Skills de Claude Code · página 125

Upload one or many videos to YouTube. Use when the user wants to "上传到 YouTube", "发 YouTube", "批量上传", "upload to YouTube", "post videos to YouTube", or to publish a finished `final/` directory of MP4s. Reads per-video metadata (title / description / tags) from a sibling `UPLOAD_META.md` file when present (the user's standard markdown format), or from command-line flags. Survives behind a SOCKS/HTTP proxy by using `requests` directly for the resumable upload (the stock `google-api-python-client` MediaFileUpload stalls under this user's proxy setup).

Use when 王建硕 wants to systematically improve his X (Twitter) content by iterating on the content-generation prompt (prompts/x/prompt.md, used by the every-6h tweet Action) and finding which prompt version produces the highest-reach tweets. Each prompt edit is a git-SHA-versioned, numbered experiment with a hypothesis; tweets are attributed to the version live at post time and judged on median impressions per tweet. Also mines per-tweet impression data for content-feature signals (angle / length / topic) that feed the next prompt edit. North-star = impressions per tweet. Triggers — "改 X 的 prompt", "X 内容改进", "哪版 prompt 最好", "什么内容 impression 高", "improve my tweets", "A/B test the X prompt", "/wjs-x-improving-content".

Use when 王建硕 wants to systematically grow his X (Twitter) followers by running numbered, A/B-testable growth experiments and tracking which ones actually work. Every action gets a number, a hypothesis, a target metric, a before-state (for rollback), and a verdict. The North-Star metric is the new-follower ÷ profile-visit ratio (conversion) — every profile change is judged against it. Daily it ingests the X Analytics CSV export, scores each running experiment, and recommends keep / rollback. Triggers — "涨粉", "增加 X 粉丝", "X 涨粉实验", "follower growth", "A/B test my profile", "今天的涨粉检查", "/wjs-x-increasing-follower".

Find context from past Claude Code (CLI) and Claude Cowork (desktop) sessions on this Mac. Use when the user wants to recall something they did before but can't find it , phrasings like "where did I work on X", "find that session where I…", "when did I last do Y", "pull up the conversation about Z", "that time I built/tried/discussed …". Searches by kind (code/cowork), time range, title, working directory, or free-text content across all transcripts.

Mine the user's Claude Code + Cowork session history into a structured task profile, what they do with AI, how often, how successfully where friction lives, then propose atomic skills that would reduce iteration. Use when the user asks to "analyse my Claude use", "build a task profile", "what tasks do I do with Claude", "where am I spending tokens", "what skills would help me", or mentions reviewing past sessions for patterns. Produces profile.csv (shareable), explorer.html (personal coaching view with AI-first principle comparison + token-spend chart), and skill-proposals.md.

Personal diagnosis of where your Claude Code + Cowork spend goes. Reads local transcripts, prints your conversation length distribution, marathon share, cache rebuild costs, and per-project diagnosis (good projects and problem projects) right in the terminal. Then offers a deeper dive that fans out parallel Haiku subagents over your most expensive (and most efficient) sessions and writes a tight Markdown report. Use when the user asks "why is my Claude spend so high", "where am I burning tokens", "diagnose my Claude habits", "audit my Claude usage", or asks for a personal token-cost diagnosis.

Analyze, re-engineer, or bootstrap projects to align with AI-first design principles. Use when asked to review, audit, improve, 'ai-firstify', or start a new project. Performs deep analysis across 7 dimensions, actively restructures existing projects, or guides new project setup through discovery questions. Based on the 9 design principles and 7 design patterns from the TechWolf AI-First Bootcamp.

Analyze engagement patterns across published posts to identify what works. Use when asked to review performance, find successful patterns, or optimize future content.

Generate LinkedIn post ideas from external sources (files, URLs, research). Use when the user provides source material (PDFs, URLs, articles) to brainstorm topics. NOT for writing or developing drafts - use write-linkedin-post instead.

Generate opinion piece ideas from recent LinkedIn posts (last 30 days). Use when asked to find opinion topics, brainstorm article ideas, or cross-pollinate content between LinkedIn and opinion pieces.

Entry point for the TechWolf content-studio plugin. Use to understand the workflow, pick the right content skill, or start setup for a new author/repository.

Set up a new content studio for a person. Copies the plugin template, adapts it to the person's voice, themes, and content types through interactive discovery. Use when asked to create a content studio for someone new.

Write or develop a blog post. Use for blog content - writing, drafting, developing ideas into drafts, or editing. Longer-form than LinkedIn (800-1200 words) with section structure.

Write or develop a LinkedIn post. Use ALWAYS for LinkedIn content - writing, drafting, developing ideas into drafts, or editing.

Write or develop an opinion piece (opiniestuk/op-ed). Use when asked to write opinion articles, newspaper pieces, or similar long-form opinion content.

|

|

|

|

Synthesised view of account health and activity for managers overseeing customer-facing teams (Sales, CS, Professional Services, Presales). Scans project channels, email threads, and Notion pages to surface status, risks, and upcoming milestones, without requiring the manager to trawl through individual channels. Supports proactive account management.

Comprehensive pre-meeting briefing that gathers all relevant context from Slack, email, Google Docs, Notion, and calendar. Produces a structured prep document so the manager walks into every meeting fully prepared. Supports thorough meeting preparation.

Deep-dive preparation for 1:1 meetings with direct reports. Surfaces recent work, wins, friction, wellbeing signals, and development goal progress, anchored in the org's performance framework, organizational values, and management best practices. Produces a prep sheet with suggested conversation topics, not a script.

Evidence gathering for performance review cycles. Gathers goal completion evidence, peer feedback, development progress, scope changes, and values alignment, organised along the org's performance framework dimensions, with organizational values as the 'how' lens. Surfaces evidence gaps. Never suggests ratings, only organises evidence for the manager's judgment.

Helps managers cut through noise and identify their highest-leverage actions for the day or week. Aggregates signals from calendar, triage, team context, and OKRs/goals. Presents a suggested focus list grouped by urgency, importance, and investment. The manager reviews and adjusts. Supports effective execution and prioritisation.

Interactive onboarding that discovers team structure, terminology, development goals, performance and management frameworks, organizational values, and ways of working by crawling Slack, Notion, Google Drive, Gmail, and Calendar. Validates everything with the manager before persisting. Run this first before using any other skill. Also handles periodic context refreshes via /setup --refresh.

Periodic check on team dynamics, engagement signals, and development trajectory for all direct reports. Surfaces patterns across the team: who might need more challenge, who might need more support, who hasn't had a 1:1 recently. Uses two universal lenses: performance & growth, and wellbeing & connection. Outputs are prompts for reflection, not diagnoses.

Batch-processes Slack messages and emails to surface what needs the manager's attention, categorised by urgency and type. Designed for batch-responder managers who do Slack sweeps rather than staying in reactive mode. Supports effective communication and responsiveness. Never drafts replies, only surfaces and prioritises.

Provides official TechWolf logo files in multiple variants (dark, white, monochrome) as SVG and PNG. Use when any output needs a TechWolf logo.

Build an MCP server end to end, tailored to how it will be used. Use when asked to build an MCP, create an MCP server, wrap an API as a tool, make a tool for Claude, expose a service to an agent, build a Claude connector, or turn a service into MCP tools. Asks up front who the server is for (just me, my org, or public) and what it wraps, then walks through analyze, build, deploy, scale, and distribute with steps tailored to that answer. Builds on the example-skills:mcp-builder skill for implementation depth.

Authoritative reference for the neo4j-agent-memory Python package — a graph-native memory system for AI agents built on Neo4j — and for the hosted service (NAMS) at memory.neo4jlabs.com. Use this skill whenever the user mentions neo4j-agent-memory, agent memory with Neo4j, context graphs, the POLE+O model, MemoryClient/MemorySettings, the memory MCP server, or any of the framework integrations (LangChain, PydanticAI, CrewAI, AWS Strands, Google ADK, Microsoft Agent Framework, OpenAI Agents, LlamaIndex). Also use when the user mentions the hosted service at memory.neo4jlabs.com, NAMS, the Neo4j Agent Memory Service, the `nams_` API key prefix, or the hosted MCP endpoint. Also use when writing documentation, blog posts, tutorials, PRDs, or code samples for the project, when comparing agent memory approaches, or when positioning graph-native memory against vector-only approaches — even if the user doesn't explicitly name the package.

Manages Neo4j Aura Agents via the v2beta1 REST API — create, list, get, update, delete,

Serverless Aura Graph Analytics (AGA) GDS Sessions — covers GdsSessions,

Provisions and manages Neo4j Aura instances via CLI (aura-cli v1.7+) or REST API.

Use when working with Neo4j command-line tools — neo4j-cli (modern unified

Generates, optimizes, and validates Cypher 25 queries for Neo4j 2025.x and 2026.x.

Ingests unstructured and semi-structured documents into Neo4j as a knowledge graph.

Neo4j .NET Driver v6 — IDriver lifecycle, DI registration (singleton), ExecutableQuery

Covers the Neo4j Go Driver v6 — driver lifecycle, ExecuteQuery, managed and

Neo4j Java Driver v6 — driver lifecycle, Maven/Gradle setup, executableQuery,

Neo4j JavaScript/TypeScript Driver v6 — driver lifecycle, executeQuery,

Neo4j Python Driver v6 — driver lifecycle, execute_query, managed and explicit

Neo4j Graph Data Science (GDS) embedded plugin via Python client or Cypher —

Use Neo4j GenAI Plugin ai.text.* functions and procedures for in-Cypher

Orchestrates zero-to-running-app in 8 stages — prerequisites → context →

Build and configure a GraphQL API backed by Neo4j using @neo4j/graphql v7 (current) or v5 (LTS).

Build GraphRAG retrieval pipelines on Neo4j using the neo4j-graphrag Python

Import structured data into Neo4j — LOAD CSV, CALL IN TRANSACTIONS, neo4j-admin

Configure and operate the Neo4j Connector for Kafka (sink + source) and the

Use when installing, configuring, or troubleshooting the official Neo4j MCP server

Migrates Neo4j driver code and Cypher queries from older versions (4.x, 5.x)

Design, review, and refactor Neo4j graph data models. Use when choosing node

Neo4j Visualization Library (NVL) — framework-agnostic graph rendering for the browser.

Diagnoses and fixes slow Neo4j Cypher queries by reading execution plans, identifying

Programmatic security management in Neo4j — RBAC/ABAC, user lifecycle (CREATE/ALTER/DROP USER),

Run Neo4j Graph Analytics algorithms (PageRank, Louvain, WCC, Dijkstra, KNN,

Use when reading from or writing to Neo4j with Apache Spark or Databricks using the

Use when building Spring Boot applications with Neo4j using Spring Data Neo4j (SDN 7.x/8.x):

Create and manage Neo4j vector indexes, run vector similarity search (ANN/kNN),

>

>

>

>

>

>

Performs deep architectural analysis of a specified module, directory, or feature area by examining structural coupling, data flow, concurrency patterns, risk, and SOLID alignment. Use when the user wants to assess, evaluate, or review the architecture, design quality, dependency structure, coupling, cohesion, or technical debt of an existing part of the codebase. Not for investigating specific bugs, runtime errors, or failures — use investigate. Not for test planning — use test-planning. Not for file-level code review — use code-review. Not for researching open-ended options, prior art, or how something works — use research. Not for writing documentation or architectural decision records.

Run a comprehensive code review on local source files. Use this skill when the user asks to review, audit, inspect, evaluate, or check code, even if they never use the word \"review.\" Does not post comments to GitHub pull requests — use post-code-review-to-pr for that. Does not analyze architectural structure or module boundaries — use architectural-analysis for that. Does not capture feedback on Han's own skills — use han-feedback for that.

>

>

>

>

>

>

>

>

>

>

Researches an open-ended question — options, possible solutions, prior art, trade-offs, or how something works — and produces a durable, evidence-backed, adversarially-validated report that recommends an option without committing the team to any artifact. Use when you want to research approaches, weigh options, survey prior art or the state of the art, or understand how something works before committing to a direction. Does not diagnose a bug, failure, or root cause — use investigate. Does not specify a feature — use plan-a-feature. Does not create or update a coding standard — use coding-standard. Does not compare two concrete artifacts for gaps — use gap-analysis. Does not assess an existing module's architecture — use architectural-analysis. Does not capture feedback on Han's own skills — use han-feedback.

>

>

>

>

>

>

>

>

>

>

>

>

>

>

>

Capture current work context for handoff to another agent/developer. Gathers git state, todos, and modified files into a structured handoff document saved to the related spec folder.

Perform thorough code reviews focusing on unused code, duplications, coding patterns, bugs, and optimizations. Use when user wants code reviewed or audited. Read-only - outputs findings without making changes.

Commit changes with well-crafted messages, grouping related files into separate commits

Instrument web/web-app code with structured debug logging via a global variable (window.__debug_logs). Produces a clean JSON timeline for reproducing and diagnosing bugs. Use when user wants to debug a feature or track down a bug.

Create detailed implementation plans for features. Asks clarifying questions, suggests solutions, proposes architecture, and outputs a structured plan document. Use when user wants to plan a feature before coding.

Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, or applications. Generates creative, polished code that avoids generic AI aesthetics.

Restructures a chaotic or overgrown MEMORY.md into a clean 2-layer architecture based on how Claude Code's autoDream system organizes memory — a lightweight pointer index (always loaded) and topic files (loaded on demand). Stale or superseded memories are deleted or corrected in place — not archived. Use this skill whenever the user says \"clean up MEMORY.md\", \"reorganize my memory files\", \"MEMORY.md is getting too long\", \"fix my memory structure\", or when you observe that MEMORY.md exceeds 200 lines, contains full paragraphs instead of pointers, or mixes index entries with topic content.

>