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Found 130 Skills
Extract and structure personal context from AI chat transcripts into themed markdown files. Use when (1) Processing Claude, Claude Code, or other AI conversation exports, (2) Building personalized AI assistants from chat history, (3) Creating context files for Claude Projects, GPTs, or Gems, (4) Consolidating scattered knowledge from multiple conversations. Optimized for Claude Haiku.
Read and summarize WeChat Official Account articles. Trigger words: "/read-gzh", "Help me read this official account article", "Summarize this article"
Implement Documenso rate limiting, backoff, and request throttling patterns. Use when handling rate limit errors, implementing retry logic, or optimizing API request throughput for Documenso. Trigger with phrases like "documenso rate limit", "documenso throttling", "documenso 429", "documenso retry", "documenso backoff".
This skill should be used when user wants to access, capture, or reference Claude Code session history. Trigger when user says "capture session", "save session history", or references past/current conversation as a source - whether for saving, extracting, summarizing, or reviewing. This includes any mention of "what we discussed", "today's work", "session history", or when user treats the conversation itself as source material (e.g., "from our conversation").
Triggered by "tidy up", "clean up transactions", "categorize uncategorized", "organize my transactions"
Generates Enonic XP scripts for bulk content operations — creating, updating, querying, migrating, and transforming content using lib-content and lib-node APIs. Covers the query DSL (NoQL), aggregations, batch processing, task controllers for long-running operations, and export/import workflows. Use when writing bulk content creation, update, or deletion scripts, querying with NoQL syntax, migrating content between environments, running long-running task operations, or working with aggregations and paginated retrieval. Do not use for Guillotine GraphQL frontend queries, content type schema definitions, single contentLib.get() calls, or non-Enonic data migration tools.
Resize, convert, and batch-process images using ImageMagick.
Enables AI-powered parsing and key information extraction from high-frequency documents including invoices, orders, receipts, long texts, and common Chinese identity & credential documents. Supports reusable custom templates for non-standard business files. Features batch concurrent processing to automate document workflows for finance, administration, HR data entry and other departments.
使用免费翻译API在语言之间翻译文本，支持批量处理和文件翻译。
Run fact-grounded image generation batches for short-form video production, especially persona images, first-frame candidates, and light consistency edits. Use this when persona and concept inputs already exist and you need local image assets, prompt records, and reusable model-call metadata. This skill should stay anchored to benchmark-backed persona locks and should save both raw provider responses and normalized local asset manifests.
Download workflow run results, export segment data, and monitor run metrics using the Cargo CLI. Use when the user wants run metrics, error rates, data export, or download results for their Cargo workspace. For billing and credit usage, use the cargo-billing skill instead.
Neo4j Python Driver v6 — driver lifecycle, execute_query, managed and explicit transactions, async (AsyncGraphDatabase), result handling, data type mapping, error handling, UNWIND batching, connection pool tuning, and causal consistency. Use when writing Python code that connects to Neo4j via GraphDatabase.driver, execute_query, execute_read, execute_write, AsyncGraphDatabase, neo4j.Result, or RoutingControl. Package name is `neo4j` (not neo4j-driver) since v6. Python >=3.10 required. Does NOT handle Cypher query authoring — use neo4j-cypher-skill. Does NOT cover driver upgrades or breaking changes — use neo4j-migration-skill. Does NOT cover GraphRAG pipelines (neo4j-graphrag package) — use neo4j-graphrag-skill.