| name | choose-approach |
| description | Decide whether a corpus analysis task should use classical NLP, a local LLM, or a cloud LLM (OpenRouter) given corpus size, task complexity, and cost tolerance. Use first, before any other skill in this plugin, especially when the corpus is large (thousands+ of documents) or when an LLM pass could get expensive. |
Choose Approach
Decision helper. Picks the right execution lane for a text-analysis task.
When to use
- Before any skill in this plugin is applied to more than a few hundred documents.
- When the user says "analyze all my notes", "categorize everything", "run NER on the corpus" — i.e. any whole-corpus pass.
- When cost could exceed a few dollars if done naively.
The three lanes
| Lane | Strengths | Weaknesses | Cost model |
|---|
| Classical NLP (spaCy, scikit-learn, gensim, TextBlob, BlackLab) | Deterministic, fast, free, scales to millions of docs, strong for frequency/NER/TF-IDF/LDA | Brittle on short or noisy text; no semantic judgment | CPU time only |
| Local LLM (Ollama: llama3.1, qwen2.5, gemma2) | No per-token cost, private, good for classification/labeling on small batches | Slow on big corpora (GPU-bound), lower ceiling than frontier models | Electricity + wall-clock time |
| Cloud LLM (OpenRouter → Claude, GPT, Gemini, DeepSeek, Llama) | Best judgment, handles nuance, parallelizable | Per-token cost — dangerous on 10k+ docs without planning | $ per M tokens |
Procedure
- Size the corpus: count documents, estimate avg tokens/doc (approx
words × 1.3), compute total input tokens.
- Classify the task as one of:
- Mechanical (frequency, length, NER with known list, metadata corr) → Classical NLP
- Semantic judgment (categorize, taxonomy, synonym-cluster, topic-label) → LLM (local or cloud)
- Hybrid (candidate extraction + labeling) → Classical NLP first, LLM on reduced set
- If LLM: estimate cost at three tiers using current OpenRouter pricing (always fetch live — prices change):
- Cheap: DeepSeek V3 / Gemini Flash / Llama 3.3 70B (~$0.05-0.30 per M input tokens)
- Mid: Claude Haiku, GPT-4o-mini (~$0.25-1.00 per M)
- Premium: Claude Sonnet/Opus, GPT-4o (~$3-15 per M)
- Apply cost-reduction tactics before running:
- Sample, don't sweep — 500 stratified docs usually derive the same taxonomy as 50,000.
- Two-pass: cheap model labels everything, premium model re-labels only the low-confidence residue.
- Map-reduce: summarize per-doc cheaply, then cluster summaries.
- Embeddings + clustering instead of LLM for grouping: one embedding call per doc (very cheap) + k-means is orders of magnitude cheaper than asking an LLM to compare docs.
- Prompt caching (Anthropic, OpenAI): if the same system prompt + taxonomy ships with every call, cache it.
- Present the user with: recommended lane, estimated cost range, and at least one cheaper fallback. Ask for go-ahead before any run >$2.
Output shape
Task: <restatement>
Corpus: N docs, ~M tokens total
Recommended lane: <Classical NLP | Local LLM | Cloud LLM via OpenRouter>
Recommended model (if LLM): <name> — est. $X.XX for full corpus
Cheaper fallback: <sampling strategy OR cheaper model> — est. $Y.YY
Reasoning: <2-3 sentences>
Hard rules
- Never launch an LLM pass over >1k documents without showing a cost estimate first.
- Never use a premium model for a task a cheap model can do (labeling into known categories, routing).
- For pure mechanical tasks, never suggest an LLM. spaCy/regex is the right answer.
