{"id":"plutolei-paperbanana-skill","name":"paperbanana-skill","af_score":65.3,"security_score":51.5,"reliability_score":40.0,"what_it_does":"Provides Claude Code skill definitions (and an underlying Python package) to generate publication-quality academic diagrams, statistical plots, and slide decks from text or structured data, using a multi-agent pipeline with evaluation/self-critique and provider fallback across multiple LLM/VLM/image providers.","best_when":"You want consistent academic visuals quickly, and you can supply prompts/descriptions (and optionally data/PDF) where iterative evaluation/fallback can improve quality.","avoid_when":"You need deterministic outputs, formal SLAs, or strict governance that disallows automated self-critique loops and multi-provider network calls.","last_evaluated":"2026-03-30T15:35:42.520773+00:00","has_mcp":false,"has_api":false,"auth_methods":["API keys for providers via environment variables (GOOGLE_API_KEY, ANTHROPIC_API_KEY, OPENAI_API_KEY, AWS credentials, OPENROUTER_API_KEY)","Interactive setup wizard for configuring API keys"],"has_free_tier":true,"known_gotchas":["Outputs may be marked UNREVIEWED when the critic cannot parse/evaluate; manual review may be needed.","Provider capability differences (e.g., Claude VLM does not support image generation per README).","Non-determinism across multi-provider fallback chains and iterative loops.","Long/slow runs depending on iterations/auto/refinement settings; tuning may be required."],"error_quality":null}