Borrowing it
Nothing to install: this file belongs to alohays/paper2pr. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/alohays/paper2pr/main/.claude/skills/new-deck/SKILL.mdgit clone --depth 1 https://github.com/alohays/paper2prWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/alohays/paper2pr/new-deck)<a href="https://agentmods.dev/skills/alohays/paper2pr/new-deck"><img src="https://agentmods.dev/badge/skills/alohays/paper2pr/new-deck/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/alohays/paper2pr/new-deck"><img src="https://agentmods.dev/badge/skills/alohays/paper2pr/new-deck.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00056 | $0.02836 |
| Opus 5.5 | $0.00022 | $0.01134 |
| Sonnet 5.5 | $0.00011 | $0.00567 |
| Haiku 4.5 | $0.00006 | $0.00284 |
Grade A, and why
new-deck 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 1mo ago.
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.
How it starts
The opening of the file, as written. The whole thing — 222 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Start a new deck
Decks in this repo are graded against a profile, and the profile only knows what to enforce if someone writes down who is listening and for how long. Before this skill existed those premises lived in the presenter's head, leaked into the deck as inconsistencies, and were re-derived from scratch every time anyone touched it. Ask once, write it down, and every downstream tool reads it from the same place.
Two halves, deliberately: you run the interview, scripts/new_deck.py writes
the files. Ask the questions however the conversation needs them asked. The
scaffold stays byte-identical for the same answers.
1. Ask
Use AskUserQuestion — this is a set of judgment calls, not a form. Batch
related ones; do not ask about anything the user already told you. Lead with a
recommendation when there is an obvious default.
Genre — papers, talks, or lectures. Run
python3 scripts/deckpath.py --genres for the live list. This picks the
directory and, unless overridden, the profile.
Audience prior knowledge — none, practitioner, or expert. The single
most consequential answer: it sets which profile's bullet budget and checks
apply (and it is what the review agents read the deck against); guessing
practitioner for a room of first-years produces a deck that scores well and
lands badly. The acronym-expansion check is off for every shipped profile
(D17: the matcher cannot tell a model name from a term), so spelling terms
out is judgment, not a gate.
Duration in minutes, and roughly how many people. Then two optional
subtractions: video_min (minutes of clips that play without narration)
and qa_min (minutes reserved for questions). deckprofile.py derives
speaking_min = duration_min - video_min - qa_min, and that is what the
speaker-notes budget runs on; a 60-minute lecture with 10 minutes of video
is a 50-minute script, not a 60-minute one. Omit both when the deck is all
talk.
Where it sits in a series - for a lecture, which course series and which
session. Run python3 scripts/series_assets.py --list for the series files
(Quarto/lectures/_series/<course>.yml, one per course; dgist-2026f is the
DGIST HSS118 term, Seminar for Comprehensive Competency Cultivation). When the deck is a session of one:
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.
- 1mo ago Changed 55fa197afeca
- 1mo ago Changed · +5 lines 00ed4383bbe9
- 1mo ago First seen · 217 lines · 56 tokens per session scan A 849901b814d3
new-deck is a skill published in the GitHub repository alohays/paper2pr (6 stars, last pushed today), licensed MIT. It adds 56 tokens to every session and 2,836 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-08-31.
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