- 📁 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.
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.
- 📁 scripts/
- 📄 LICENSE
- 📄 main_claw.py
- 📄 manifest.json
Search the web with multiple output formats using the Qryma API. Use this skill when the user wants to search the web, find information on a specific topic, says "search", "look up", "find information", "web search", or needs quick answers from the internet. Supports Markdown format for readability, JSON for structured data, and Brave search-like format.
- 📁 references/
- 📁 scripts/
- 📄 SKILL.md
Build search applications and query log analytics data with OpenSearch. Use this skill when the user mentions OpenSearch, search app, index setup, search architecture, semantic search, vector search, hybrid search, BM25, dense vector, sparse vector, agentic search, RAG, embeddings, KNN, PDF ingestion, document processing, or any related search topic. Also use for log analytics and observability — when the user wants to set up log ingestion, query logs with PPL, analyze error patterns, set up index lifecycle policies, investigate traces, or check stack health. Activate even if the user says log analysis, Fluent Bit, Fluentd, Logstash, syslog, traceId, OpenTelemetry, or log analytics without mentioning OpenSearch.
Interact with GitHub using the `gh` CLI. Use `gh pr`, `gh run`, `gh workflow`, `gh search`, and `gh api` for PRs, CI, search, and advanced queries.
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
Perform an internet search using the official Brave Search MCP server. Use when the user asks to search with Brave, needs image/video/news/local search, or wants Brave's privacy-focused index.
Search, qualify, and enrich people and companies. Use this skill whenever the user wants to find professionals, candidates, or KOLs by title, company, location, seniority, or audience; enrich known contacts with email, phone, or LinkedIn; research companies for industry, funding, tech stack, or hiring activity; look up someone's contact info; source candidates for recruiting; generate B2B lead lists; or perform background web research on people or organizations. Trigger this skill even when the user doesn't explicitly say "search" or "enrich" — any mention of finding contacts, sourcing, prospecting, looking up a person or company, or gathering business intelligence should activate it. --- # Lessie — People Search & Enrichment ## Setup
Search and analyze cryptocurrency market data using CoinPaprika. Activates when users search for coins, ask about prices, request market overviews, or look up tokens by contract address.
Multi-engine AI **WEB SEARCH** tool — NOT for codebase search. Use greedy_search for high-quality web research where training data may be stale or single-engine results are insufficient. Searches Perplexity, Bing, Google via browser automation. NO API KEYS needed.
Guide for writing ast-grep rules to perform structural code search and analysis. Use when users need to search codebases using Abstract Syntax Tree (AST) patterns, find specific code structures, or perform complex code queries that go beyond simple text search. This skill should be used when users ask to search for code patterns, find specific language constructs, or locate code with particular structural characteristics.
