skills/lingzhi227__agent-research-skills

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Lingzhi Yang 882d24ff3f Add skills design spec and implementation checklist

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

2026-02-20 19:53:15 -05:00

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Research Paper Writing: Implemented Claude Skills

Implementation record for 30 skills deployed at ~/.claude/skills/. Companion to SKILLS_DESIGN.md which contains the original design specifications.

Summary

Category	Count	With Scripts	Prompt-Only
Research Discovery & Planning	6	4	2
Method Design	4	0	4
Experiment Pipeline	4	2	2
Paper Writing	4	1	3
Figures, Tables & Citations	4	4	0
LaTeX & Compilation	3	3	0
Review & Polish	5	3	2
Total	30	17	13

Scripts total: 27 Python + 7 CJS (Excalidraw) across 15 skill directories.

Phase 0: Research Discovery & Planning

1. `deep-research` — Systematic Literature Survey

Design ref: Pre-dates SKILLS_DESIGN.md (not in S01-S28). Most script-heavy skill.

How it works:

Prompt: Orchestrates a 6-phase literature survey: Frontier (latest conferences) → Survey (35-80 papers) → Deep Dive (8-15 detailed reads) → Code & Tools (GitHub extraction) → Synthesis (cross-paper analysis) → Compilation (final report).
Scripts (7):
- search_semantic_scholar.py — Semantic Scholar API search, returns JSONL with title/authors/year/venue/abstract/citations
- search_arxiv.py — arXiv API search via arxiv package
- download_papers.py — Download PDFs from URLs or arXiv IDs
- extract_pdf.py — Extract text from PDF using PyMuPDF
- paper_db.py — Merge, deduplicate, and manage paper databases (JSONL format)
- bibtex_manager.py — Convert JSONL paper records to BibTeX entries
- compile_report.py — Assemble final survey report from phase outputs

Usage pattern: Scripts handle API calls, PDF processing, and data management. Prompt guides Claude through analysis, synthesis, and gap identification at each phase.

2. `literature-search` — Academic Paper Search

Design ref: S03

How it works:

Prompt: Expands user query into 2-4 complementary searches, runs across 3+ APIs, merges and deduplicates results, ranks by citations (0.3) + recency (0.3) + venue quality (0.2) + relevance (0.2).
Scripts (4 own + shared from deep-research):
- search_crossref.py — CrossRef API search with BibTeX generation, type mapping (article/inproceedings/book). Stdlib-only. New.
- download_arxiv_source.py — Search arXiv by title, download source tarball, extract .tex files. Stdlib-only (urllib + xml.etree). New.
- search_openalex.py — OpenAlex API with citation count and year filtering
- Also uses: deep-research/scripts/search_semantic_scholar.py, search_arxiv.py, paper_db.py, bibtex_manager.py

Usage pattern: Scripts call search APIs and return structured JSONL. Prompt handles query expansion, result ranking, and relevance filtering.

3. `literature-review` — Multi-Perspective Literature Review

Design ref: S04

How it works:

Prompt: Generates 3-5 expert personas from different research perspectives. Each persona conducts a multi-turn grounded Q&A conversation (3-5 turns). All conversations are synthesized into a unified knowledge base with inline citations.
Scripts: Shares search scripts from literature-search and deep-research.

Usage pattern: Prompt-driven role-play simulation. Scripts provide the search backbone for grounding expert answers in real literature.

4. `idea-generation` — Research Idea Generation

Design ref: S02

How it works:

Prompt: Generates 3-5 diverse research ideas, each with Name/Title/Experiment plan. Iterative reflection (up to 5 rounds) to refine. Scores each idea on Interestingness, Feasibility, Novelty (1-10 scale).
Scripts (1):
- novelty_check.py — Searches Semantic Scholar for similar work, evaluates overlap in multiple rounds

Usage pattern: Prompt generates and refines ideas. Script validates novelty against existing literature.

5. `novelty-assessment` — Research Novelty Evaluation

Design ref: S27

How it works:

Prompt: Adopts a harsh critic persona. Runs up to 10 rounds of search-evaluate loops. Final output is a binary Novel/Not Novel decision with justification. Identifies the most similar existing papers and explains differentiation.
Scripts: Shares idea-generation/scripts/novelty_check.py and deep-research/scripts/search_semantic_scholar.py.

Usage pattern: Prompt drives the adversarial evaluation. Scripts perform iterative literature searches.

6. `research-planning` — Research Plan Design

Design ref: S01

How it works:

Prompt: 4-stage planning framework: Overall Plan → Architecture Design (UML) → Logic Design (task list) → Configuration. Outputs paper structure, section-by-section plan, dependency-ordered task graph, baselines, datasets, metrics, and risk flags.
Scripts: None.

Usage pattern: Purely prompt-driven. Claude produces structured research plans as JSON or Markdown.

Phase 1: Method Design

7. `atomic-decomposition` — Atomic Concept Decomposition

Design ref: S08

How it works:

Prompt: Decomposes a complex research idea into atomic, self-contained concepts. For each concept, performs a Paper Survey (extract math formula from papers) and Code Survey (find implementation in repos). Creates bidirectional mapping: math_formula ↔ code_implementation.
Scripts: None.

Usage pattern: Purely prompt-driven. Claude reads papers and code to build a structured knowledge base of atomic definitions.

8. `algorithm-design` — Algorithm Design & Pseudocode

Design ref: S07

How it works:

Prompt: Formalizes algorithms with clear input/output/parameters. Generates LaTeX pseudocode using algorithm + algpseudocode environments. Creates Mermaid UML class diagrams and sequence diagrams. Verifies consistency: every pseudocode step maps to a code module.
Scripts: None.

Usage pattern: Purely prompt-driven. Outputs LaTeX algorithm environments and Mermaid diagram code.

9. `math-reasoning` — Mathematical Reasoning

Design ref: S06

How it works:

Prompt: Six task modes:
- derive — Step-by-step equation derivation with justifications, boxed final result
- prove — Formal theorem proof using appropriate technique (induction, contradiction, construction)
- formalize — Problem setting formalization with variable definitions and assumptions
- stats — Statistical test selection and proper reporting
- notation — Generate notation table with all symbols used in the paper
- verify — Check mathematical correctness of existing derivations
Scripts: None.

Usage pattern: Purely prompt-driven. Outputs LaTeX math notation.

10. `symbolic-equation` — Scientific Equation Discovery

Design ref: S28

How it works:

Prompt: Implements the LLM-SR framework: LLM-guided evolutionary search over symbolic expression space. Multi-island algorithm with softmax-based cluster sampling for diversity. Island reset mechanism prevents premature convergence. LLM proposes candidate equations, evaluates fitness against data, iteratively improves.
Scripts: None.

Usage pattern: Purely prompt-driven. Claude generates and evaluates candidate equations in an evolutionary loop.

Phase 2: Experiment Pipeline

11. `experiment-design` — Experiment Plan Design

Design ref: S11

How it works:

Prompt: 4-stage progressive framework (from AI-Scientist-v2):
- Stage 1: Initial Implementation — basic working baseline on simple dataset
- Stage 2: Baseline Tuning — hyperparameters on 2+ datasets, no architecture changes
- Stage 3: Creative Research — novel improvements on 3+ datasets
- Stage 4: Ablation Studies — systematic component analysis
Scripts (1):
- design_experiments.py — Generates experiment design JSON/Markdown: baseline list, ablation matrix, hyperparameter grid, metric selection by task type (classification/regression/generation/detection/segmentation/retrieval), total run estimation. Stdlib-only. New.

Usage pattern: Script generates the structural experiment plan. Prompt fills in domain-specific details and rationale.

12. `experiment-code` — Experiment Code Writing

Design ref: S09

How it works:

Prompt: Three actions:
- generate — Create complete training/evaluation pipeline with logging and figure generation
- improve — Read results, reflect on what worked, apply targeted edits, re-run and compare
- debug — Identify root cause, apply minimal fix, up to 4 retries
Enforces constraints: no placeholder code, must use real datasets, must generate figures, PyTorch/scikit-learn only.
Scripts: None.

Usage pattern: Purely prompt-driven. Claude writes, executes, and iteratively improves experiment code.

13. `code-debugging` — Code Debugging

Design ref: S10

How it works:

Prompt: Structured error analysis pipeline:
1. Categorize error (SyntaxError, ImportError, RuntimeError, TimeoutError, OutputError, LogicError)
2. Analyze root cause from traceback
3. Apply targeted fix (up to 4 retries)
4. Reflect: explain error, identify lines, describe fix, note patterns to avoid
Scripts: None.

Usage pattern: Purely prompt-driven. Claude follows the categorize→analyze→fix→reflect workflow.

14. `data-analysis` — Statistical Data Analysis

Design ref: S12

How it works:

Prompt: Generates analysis code in 7 sections (IMPORT → LOAD DATA → DATASET PREPARATIONS → DESCRIPTIVE STATISTICS → PREPROCESSING → ANALYSIS → SAVE). Then runs a 4-round code review: Round 1 (code flaws) → Round 2 (data handling) → Round 3 (per-table) → Round 4 (cross-table completeness). Statistical test selection table guides appropriate test choice.
Scripts (2):
- stat_summary.py — Loads CSV/JSON, detects data types, recommends statistical tests (t-test, Mann-Whitney, Wilcoxon, ANOVA, Kruskal-Wallis), computes effect sizes (Cohen's d), outputs significance stars. Requires numpy + scipy. New.
- format_pvalue.py — Formats p-values as text, significance stars (*/**/***/ns), LaTeX notation, or JSON. Supports batch processing from CLI values, CSV, or stdin. Stdlib-only. New.

Usage pattern: Scripts handle statistical computation and formatting. Prompt performs the 4-round review and generates the full analysis code.

Phase 3: Paper Writing

15. `paper-writing-section` — Section-by-Section Writing

Design ref: S15

How it works:

Prompt: Writes each paper section with section-specific guidance:

Section	Guidance
Abstract	TL;DR → why hard → contribution → how verified. Single paragraph.
Introduction	Longer abstract; list contributions as bullet points.
Background	Problem setting with formal notation; define all symbols.
Related Work	Compare and contrast, not just describe. Organize by theme.
Methods	Precise equations; what we do AND why.
Experiments	Datasets, metrics, hyperparameters. No hallucinated numbers.
Results	Only from actual logs. Include ablations and all figures.
Discussion	Brief recap → limitations → future work.

Two-pass refinement: Pass 1 (fix errors, unenclosed math, broken refs, hallucinated numbers) → Pass 2 (remove redundancies, compress, smooth transitions).

Scripts: None.

Usage pattern: Purely prompt-driven. Claude reads existing materials, writes the section, then self-refines in two passes.

16. `related-work-writing` — Related Work Section

Design ref: S05

How it works:

Prompt: 4-step process:
1. Analyze paper's key contributions and novelty claims
2. Organize literature into thematic clusters
3. Write each theme paragraph: topic sentence → describe key works → compare/contrast with this paper
4. Refine: verify citation reasons are clear, novelty is explicit, all cite keys resolve
Scripts: None.

Usage pattern: Purely prompt-driven. Focused specifically on the Related Work section with compare-and-contrast emphasis.

17. `survey-generation` — Survey Paper Generation

Design ref: S24

How it works:

Prompt: AutoSurvey pipeline:
1. Collect 50-200 papers via Semantic Scholar/arXiv
2. Generate N outlines in parallel, merge best elements
3. RAG-based subsection writing: retrieve relevant papers per subsection, generate with inline citations
4. Validate citations: check titles match, verify claims are supported
5. Enhance local coherence: read adjacent sections, refine transitions
6. Convert paper title citations to BibTeX \cite{key} format
Scripts: Shares deep-research/scripts/search_semantic_scholar.py.

Usage pattern: Prompt orchestrates the multi-step RAG pipeline. Script provides the search backend.

18. `paper-to-code` — Paper to Runnable Code

Design ref: S25

How it works:

Prompt: Paper2Code 3-stage pipeline:
1. Planning — Overall plan, architecture design (UML), task breakdown, configuration extraction
2. Analysis — Per-file detailed logic analysis
3. Coding — Dependency-ordered code generation, each file sees all previously generated files
4. Debugging — If execution fails, identify root cause and apply fixes
Scripts: None.

Usage pattern: Purely prompt-driven. Claude reads the paper, designs the architecture, then generates code file by file in dependency order.

Phase 4: Figures, Tables & Citations

19. `figure-generation` — Scientific Figure Generation

Design ref: S13

How it works:

Prompt: 3-phase pipeline:
1. Query Expansion — expand user description into step-by-step coding specifications
2. Code Generation with Execution Loop — generate matplotlib/seaborn script, execute, fix errors (up to 4 retries)
3. Visual Refinement — read generated PNG, inspect using VLM feedback, generate corrections
Scripts (1):
- figure_template.py — Generates starter matplotlib code for 10 figure types: bar, line, heatmap, scatter, training_curve, ablation, radar, violin, tsne, attention. The last 4 templates were added in the upgrade. Updated.

Usage pattern: Script generates code scaffolds for common figure types. Prompt handles the iterative refinement loop with VLM feedback.

20. `table-generation` — LaTeX Table Generation

Design ref: S14

How it works:

Prompt: Converts JSON/CSV experiment results to publication-ready LaTeX tables with booktabs styling, bold best results, proper captions and labels. Supports comparison, ablation, descriptive, and multi-dataset table types.
Scripts (1):
- results_to_table.py — Converts JSON/CSV data to LaTeX. 4 table types: comparison (methods × metrics), ablation (variants × metrics), descriptive (dataset stats), multi-dataset (methods × datasets × metrics). Flags: --bold-best, --significance (p-value stars), --underline-second (second-best results). Updated.

Usage pattern: Script handles the mechanical conversion. Prompt decides table layout and which results to include.

21. `citation-management` — BibTeX Citation Management

Design ref: S17

How it works:

Prompt: Four actions:
- harvest — Iterative citation harvesting: scan draft for uncited claims, search Semantic Scholar, add candidate BibTeX entries (up to 20 rounds)
- validate — Pre-compilation check: every \cite{key} must exist in .bib, every \includegraphics must exist
- add — Add a specific paper by title or DOI
- format — Standardize and deduplicate .bib file
Scripts (2 own + shared):
- harvest_citations.py — Scans .tex for sentences lacking \cite, searches Semantic Scholar API, outputs candidate BibTeX. Stdlib-only. New.
- validate_citations.py — Checks cite keys vs .bib, label vs ref consistency, figure file existence. --fix mode generates placeholder entries for missing keys. Updated.
- Also uses: deep-research/scripts/bibtex_manager.py, search_semantic_scholar.py

Usage pattern: Scripts automate search and validation. Prompt handles citation selection and integration into paper text.

22. `backward-traceability` — Numeric Value Traceability

Design ref: S23

How it works:

Prompt: Ensures every number in the final PDF traces to the exact code line that produced it. Workflow:
1. Tag code outputs with \hypertarget{label}{value}
2. Reference in paper with \hyperlink{label}{value}
3. Use \num{formula} for derived values (compile-time evaluation)
4. Generate appendix code listing with hypertarget anchors
5. Verify all hyperlinks resolve correctly
Scripts (1):
- ref_numeric_values.py — Two modes: --scan (report all hypertarget/hyperlink usage, orphan references, unreferenced numbers) and --verify (cross-reference integrity between .tex and code output, value mismatch detection). Stdlib-only. New.

Usage pattern: Script scans and verifies traceability. Prompt guides how to add hypertarget/hyperlink tags.

Phase 5: LaTeX & Compilation

23. `latex-formatting` — LaTeX Formatting & Templates

Design ref: S16

How it works:

Prompt: Three actions:
- setup — Create project directory with conference template (ICML, ICLR, NeurIPS, AAAI, ACL)
- fix — Fix common LaTeX issues: unescaped characters, math mode errors, float placement, cross-references
- check — Pre-submission validation: word count, section structure, anonymization, citation consistency
Scripts (2):
- latex_checker.py — Checks word count, section completeness (flags missing expected sections), citation/figure/equation counts, venue-specific rules, anonymization. --fix mode calls clean_latex.py after checking. Updated.
- clean_latex.py — Replaces special characters with LaTeX equivalents (28 special chars + 20 non-UTF8 chars). Skips math environments, comments, command definitions, tabular environments, and LaTeX commands. Stdlib-only. New.

Usage pattern: Scripts handle automated checking and cleaning. Prompt does venue-specific template setup and complex formatting fixes.

24. `paper-compilation` — LaTeX Compilation

Design ref: S22

How it works:

Prompt: Full compilation pipeline: pdflatex → bibtex → pdflatex → pdflatex. Pre-compilation validation, up to 5 rounds of error correction, post-compilation report (page count, warnings, style issues).
Scripts (2):
- compile_paper.py — Runs the full pdflatex+bibtex pipeline, optional chktex style checking. --auto-fix flag runs fix_latex_errors.py + recompile up to 3 rounds automatically. Updated.
- fix_latex_errors.py — Parses pdflatex .log files, classifies errors (undefined commands, missing math mode, mismatched environments, missing files), applies automated fixes: HTML tag conversion, environment balancing, missing figure commenting. --dry-run mode for preview. Stdlib-only. New.

Usage pattern: Scripts automate the compile-fix-recompile cycle. Prompt handles complex errors that require understanding paper content.

25. `excalidraw-skill` — Excalidraw Diagramming

Design ref: Not in S01-S28. Uses MCP tools, not traditional scripts.

How it works:

Prompt: Programmatic canvas control via MCP Server tools. Mandatory quality gate after every diagram: check text truncation, element overlap, arrow crossing, spacing, and readability. Workflows include: Draw (plan grid → create elements → bind arrows → verify), Iterative Refinement (screenshot → evaluate → fix), File I/O (export/import .excalidraw), and Sharing (export to excalidraw.com URL).
Scripts (7 CJS): MCP server implementation files (not called directly by users).

Usage pattern: Claude calls MCP tools (create_element, batch_create_elements, describe_scene, get_canvas_screenshot) to manipulate a live Excalidraw canvas in the browser.

Phase 6: Review & Polish

26. `self-review` — Automated Paper Review

Design ref: S18

How it works:

Prompt: Simulates peer review using the NeurIPS review form. Three independent reviewer personas (harsh-fair, harsh-critical, open-minded) each produce a full review. Reflection refinement loop (up to 3 rounds). Reviews are aggregated into a meta-review with averaged scores. Scores: Originality, Quality, Clarity, Significance, Soundness, Presentation, Contribution (1-4), Overall (1-10).
Scripts (2):
- extract_pdf_text.py — Extracts raw text from PDF, outputs as plain text or markdown
- parse_pdf_sections.py — Parses PDF into structured sections using PyMuPDF font-size analysis. Detects title (largest font), headings (ALL CAPS or larger font), and body text. Outputs {title, pages, sections: [{name, text, page}]}. Requires pymupdf. New.

Usage pattern: Scripts extract paper content from PDF. Prompt runs 3 independent reviews, refines, and aggregates.

27. `paper-revision` — Paper Revision from Reviews

Design ref: S20

How it works:

Prompt: 5-step process:
1. Parse reviewer concerns — extract, classify (major/minor), map to specific paper sections, prioritize
2. Plan revisions — create mapping: Concern → Section → Action → New Content
3. Execute revisions — read section, apply edits, run additional experiments if needed, mark changes with \revised{}
4. Verify improvements — re-run self-review, check all concerns addressed, check page count
5. Write revision summary — list all changes with cross-references to reviewer concerns
Scripts: None.

Usage pattern: Purely prompt-driven. Claude systematically addresses each reviewer concern.

28. `rebuttal-writing` — Rebuttal Writing

Design ref: S19

How it works:

Prompt: Extracts reviewer concerns one by one. For each concern, generates a response following the pattern: Acknowledge → Respond with evidence → Describe what was done (not what will be done). Outputs formatted rebuttal with ## Reviewer #N / **Concern #N** / **Author Response** structure.
Scripts: None.

Usage pattern: Purely prompt-driven. Emphasis on evidence-based responses to specific concerns.

29. `slide-generation` — Presentation Slide Generation

Design ref: S26

How it works:

Prompt: Converts a completed paper into Beamer LaTeX slides or poster. Standard flow: extract key content → design 15-20 slide structure → generate Beamer code → simplify for presentation (max 1 key message per slide, max 6 bullets, large figures). Optional poster layout with 4-column structure.
Scripts (1):
- extract_paper_elements.py — Parses .tex (resolves \input{} directives), extracts title, authors, abstract, sections (with hierarchy), figures (path + caption + label), equations, and tables. Generates complete Beamer skeleton or raw JSON. Supports theme selection. Stdlib-only. New.

Usage pattern: Script extracts paper structure and generates slide skeleton. Prompt fills in content and optimizes the narrative flow.

30. `paper-assembly` — End-to-End Paper Orchestrator

Design ref: S21

How it works:

Prompt: Manages the full 9-phase paper pipeline: literature → planning → code → results → figures → tables → bibliography → sections → compilation. Supports checkpointing after each phase and resumption. Quality gates verify outputs before proceeding. State propagation passes results to downstream phases.
Scripts (1):
- assembly_checker.py — Scans a paper project directory, checks completeness of all 9 pipeline phases, analyzes .tex section coverage (abstract, introduction, method, experiment, conclusion), verifies citation cross-references, reports missing artifacts, suggests next steps with skill recommendations. Stdlib-only. New.

Usage pattern: Script assesses current pipeline state. Prompt orchestrates calls to other skills in dependency order.

Skill Dependency Graph

Every SKILL.md includes a ## Related Skills section linking upstream, downstream, and complementary skills. The full graph:

deep-research ──→ literature-search ──→ citation-management ──→ paper-compilation
                  literature-review      related-work-writing     latex-formatting
                  novelty-assessment     survey-generation

idea-generation ──→ research-planning ──→ experiment-design ──→ experiment-code
                    atomic-decomposition                        code-debugging

experiment-code ──→ data-analysis ──→ figure-generation ──→ paper-writing-section
                                      table-generation      related-work-writing
                                      backward-traceability

paper-writing-section ──→ latex-formatting ──→ paper-compilation ──→ self-review
citation-management                                                   paper-revision
                                                                      rebuttal-writing

paper-assembly (orchestrator) ──→ all skills above ──→ slide-generation

Scripts Inventory

New Scripts (12)

Script	Skill	Lines	Dependencies	Source
`search_crossref.py`	literature-search	~260	stdlib	data-to-paper crossref.py
`download_arxiv_source.py`	literature-search	~230	stdlib	AI-Researcher arxiv.py
`harvest_citations.py`	citation-management	~245	stdlib	AI-Scientist citation loop pattern
`clean_latex.py`	latex-formatting	~240	stdlib	data-to-paper clean_latex.py
`fix_latex_errors.py`	paper-compilation	~305	stdlib	data-to-paper + AI-Scientist patterns
`parse_pdf_sections.py`	self-review	~260	pymupdf	ChatReviewer get_paper_from_pdf.py
`ref_numeric_values.py`	backward-traceability	~265	stdlib	data-to-paper ref_numeric_values.py
`stat_summary.py`	data-analysis	~320	numpy, scipy	data-to-paper 4-round review pattern
`format_pvalue.py`	data-analysis	~145	stdlib	data-to-paper pvalue.py
`design_experiments.py`	experiment-design	~275	stdlib	AI-Scientist-v2 4-stage pattern
`assembly_checker.py`	paper-assembly	~290	stdlib	New
`extract_paper_elements.py`	slide-generation	~270	stdlib	New

Updated Scripts (5)

Script	Skill	Change
`validate_citations.py`	citation-management	Added `--fix` auto-fix mode
`compile_paper.py`	paper-compilation	Added `--auto-fix` flag (fix + recompile 3 rounds)
`latex_checker.py`	latex-formatting	Added `--fix` flag (calls clean_latex.py)
`figure_template.py`	figure-generation	Added 4 templates: radar, violin, tsne, attention
`results_to_table.py`	table-generation	Added multi-dataset type, --significance, --underline-second

Pre-existing Scripts (17)

Skill	Scripts
deep-research	search_semantic_scholar.py, search_arxiv.py, download_papers.py, extract_pdf.py, paper_db.py, bibtex_manager.py, compile_report.py
literature-search	search_openalex.py
citation-management	validate_citations.py (pre-upgrade)
figure-generation	figure_template.py (pre-upgrade)
idea-generation	novelty_check.py
latex-formatting	latex_checker.py (pre-upgrade)
paper-compilation	compile_paper.py (pre-upgrade)
self-review	extract_pdf_text.py
table-generation	results_to_table.py (pre-upgrade)
excalidraw-skill	7 CJS files (MCP server)

Verification Results

Tested against real paper: RIGID metamaterials (OpenResearcher/2401.00003, with main.tex + references.bib + 14 figures).

Test	Script	Result
CrossRef API search	`search_crossref.py --query "attention mechanism transformer" --rows 3`	PASS — 3 results
LaTeX error fixer	`fix_latex_errors.py --tex main.tex --dry-run`	PASS — no fixes needed
PDF section parser	`parse_pdf_sections.py --pdf report.pdf --format json`	PASS — title + 7 sections
Citation validator	`validate_citations.py --tex main.tex --bib references.bib`	PASS — 71 citations, 7 unused entries
Beamer skeleton	`extract_paper_elements.py --tex main.tex --format beamer`	PASS — 19 sections, 12 figures, 3 equations
Assembly checker	`assembly_checker.py --dir paper/ --verbose`	PASS — 2/9 phases complete
LaTeX cleaner	`clean_latex.py --input main.tex --dry-run`	PASS — 2 legitimate changes
LaTeX checker	`latex_checker.py main.tex`	PASS — 7515 words, 12 sections
Traceability scan	`ref_numeric_values.py --scan main.tex`	PASS — 0 targets (paper has none)
P-value formatter	`format_pvalue.py --values "0.0001 0.003 0.012 0.048 0.067 0.5" --format latex`	PASS
Experiment design	`design_experiments.py --method "inverse design" --task classification`	PASS
Element extraction	`extract_paper_elements.py --tex main.tex --format json`	PASS

Bugs found and fixed during verification:

parse_pdf_sections.py: len(doc) called after doc.close() — fixed by saving page count before closing
clean_latex.py: Escaped % in comments and # in \newcommand — fixed by adding comment/command-definition skip patterns; removed ~ from escape chars (it's a valid LaTeX tie); added tabular/array to skip environments

29 KiB Raw Blame History Unescape Escape

Research Paper Writing: Implemented Claude Skills

Summary

Phase 0: Research Discovery & Planning

1. deep-research — Systematic Literature Survey

2. literature-search — Academic Paper Search

3. literature-review — Multi-Perspective Literature Review

4. idea-generation — Research Idea Generation

5. novelty-assessment — Research Novelty Evaluation

6. research-planning — Research Plan Design

Phase 1: Method Design

7. atomic-decomposition — Atomic Concept Decomposition

8. algorithm-design — Algorithm Design & Pseudocode

9. math-reasoning — Mathematical Reasoning

10. symbolic-equation — Scientific Equation Discovery

Phase 2: Experiment Pipeline

11. experiment-design — Experiment Plan Design

12. experiment-code — Experiment Code Writing

13. code-debugging — Code Debugging

14. data-analysis — Statistical Data Analysis

Phase 3: Paper Writing

15. paper-writing-section — Section-by-Section Writing

16. related-work-writing — Related Work Section

17. survey-generation — Survey Paper Generation

18. paper-to-code — Paper to Runnable Code

Phase 4: Figures, Tables & Citations

19. figure-generation — Scientific Figure Generation

20. table-generation — LaTeX Table Generation

21. citation-management — BibTeX Citation Management

22. backward-traceability — Numeric Value Traceability

Phase 5: LaTeX & Compilation

23. latex-formatting — LaTeX Formatting & Templates

24. paper-compilation — LaTeX Compilation

25. excalidraw-skill — Excalidraw Diagramming

Phase 6: Review & Polish

26. self-review — Automated Paper Review

27. paper-revision — Paper Revision from Reviews

28. rebuttal-writing — Rebuttal Writing

29. slide-generation — Presentation Slide Generation

30. paper-assembly — End-to-End Paper Orchestrator

Skill Dependency Graph

Scripts Inventory

New Scripts (12)

Updated Scripts (5)

Pre-existing Scripts (17)

Verification Results

29 KiB

Raw Blame History

1. `deep-research` — Systematic Literature Survey

2. `literature-search` — Academic Paper Search

3. `literature-review` — Multi-Perspective Literature Review

4. `idea-generation` — Research Idea Generation

5. `novelty-assessment` — Research Novelty Evaluation

6. `research-planning` — Research Plan Design

7. `atomic-decomposition` — Atomic Concept Decomposition

8. `algorithm-design` — Algorithm Design & Pseudocode

9. `math-reasoning` — Mathematical Reasoning

10. `symbolic-equation` — Scientific Equation Discovery

11. `experiment-design` — Experiment Plan Design

12. `experiment-code` — Experiment Code Writing

13. `code-debugging` — Code Debugging

14. `data-analysis` — Statistical Data Analysis

15. `paper-writing-section` — Section-by-Section Writing

16. `related-work-writing` — Related Work Section

17. `survey-generation` — Survey Paper Generation

18. `paper-to-code` — Paper to Runnable Code

19. `figure-generation` — Scientific Figure Generation

20. `table-generation` — LaTeX Table Generation

21. `citation-management` — BibTeX Citation Management

22. `backward-traceability` — Numeric Value Traceability

23. `latex-formatting` — LaTeX Formatting & Templates

24. `paper-compilation` — LaTeX Compilation

25. `excalidraw-skill` — Excalidraw Diagramming

26. `self-review` — Automated Paper Review

27. `paper-revision` — Paper Revision from Reviews

28. `rebuttal-writing` — Rebuttal Writing

29. `slide-generation` — Presentation Slide Generation

30. `paper-assembly` — End-to-End Paper Orchestrator