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.
npx agentmods add skills/faviovazquez/learnship/typesetnpx skills add FavioVazquez/learnship --skill typesetgit clone --depth 1 https://github.com/FavioVazquez/learnshipWrote 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/faviovazquez/learnship/typeset)<a href="https://agentmods.dev/skills/faviovazquez/learnship/typeset"><img src="https://agentmods.dev/badge/skills/faviovazquez/learnship/typeset.svg" alt="Measured on agentmods" 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 | $0.00027 | $0.01315 |
| Opus 5 | $0.00014 | $0.00658 |
| Sonnet 5 | $0.00005 | $0.00263 |
| Haiku 4.5 | $0.00003 | $0.00131 |
Grade A, and why
typeset 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 4d 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.
This is a copy
97% identical to typeset — 10 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Assess and improve typography that feels generic, inconsistent, or poorly structured — turning default-looking text into intentional, well-crafted type.
MANDATORY PREPARATION
Use the frontend-design skill — it contains design principles, anti-patterns, and the Context Gathering Protocol. Follow the protocol before proceeding — if no design context exists yet, you MUST run teach-impeccable first.
Assess Current Typography
Analyze what's weak or generic about the current type:
-
Font choices:
- Are we using invisible defaults? (Inter, Roboto, Arial, Open Sans, system defaults)
- Does the font match the brand personality? (A playful brand shouldn't use a corporate typeface)
- Are there too many font families? (More than 2-3 is almost always a mess)
-
Hierarchy:
- Can you tell headings from body from captions at a glance?
- Are font sizes too close together? (14px, 15px, 16px = muddy hierarchy)
- Are weight contrasts strong enough? (Medium vs Regular is barely visible)
-
Sizing & scale:
- Is there a consistent type scale, or are sizes arbitrary?
- Does body text meet minimum readability? (16px+)
- Is the sizing strategy appropriate for the context? (Fixed
remscales for app UIs; fluidclamp()for marketing/content page headings)
-
Readability:
- Are line lengths comfortable? (45-75 characters ideal)
- Is line-height appropriate for the font and context?
- Is there enough contrast between text and background?
-
Consistency:
- Are the same elements styled the same way throughout?
- Are font weights used consistently? (Not bold in one section, semibold in another for the same role)
- Is letter-spacing intentional or default everywhere?
CRITICAL: The goal isn't to make text "fancier" — it's to make it clearer, more readable, and more intentional. Good typography is invisible; bad typography is distracting.
Plan Typography Improvements
Consult the typography reference from the frontend-design skill for detailed guidance on scales, pairing, and loading strategies.
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.
- 4d ago First seen · 119 lines · 27 tokens per session scan A bacc8a8ee0fe
typeset is a skill published in the GitHub repository FavioVazquez/learnship (59 stars, last pushed 3mo ago), licensed MIT. It adds 27 tokens to every session and 1,315 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to typeset, differing in 10 lines, and is treated as a copy.
Other skills, from other repositories
manuscript-reframe
Reframe report-like academic drafts into paper-form scientific arguments while preserving or explicitly renegotiating author intent; requires an approved old-versus-proposed spine, evidence and argument baselines, analysis-role control, and post-edit drift review.
audit
Use when checking a thesis draft before submission for inconsistent numbers, terminology, cross-references, or citation problems.
peer-review
Review another author's manuscript, paper, thesis chapter, proposal, or preprint as an external reviewer. Use when asked to evaluate novelty, significance, gap-contribution fit, claim-evidence adequacy, methods, evaluation, overclaim risks, structure, writing, required revisions, or recommendation without rewriting…
revision-escalation
Stop repeated failed writing, coding, manuscript, rebuttal, or restructuring revisions when the same issue has gone through 3+ unsatisfactory edits, vague feedback such as still wrong/weird/unclear/weak/越改越乱, version contamination, or possible gap/claim/evidence/venue-fit drift.
llmwiki-query
Answer a question by querying the user's llmwiki. Use when the user asks about their own past work — "what did I decide about X", "what have I been working on", "how did I solve Y", "what's my preferred approach to Z", or any question that the wiki (built from their session history) might answer. Always read the wiki…
verify
Fact-check claims encountered during reading — dates, names, events, citations. Use when encountering historical facts or disputed claims.