Borrowing it
Nothing to install: this file belongs to causify-ai/helpers. 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/causify-ai/helpers/master/.claude/skills/slides.convert_to_typst/SKILL.mdgit clone --depth 1 https://github.com/causify-ai/helpersWrote 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/causify-ai/helpers/slides.convert_to_typst)<a href="https://agentmods.dev/skills/causify-ai/helpers/slides.convert_to_typst"><img src="https://agentmods.dev/badge/skills/causify-ai/helpers/slides.convert_to_typst/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/causify-ai/helpers/slides.convert_to_typst"><img src="https://agentmods.dev/badge/skills/causify-ai/helpers/slides.convert_to_typst.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 3 Skill selects an external model or provider that may use a different account or billing plan than the operator expects. Undisclosed model switches can cause unexpected cost or quota consumption.Fix: Remove the model/provider override or disclose it prominently and require explicit operator approval before invoking an external coding CLI or billed model.
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 | $0.00011 | $0.00682 |
| Opus 5 | $0.00005 | $0.00341 |
| Sonnet 5 | $0.00002 | $0.00136 |
| Haiku 4.5 | $0.00001 | $0.00068 |
Grade A, and why
slides.convert_to_typst 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 5d 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 — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Goal
- Convert a markdown slides file to Typst presentation format
- Preserve structure, content, and formatting
- Output clean, well-formatted markdown code suitable for conversion to Typst
Workflow
- Read input file: markdown slides file (e.g.,
lectures.md) - Apply conversion rules: systematically fix math, unicode, and formatting
- Validate output: verify no content loss, structure preserved
- Output file: write converted markdown with same base name
Conversion Rules
LaTeX Custom Commands
- Replace presentation-specific commands with Typst equivalents:
$\EE$→$bb(E)$(blackboard E)$\VV$→$bb(V)$(blackboard V)$\Pr$→$Pr$(remove backslash)
Variables & Math Expressions
- Wrap all variables, parameters, and math expressions in
$...$:- Bad:
Randomly permute the values of x_j across all samples - Good:
Randomly permute the values of $x_j$ across all samples - Bad:
Compute f(x) and g(x) - Good:
Compute $f(x)$ and $g(x)$
- Bad:
Unicode → LaTeX
- Replace unicode math characters with LaTeX:
- Ω →
$\Omega$ - → →
$\to$ - ≤ →
$\leq$ - × →
$\times$
- Ω →
Subscripts & Superscripts
- Use math mode for all subscripts/superscripts:
- Bad: P₀ or P^n
- Good:
$P_0$or$P^n$
Math Operators
- Use
op()for named operators in Typst:- Bad:
$g^* = arg min_(g in G)$ - Good:
$g^* = op("arg min")_(g in G)$ - Also:
$max_i x_i$→$op("max")_i x_i$
- Bad:
Plain Numbers & Currency
- Do NOT wrap pure numbers in math mode:
- Bad:
The house costs $50k because it has 4 bedrooms ($+\$30$k) - Good:
The house costs \$50k because it has 4 bedrooms (+\$30k)
- Bad:
Block Formulas
- Use Typst code blocks for complex multi-line formulas:
$ Pr(X_1 , ... , X_n) = product_(i = 1)^n Pr(X_i | "Parents"(X_i)) $
Quality Checks
- All slide headers & hierarchy preserved
- No content deleted or truncated
- All math expressions wrapped (
$...$) - No unicode math characters remain
- Operators properly formatted with
op() - Code blocks and lists intact
- File is valid markdown
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.
- 5d ago First seen · 75 lines · 11 tokens per session scan A 57e0a590f9c3
slides.convert_to_typst is a skill published in the GitHub repository causify-ai/helpers (145 stars, last pushed today), licensed Apache-2.0. It adds 11 tokens to every session and 682 once invoked, about $0.0001 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 2026-09-03.
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