lingzhi227
31 skills with substantive source documentation and an official publisher or observed installation demand. Read each skill’s original documentation through its card.
lingzhi227algorithm-design
Design algorithms with LaTeX pseudocode and UML diagrams. Generate algorithmic environments, Mermaid class/sequence diagrams, and ensure consistency between pseudocode and implementation. Use when formalizing methods for a paper.
- installations
- 2
- GitHub Stars
- 390
- Mis à jour
- 27 févr.
lingzhi227atomic-decomposition
Decompose research ideas into atomic, self-contained concepts with bidirectional math-code mapping. For each concept, extract the math formula from papers and find code implementations. Use for complex system papers requiring formal grounding.
- installations
- 2
- GitHub Stars
- 390
- Mis à jour
- 27 févr.
lingzhi227backward-traceability
Make every number in the final PDF traceable to the exact code line that produced it. Uses \hypertarget/\hyperlink LaTeX commands and \num{formula} evaluated at compile time. Use for reproducibility and data integrity verification.
- installations
- 2
- GitHub Stars
- 407
- Mis à jour
- 27 févr.
lingzhi227citation-management
Manage BibTeX citations for LaTeX papers. Harvest missing citations from a draft using Semantic Scholar, validate cite keys against .bib files, deduplicate entries, and format bibliography. Use when working with references, BibTeX, or citations.
- installations
- 2
- GitHub Stars
- 390
- Mis à jour
- 27 févr.
lingzhi227code-debugging
Debug experiment code with structured error analysis. Categorize errors, apply targeted fixes with retry logic, and use reflection to prevent recurring issues. Use when experiment code fails or produces incorrect results.
- installations
- 2
- GitHub Stars
- 390
- Mis à jour
- 27 févr.
lingzhi227data-analysis
Generate statistical analysis code with 4-round review. Select appropriate statistical tests, interpret results, and produce analysis reports with p-values, effect sizes, and confidence intervals. Use when analyzing experimental data for a paper.
- installations
- 2
- GitHub Stars
- 390
- Mis à jour
- 27 févr.
lingzhi227deep-research
Conduct systematic academic literature reviews in 6 phases, producing structured notes, a curated paper database, and a synthesized final report. Output is organized by phase for clarity.
- installations
- 2
- GitHub Stars
- 390
- Mis à jour
- 27 févr.
lingzhi227excalidraw-skill
Programmatic canvas toolkit for creating, editing, and refining Excalidraw diagrams via MCP tools with real-time canvas sync. Use when an agent needs to (1) draw or lay out diagrams on a live canvas, (2) iteratively refine diagrams using describescene and getcanvasscreenshot to see its own work, (3) export/import .excalidraw files or PNG/SVG images, (4) save/restore canvas snapshots, (5) convert Mermaid to Excalidraw, or (6) perform element-level CRUD, alignment, distribution, grouping, duplicat
- installations
- 2
- GitHub Stars
- 390
- Mis à jour
- 27 févr.
lingzhi227experiment-design
Design experiment plans with progressive stages — initial implementation, baseline tuning, creative research, and ablation studies. Plan baselines, datasets, hyperparameter sweeps, and evaluation metrics. Use when planning experiments for a research paper.
- installations
- 2
- GitHub Stars
- 390
- Mis à jour
- 27 févr.
lingzhi227figure-generation
Generate publication-quality scientific figures using matplotlib/seaborn with a three-phase pipeline (query expansion, code generation with execution, VLM visual feedback). Handles bar charts, line plots, heatmaps, training curves, ablation plots, and more. Use when the user needs figures, plots, or visualizations for a paper.
- installations
- 2
- GitHub Stars
- 390
- Mis à jour
- 27 févr.
lingzhi227github-research
Explore and analyze GitHub repositories related to a research topic. Reads deep-research output, discovers repos from multiple sources, deeply analyzes code, and produces integration blueprints.
- installations
- 2
- GitHub Stars
- 390
- Mis à jour
- 27 févr.
lingzhi227idea-generation
Generate novel research ideas with iterative refinement and novelty checking against literature. Score ideas on Interestingness, Feasibility, and Novelty. Use when brainstorming research directions or validating idea novelty.
- installations
- 2
- GitHub Stars
- 390
- Mis à jour
- 27 févr.
lingzhi227latex-formatting
Handle LaTeX formatting, templates, and styling for academic papers. Set up conference templates (ICML, ICLR, NeurIPS, AAAI, ACL), fix formatting issues, manage packages, and ensure venue-specific compliance. Use when the user needs to set up a paper template, fix LaTeX formatting, or prepare for submission.
- installations
- 2
- GitHub Stars
- 390
- Mis à jour
- 27 févr.
lingzhi227literature-search
Search academic literature using Semantic Scholar, arXiv, and OpenAlex APIs. Returns structured JSONL with title, authors, year, venue, abstract, citations, and BibTeX. Use when the user needs to find papers, check related work, or build a bibliography.
- installations
- 2
- GitHub Stars
- 390
- Mis à jour
- 27 févr.
lingzhi227math-reasoning
Formal mathematical reasoning for research papers — derive equations, write proofs, formalize problem settings, select statistical tests, and generate LaTeX math notation. Use when the user needs mathematical derivations, theorem proofs, notation tables, or statistical analysis formalization.
- installations
- 2
- GitHub Stars
- 390
- Mis à jour
- 27 févr.
lingzhi227novelty-assessment
Assess research idea novelty through systematic literature search. Multi-round search-evaluate loops with harsh critic persona. Binary novel/not-novel decision with justification. Use before committing to a research direction.
- installations
- 2
- GitHub Stars
- 390
- Mis à jour
- 27 févr.
lingzhi227paper-compilation
Compile LaTeX papers to PDF with automatic error detection, chktex style checking, and citation/reference validation. Runs the full pdflatex + bibtex pipeline. Use when the user wants to compile a paper, fix compilation errors, or debug LaTeX.
- installations
- 2
- GitHub Stars
- 390
- Mis à jour
- 27 févr.
lingzhi227paper-revision
Revise papers based on reviewer feedback. Map reviewer concerns to specific sections, apply targeted edits, run additional experiments if needed, and verify improvements. Use after receiving peer review with revision requests.
- installations
- 2
- GitHub Stars
- 390
- Mis à jour
- 27 févr.
lingzhi227paper-to-code
Convert an ML research paper into a complete, runnable code repository. 3-stage pipeline from Paper2Code — Planning (UML + dependency graph) → Analysis (per-file logic) → Coding (dependency-ordered generation). Use for reproducing paper methods.
- installations
- 2
- GitHub Stars
- 390
- Mis à jour
- 27 févr.
lingzhi227paper-writing-section
Write a specific section of an academic paper (Abstract, Introduction, Background, Related Work, Methods, Experiments, Results, Discussion/Conclusion) with section-specific guidance and two-pass refinement. Use when the user wants to write, draft, or improve a paper section.
- installations
- 2
- GitHub Stars
- 390
- Mis à jour
- 27 févr.
lingzhi227rebuttal-writing
Write point-by-point rebuttals to reviewer comments. Extract concerns from reviews, generate evidence-based responses, and format as a structured rebuttal document. Use after receiving peer review feedback.
- installations
- 2
- GitHub Stars
- 390
- Mis à jour
- 27 févr.
lingzhi227related-work-writing
Write Related Work sections that compare and contrast prior work with your approach. Organize by theme, cite broadly, and explain how your work differs. Use when writing or improving the Related Work section of a paper.
- installations
- 2
- GitHub Stars
- 390
- Mis à jour
- 27 févr.
lingzhi227research-planning
Design research plans and paper architectures. Given a research topic or idea, generate structured plans with methodology outlines, paper structure, dependency-ordered task lists, UML diagrams, and experiment designs. Use when starting a new research project or paper.
- installations
- 2
- GitHub Stars
- 390
- Mis à jour
- 27 févr.
lingzhi227self-review
Automatically review an academic paper using the NeurIPS review form with three reviewer personas, ensemble scoring, and reflection refinement. Extracts text from PDF, runs structured review, and outputs actionable feedback. Use when the user wants to review a paper before submission or get feedback on a draft.
- installations
- 2
- GitHub Stars
- 390
- Mis à jour
- 27 févr.