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/write-speaker-notes/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/write-speaker-notes)<a href="https://agentmods.dev/skills/alohays/paper2pr/write-speaker-notes"><img src="https://agentmods.dev/badge/skills/alohays/paper2pr/write-speaker-notes/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/write-speaker-notes"><img src="https://agentmods.dev/badge/skills/alohays/paper2pr/write-speaker-notes.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 | $0.00070 | $0.03849 |
| Opus 5.5 | $0.00028 | $0.01540 |
| Sonnet 5.5 | $0.00014 | $0.00770 |
| Haiku 4.5 | $0.00007 | $0.00385 |
Grade A, and why
write-speaker-notes 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 — 362 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Speaker Notes (Presentation Script) Workflow
Generate a verbatim reading script for a deck. The presenter reads these notes as-is during the talk, so every note is the actual sentences to say aloud, in the language the deck declares.
CRITICAL: These are presentation scripts (대본), NOT talking points. Full spoken sentences that flow naturally when read aloud.
The deck's genre decides what a good script is. Never guess the genre: read the resolved profile first (Phase 0B) and apply the matching genre section below. The mechanics (where notes live, the three privacy layers) are the same for every genre.
Phase 0: Pre-Flight Checks
0A. Identify Target File
Decks live at Quarto/<genre>/<name>.qmd. Resolve the argument rather than
building a path by hand:
python3 scripts/deckpath.py [Deck] --field qmd
A bare name works; genre/name disambiguates if two genres ever share one.
If no argument, ask the user. python3 scripts/deckpath.py --list shows what
exists.
0B. Read the Deck's Premises (profile + deck.yml)
Everything the script depends on is declared, not guessed. Read both, first thing:
python3 scripts/deckprofile.py [Deck] # resolved config, JSON
cat "$(python3 scripts/deckpath.py [Deck] --field config)" # the raw deck.yml
From the resolved JSON:
profile- which genre section below applies:paper-review,lecture, orinvited-talknotes_language-ko(280 Hangul syllables/min) oren(130 words/min)speaking_min- the minutes the presenter actually talksprior_session- for a lecture in a series: the previous session's title, week, date and presenter. The opening note names it (see Genre: Lecture)sources- what technical claims in the notes are checked against
From the raw deck.yml: audience.assumes, audience.size, audience.prior
and delivery - review-agent context the resolved JSON does not carry; the
invited-talk section depends on it.
Budget = speaking_min x 280 Hangul syllables (ko) or x 130 words (en).
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 First seen · 362 lines · 70 tokens per session scan A c564610ed9d3
write-speaker-notes is a skill published in the GitHub repository alohays/paper2pr (6 stars, last pushed today), licensed MIT. It adds 70 tokens to every session and 3,849 once invoked, about $0.0003 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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