code, AI Studio, and Gemini CLI. Use when user asks to "find that file from last week", "search sessions", "recover context after compaction", "what did the AI do", "export session to markdown", "find corrections", "analyze session quality", "improve CLAUDE.md from past mistakes", or "turn AI mistakes into rules". Contains session search, file recovery, correction detection, self-improvement workflow.
