Claude Code Skills · page 120

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),

Use when setting up a project environment — installing dependencies, verifying builds, detecting the tech stack. Covers Phase 0 of a new session.

Use when committing code, pushing branches, writing .gitignore entries, or generating PR metadata. Covers commit format, branch rules, and artifact exclusion.

Use when updating project documentation, saving learnings, or running retrospectives. Covers CLAUDE.md updates, memory persistence, and cross-session learning.

Fetches live AI crypto trading signals with entry price, stop-loss, take-profit, leverage, confidence scores, and automated verification. Covers 50+ coins including BTC, ETH, SOL. Use when the user asks for crypto signals, trade ideas, market direction, portfolio analysis, or wants to build a trading bot.

Unified skill hub for Solana development. Routes to external submodule skills (solana-foundation, sendai, solana-game, trailofbits, cloudflare, qedgen, colosseum, solana-new, ghostsecurity, defending-code) and local skills. Progressive disclosure — read only what you need.

Prepare a winning hackathon submission. Use when the user says "hackathon submission", "submit to hackathon", "demo script", "demo video", "which track should I enter", "Colosseum", "help me win the hackathon", or asks about hackathon grants and Superteam Earn.

Find and validate what to build in crypto. Use when the user asks "what should I build", "validate this idea", "is this worth building", "find me a startup idea", "crypto idea", or wants blunt feedback on a project concept before writing code.

Build a pitch deck for a crypto project. Use when the user says "pitch deck", "demo day", "investor presentation", "grant application slides", "accelerator application", "help me pitch", or needs slides for a hackathon final.

Skill hub for the solana-ai-kit Claude Code plugin. Routes the bundled go-to-market skills and the opt-in add-on catalog, and points to upstream marketplaces (and the install.sh full install) for protocol, security, and ecosystem depth. Progressive disclosure — read only what you need.

Report deck generator / data-report presentation builder — turns a data report into a 16:9 web-based slide deck (1600×900 design canvas, auto-scaled to the browser), playable fullscreen with one-click PDF export. Per-slide split source + physical data separation + switchable themes + ECharts charts. **Always use this skill** when the user asks to "make a monthly/quarterly report", "make a work-report deck", "turn data into a web/PDF presentation", or wants to reuse a "one file per slide, data stored separately, themes switchable" deck template. Even if the user never says "PPT" or "slides", any time the task is to turn "several datasets + conclusions" into a presentable/shareable web report, follow this skill's workflow.

Documentation-first development methodology. The goal is AI-ready documentation - when docs are clear enough, code generation becomes automatic. Triggers on "Build", "Create", "Implement", "Document", or "Spec out". Version 3.5 adds Phase 2.5 Adversarial Review and renames internal verification to Spec Gate (structural completeness). Clarity Gate is now a separate standalone tool for epistemic quality.

Read all atomic guidelines in wiki-twobatch/guidelines/ and propose themed clusters that group near-duplicates. Writes cluster pages and updates _config.yaml; originals are preserved with a `superseded_by:` backref.

Consult an agent-wiki for guidelines relevant to the task at hand. The wiki itself documents how to retrieve from it (AGENTS.md). Use this skill once you know what task or sub-task you're about to do — not at session start.

Read a normalized Claude Code trajectory JSON and extract reusable guidelines into wiki-twobatch/guidelines/. Use when mining saved trajectories for reusable lessons.

Ingest one or more agent trajectories (raw bob/claude traces or normalized JSON) into an agent-wiki end-to-end — convert, summarize, extract guidelines, synthesize skills, consolidate into clusters, and catalog. Use when you have a batch of traces to turn into a wiki in one pass.

Read a normalized Claude Code trajectory JSON and write an episodic summary page to wiki-twobatch/summaries/. Use when summarizing one or more saved trajectories into the agent wiki.

Read a normalized Claude Code trajectory JSON and produce a wiki-resident SKILL.md page that future agents can invoke. Use when a trajectory captured a non-trivial successful workflow worth promoting from a free-text guideline to an executable, callable artifact.

Discover task families across summaries and write per-family comparison pages with findings narrative. Updates wiki-twobatch/_config.yaml task definitions and writes tasks/<slug>__task.md.

Must be used near the end of any non-trivial turn that produced potentially reusable tools, guidance, errors, workarounds, or workflows, so those lessons are saved for future turns.

Analyze saved trajectories and recall audit events offline to record whether recalled guidelines influenced completed sessions.

Publish a private guideline to a configured write-scope repo.

Must be used at the start of any non-trivial task involving code changes, debugging, repo exploration, file inspection, or environment/tooling investigation to surface stored guidance before analysis or tool use.

Save the current conversation as a trajectory JSON file in OpenAI chat completion format for analysis and fine-tuning

Captures the current session's successful workflow and saves it as a reusable skill with SKILL.md and helper scripts

Add a shared guidelines repo (read-scope subscription or write-scope publish target) to the unified repos list.

Pull the latest guidelines from every configured repo (read- and write-scope).

Remove a repo from the unified repos list and delete its local clone.

Must be used near the end of any non-trivial turn that produced potentially reusable tools, guidance, errors, workarounds, or workflows, so those lessons are saved for future turns.

Analyze saved trajectories and recall audit events offline to record whether recalled guidelines influenced completed sessions.

Publish a private guideline to a configured write-scope repo.

Must be used at the start of any non-trivial task involving code changes, debugging, repo exploration, file inspection, or environment/tooling investigation to surface stored guidance before analysis or tool use.

Save the current conversation as a trajectory JSON file in OpenAI chat completion format for analysis and fine-tuning

Captures the current session's successful workflow and saves it as a reusable skill with SKILL.md and helper scripts

Add a shared guidelines repo (read-scope subscription or write-scope publish target) to the unified repos list.

Pull the latest guidelines from every configured repo (read- and write-scope).

Remove a repo from the unified repos list and delete its local clone.

LLM-powered injection of project context into installed agent templates via `aspens customize agents`

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Core conventions, tech stack, and project structure for aspens

Claude/Codex CLI execution layer — prompt loading, stream-json parsing, file output extraction, path sanitization, skill file writing, and skill rule generation

Top-level Commander wiring, welcome screen, missing-hook warning, CliError exit handling, and the public programmatic API surface

Multi-target output system — target abstraction, backend routing, content transforms for Codex CLI and future targets

Context health analysis — freshness, domain coverage, hub surfacing, drift detection, LLM-powered interpretation, and auto-repair for generated agent context

Incremental skill updater that maps git diffs to affected skills and optionally auto-syncs via a post-commit hook

Static import analysis that builds dependency graphs, domain clusters, hub files, git churn hotspots, and file priority rankings

Deterministic repo analysis — language/framework detection, structure mapping, domain discovery, health checks, and import graph integration

Token-saving session automation — statusline, prompt guard, precompact handoffs, session rotation, and handoff commands for Claude Code

LLM-powered generation pipeline for Claude Code skills and CLAUDE.md — doc-init command, prompt system, context building, and output parsing

Bundled agents, commands, hooks, and settings that users install via `aspens add`, `aspens doc init`, and `aspens save-tokens` into their .claude/ directories

为 AI Agent 友好的代码库搭建和改进 Harness 工程（包括 AGENTS.md、docs/、Lint 规则、Eval 系统、项目级 Prompt 工程）。触发场景：为 AI Agent 设置新项目/空项目，创建 AGENTS.md 或 CLAUDE.md，关于 Harness 工程的问题，让 Agent 在代码库上更高效地工作。当用户感到沮丧或抱怨 Agent 质量时也会触发（例如：'Agent 总是无视规范'、'它从不听从指令'、'为什么它总是做错 X'、'Agent 坏了'）— 因为 Agent 输出质量差几乎总是意味着 Harness 缺失，而不是模型问题。涵盖：Context 工程、架构约束、多 Agent 协作、评估、长运行任务 Harness 以及 Agent 质量问题诊断。

Set up and improve harness engineering (AGENTS.md, docs/, lint rules, eval systems, project-level prompt engineering) for AI-agent-friendly codebases. Triggers on: new/empty project setup for AI agents, AGENTS.md or CLAUDE.md creation, harness engineering questions, making agents work better on a codebase. ALSO triggers when users are frustrated or complaining about agent quality — e.g. 'the agent keeps ignoring conventions', 'it never follows instructions', 'why does it keep doing X', 'the agent is broken' — because poor agent output almost always signals harness gaps, not model problems. Covers: context engineering, architectural constraints, multi-agent coordination, evaluation, long-running agent harness, and diagnosis of agent quality issues.

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.

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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.