From text-corpus-analysis
Audit what local LLM runtimes are installed (Ollama, llama.cpp, vLLM, LM Studio) and suggest/install a model suitable for corpus analysis tasks — classification, labeling, summarization. Use when a skill in this plugin wants a local-LLM lane but nothing is available, or when the user wants to move a cloud workload local for cost/privacy.
npx claudepluginhub danielrosehill/claude-code-plugins --plugin text-corpus-analysisThis skill uses the workspace's default tool permissions.
Get a local model ready for text-corpus work.
Provides UI/UX resources: 50+ styles, color palettes, font pairings, guidelines, charts for web/mobile across React, Next.js, Vue, Svelte, Tailwind, React Native, Flutter. Aids planning, building, reviewing interfaces.
Fetches up-to-date documentation from Context7 for libraries and frameworks like React, Next.js, Prisma. Use for setup questions, API references, and code examples.
Analyzes multiple pages for keyword overlap, SEO cannibalization risks, and content duplication. Suggests differentiation, consolidation, and resolution strategies when reviewing similar content.
Share bugs, ideas, or general feedback.
Get a local model ready for text-corpus work.
Audit: check for ollama, llama.cpp, vllm, LM Studio, llamafile. Check GPU (nvidia-smi, rocm-smi) for VRAM budget.
Recommend by task profile:
| Task profile | Suggested models |
|---|---|
| Classification / labeling into known categories (1-3k tokens, short answer) | llama3.1:8b, qwen2.5:7b, gemma2:9b — 8-16GB VRAM |
| Semantic judgment on longer docs | qwen2.5:14b, llama3.1:70b (quantized) — 24GB+ VRAM |
| Embedding generation | nomic-embed-text, mxbai-embed-large via Ollama, or all-MiniLM-L6-v2 via sentence-transformers (CPU-fast) |
| Topic labeling (one-off, 20-50 calls) | Any 7B+ instruct model |
Install (Ollama preferred):
ollama pull qwen2.5:7b-instruct
ollama pull nomic-embed-text
Verify with a test prompt, measure throughput (tokens/sec). For a 10k-doc classification run, calculate wall-clock: (N docs × ~300 output tokens) / throughput. If >8 hours, consider cloud instead.
Expose via Ollama's OpenAI-compatible endpoint at http://localhost:11434/v1 — skills in this plugin can hit it with the OpenAI SDK.
See docs/local-llm-sizing.md if present for VRAM/throughput guidance.