Every citation verified. Every figure editable. Every number traced to source.

13 composable Claude Code skills turn a one-line idea into a compiled PDF —
real references, editable vector figures, and machine-checked integrity included.
No app. No server. No setup.

🏆 Showcase · ✨ Features · 🧭 Compare · 🖼️ Figure Engine · 🔬 Pipeline · 🚀 Quick Start

4 papers across 4 domains — environmental monitoring, energy forecasting, environmental AI, computer vision — generated fully end-to-end with real citations, editable vector figures, and compiled PDF output.

• v1.1.0 — Claude Code plugin support. Restructured as a proper plugin with .claude-plugin/plugin.json. One-command install, auto-loads on session start.
• v1.0.1 — Soft update notification. check_update.py queries GitHub Releases API on each run (24h cache, silent when up-to-date, never blocks).
• v1.0 — Initial release. 13 skills, end-to-end pipeline, DrawAI hybrid figure engine, MIT License.

# Install — auto-loads on next Claude Code session
git clone https://github.com/Albus-White/spark-to-paper-skills.git ~/.claude/skills/spark-to-paper-skills

Run ts-paper on this proposal.   ← paste your idea, proposal, or data

The orchestrator auto-routes your input, picks the right mode, and runs the full chain.

_{✓ full  ·  ● partial  ·  – none  |  sources: ARS · Idea2Paper · AutoResearchClaw · AI-Scientist · Kosmos · karpathy/autoresearch · auto_research}

The heavy autonomous scientists (AutoResearchClaw, AI-Scientist) match the breadth — but ship as standalone Python products (Docker, Neo4j, tens of thousands of LOC). The lighter skills (ARS, Idea2Paper) don't run experiments or draw figures. Nobody else gives you all of it as drop-in Claude Code skills with editable vector figures.

The capability no other skill suite has. AI image models produce rasters — but a paper needs editable vector figures.

ts-figure-optimize vendors the full DrawAI engine and does not just embed a bitmap. It decomposes and reconstructs the figure:

raster figure (PNG/JPG)
   ├─ SAM3            → segment the layout into regions    (local, no account)
   ├─ PaddleOCR       → read every text run                (local, no account)
   ├─ Box-IR          → build a structured layout IR
   └─ HYBRID build    → pixel-exact render + editable <text> overlay
   │
   └─▶ editable SVG · vector PDF · editable PPTX   (~0.91 SSIM, ~63 editable text boxes)

python skills/ts-figure-optimize/scripts/setup_drawai.py --device cpu   # provision
python skills/ts-figure-optimize/scripts/setup_drawai.py --check-only   # doctor

One spark in. One paper out.

One orchestrator (ts-paper) routes the input, then drives a 7-stage chain plus auto-run experiments:

                         ┌──────────────── optional upstream ────────────────┐
 [corpus.jsonl] ─▶ ts-kg-build ─▶ kg/         (research-pattern KG)
 [raw idea]     ─▶ ts-idea2story ─▶ story + citation seed
                         └──────────────────────────────────────────────────┘
                                                │
 [proposal  OR  proposal + real results]  ─────▶ ts-paper  (Stage 0: ROUTE)
                                                │
   1. ts-paper-plan ──▶ blueprint.json          title · keywords · contributions
   2. ts-paper-cite ──▶ refs.bib                ≥40 REAL refs via WebSearch + Crossref
   3. ts-paper-write ─▶ sections/*.tex          all sections in one holistic pass
   4. ts-paper-refine ▶ right-size + de-AI      scrub + logic self-check
   5. ts-paper-review ▶ adversarial review      multi-reviewer hardening
   6. ts-paper-figure ▶ figures + vectorize     image-model → DrawAI hybrid → editable PDF
   7. ts-paper-latex ─▶ main.pdf                assemble + compile
                                                │
   8. ts-paper-experiment (AUTO) ─▶ run feasible experiments, fill tables, recompile

Four complementary layers — Claude handles judgment, code provides the deterministic backstop.

python skills/ts-paper/scripts/run_gates.py <workdir> all     # nonzero exit = NOT done

Write to whatever venue you pick — content quality is invariant.

Add a venue by dropping a templates/<name>/ dir with template.json + LaTeX assets — no code changes.

13 skill folders; all active.

Option A — Claude Code plugin (recommended)

git clone https://github.com/Albus-White/spark-to-paper-skills.git ~/.claude/skills/spark-to-paper-skills

Auto-loads on next session. Skills available as /spark-to-paper:ts-paper, etc.

Option B — Try before you install

git clone https://github.com/Albus-White/spark-to-paper-skills.git
claude --plugin-dir ./spark-to-paper-skills

Option C — Standalone skills (no namespacing)

cp -r spark-to-paper-skills/skills/ts-* ~/.claude/skills/

Option D — Git submodule (auto-updatable)

git submodule add https://github.com/Albus-White/spark-to-paper-skills.git .claude/skills/spark-to-paper-skills

💡 The suite checks GitHub for newer versions on each run. Update: git -C ~/.claude/skills/spark-to-paper-skills pull

Copy .env.example → .env and fill in only what you use.

Run ts-paper on this proposal.

Paste your idea, proposal, or proposal + data. The orchestrator auto-routes, runs the chain, and delivers a compiled paper with page count, sections, references, review outcome, and editable vector figures.

• Claude Code (the suite is a plugin)
• Python 3.10+ with pip install -r skills/ts-figure-optimize/requirements.txt
• LaTeX (latexmk + a TeX distribution) for compilation
• Optional: DrawAI runtime (~4 GB) · image-model endpoint · LibreOffice

main.pdf exists and is non-trivial · zero LaTeX errors · main.bbl resolved all citations · every \cite{} maps to a complete refs.bib entry · no fabricated numbers anywhere · every figure embedded as an editable vector PDF · the adversarial review stage ran · and run_gates.py <workdir> all exits zero.

Inspired by:

• 🔬 AI-Scientist (Sakana AI) — Automated research pioneer
• 🦞 AutoResearchClaw (AIMING Lab) — 23-stage autonomous research pipeline
• 📚 academic-research-skills (Imbad0202) — Claude Code research skill suite
• 🧠 autoresearch (Andrej Karpathy) — End-to-end research automation
• 🎨 DrawAI — Figure vectorization engine (vendored)

The model does the reasoning. The code keeps it honest. You get a paper.
_{Built on Claude Code · Figure engine vendored from DrawAI · MIT License}