audit-paper-book
Use when you need to detect drift between an existing paper-book companion and a revised version of its source paper, then sync the mechanical pieces (new bib entries, new/changed figures) and report the substantive drift (renamed sections, changed numbers, new theorems, new contributions) for the user to triage. Counterpart to /init-paper-book. Read-only by default; --apply flag opts in to mechanical fixes.
Academic Paper Skill
Generate publication-ready academic research papers as professionally formatted PDFs. This skill covers the full pipeline: gathering inputs â writing/rewriting â figure creation â PDF formatting with reportlab â submission preparation for SSRN/arXiv.
exploring-llm-traces
ABSOLUTE MUST to debug and inspect LLM/AI agent traces using PostHog's MCP tools. Use when the user pastes a trace URL (e.g. /llm-observability/traces/<id>), asks to debug a trace, figure out what went wrong, check if an agent used a tool correctly, verify context/files were surfaced, inspect subagent behavior, investigate LLM decisions, or analyze token usage and costs.
academic-pdf-to-gfm
Convert academic PDF papers to GitHub-renderable GFM markdown with inline figures and correctly formatted math equations. Use this skill when converting research papers, technical reports, or math-heavy PDFs for display on GitHub or GitLab. Also use it when GFM math equations are broken or not rendering on GitHub, when someone asks about the $$-vs-```math decision, when equations look garbled on GitHub, when KaTeX validation is needed, or when investigating why LaTeX renders locally but not on GitHub. Also use when comparing GitHub vs GitLab math rendering, when asking about self-hosting GitLab for math documents, or when looking for a platform that requires less LaTeX workarounds. Covers PDF type detection (Word vs LaTeX vs scanned), tool selection (pymupdf4llm/pdftotext/marker-pdf), image extraction, GitHub math rendering rules ($$-vs-```math decision), GitLab native math support (no workarounds needed), KaTeX validation, and multi-agent adversarial equation verification.
academic-plotting
Generates publication-quality figures for ML papers from research context. Given a paper section or description, extracts system components and relationships to generate architecture diagrams via Gemini. Given experiment results or data, auto-selects chart type and generates data-driven figures via matplotlib/seaborn. Use when creating any figure for a conference paper.
ai-scientist-evaluator
Critically review, score, compare, and rank one or more AI scientist outputs for biology, bioinformatics, computational life science, or adjacent research tasks. Trigger when the user asks to evaluate notebooks, code, figures, analyses, manuscripts, software, or final reports produced by AI scientists; compare multiple AI scientists on the same task; judge publication readiness; or audit rigor, reproducibility, novelty, and task completion. Do not use this skill to perform the original research task itself unless the user is explicitly asking for a reviewer-style audit of already produced outputs.
academic-paper
Use when starting or adapting an A4 single-column OpenPress academic / research paper starter for conference drafts, journal preprints, course papers, technical reports, or thesis-style chapters with abstract, numbered sections, figures, tables, and a references list.