paper2video

A workflow that turns a research paper or slide deck into a narrated MP4 video. It passes the work through dedicated tools for slide creation, rendering, subtitles, timeline assembly, and media checks.

In plain words
What is it for?
Creating narrated research videos from PDFs, prepared paper assets, or existing PowerPoint decks, with subtitles and a checked final video.
Why use it?
It provides one defined path from paper or presentation to video and keeps the specialized production and quality-checking steps coordinated.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/microsoft/researchstudio/paper2video
Any agent
npx skills add microsoft/ResearchStudio --skill paper2video
Clone the repo
git clone --depth 1 https://github.com/microsoft/ResearchStudio

Made for: Claude Code, Codex.

Per session 157 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 6,731 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00157 $0.06731
Opus 5 $0.00078 $0.03365
Sonnet 5 $0.00031 $0.01346
Haiku 4.5 $0.00016 $0.00673

Measured 2d ago against content hash ec80baf41b35, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

paper2video scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

The scan reads SKILL.md. This mod also ships 2 executable files (scripts/ppt_options_contract.py, scripts/ppt_stage_validator.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

ResearchStudio-Reel/skills/paper2video/SKILL.md · 547 lines

How it starts

The opening of the file, as written. The whole thing — 547 lines — stays where its author put it; the contents beside it link to each section on GitHub.

paper2video - paper/assets -> ppt-master deck -> pptx2video render -> narrated MP4

paper2video is a thin orchestrator with exactly one supported path. It does not author slides itself, does not synthesize audio itself, does not composite video itself, and does not gate QA itself. It resolves inputs and one shared trigger decision, then fully delegates:

paper.pdf
  -> skills/paper2assets/scripts/build_package.py       (shared package)
  -> assets/meta/paper_spec.md + assets/meta/narration.json
  -> ppt_options_contract.py resolve-ppt-trigger          (ONE trigger decision)
  -> installed ppt-master skill                          (full workflow, receives the trigger)
  -> installed pptx2video skill / CLI `bootstrap` then `render` (full workflow, receives the trigger)
  -> ppt_stage_validator.py audit-final-pptx-trigger      (proves the handoff held)
  -> video.mp4, video_no_subtitles.mp4, video.pptx

There is only one route. An earlier version of this skill had a second, self-implemented "Route A" that called private narration, duration, and visual-cue scripts and invoked python -m pptx2video.render_video / pptx2video.build_timeline / pptx2video.check_video_package as if they were importable submodules of the installed public package. Those modules do not exist at that import path in the public pptx2video CLI; the entry points are the pptx2video render, pptx2video doctor, and pptx2video bootstrap subcommands. Do not recreate that shortcut. Do not hand-assemble a narration script, a visual-cue file, or a "simplified" render pipeline that skips the installed pptx2video skill. Do not invoke any private submodule path under pptx2video.*. Every render goes through /pptx2video (the installed skill) or the exact pptx2video render CLI invocation in Step 5; there is no lower-effort fallback for a rushed or low-context session.

Why full delegation is mandatory

pptx2video render is not a smoke-test wrapper. Its own CLI (cli.py _render()) always runs the QA pass and always writes assets/meta/reports/video_qa_report.json; there is no --no-qa flag and no way to skip the check itself. A hand-rolled call into an internal module could skip that gate entirely, which is why every render goes through the Step 5 command.

Read the full file on GitHub · 547 lines

Files

What ships with it

6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 547 lines · 157 tokens per session scan A ec80baf41b35

Subscribe to this mod's changes

paper2video is a skill published in the GitHub repository microsoft/ResearchStudio (2,614 stars, last pushed 3d ago), licensed MIT. It adds 157 tokens to every session and 6,731 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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