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 skills add wunki/dotfiles --skill typesafegit clone --depth 1 https://github.com/wunki/dotfilesWrote 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/wunki/dotfiles/typesafe)<a href="https://agentmods.dev/skills/wunki/dotfiles/typesafe"><img src="https://agentmods.dev/badge/skills/wunki/dotfiles/typesafe/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/wunki/dotfiles/typesafe"><img src="https://agentmods.dev/badge/skills/wunki/dotfiles/typesafe.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 | $0.00122 | $0.02064 |
| Opus 5.5 | $0.00049 | $0.00826 |
| Sonnet 5.5 | $0.00024 | $0.00413 |
| Haiku 4.5 | $0.00012 | $0.00206 |
Grade A, and why
typesafe 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 14d 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
100% identical to typesafe-ai — 2 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 — 150 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Build with TypeSafe
TypeSafe makes units of AI intelligence usable like programming primitives: small judgments you can compose into larger capabilities. Its System One models return fast, focused judgments that software can consume directly. Jev is TypeSafe's flagship and first System One model. It understands natural language and returns typed answers and probabilities rather than generating text or reasoning explanations. Code owns the workflow; the model supplies programmable common sense where ordinary code needs semantic understanding.
Read the live docs
The live TypeSafe docs are the source of truth. Read them as part of the task. This skill gives direction; the docs carry current concepts, prompting guidance, API contracts, SDK usage, models, limits, and worked examples.
- Start with the documentation index to discover relevant pages and cookbooks. Use targeted reads rather than loading the entire site.
- Mintlify serves Markdown by appending
.mdto a page path, for example how to build with TypeSafe. Follow links from the index; convert extensionless documentation page links to.mdwhen useful. Resolve relative links againsthttps://docs.typesafe.ai. - Before writing an integration, read the current API or chosen SDK page and the question guidance relevant to the design. For a new workflow, also inspect the closest cookbook: it often shows a better decomposition than a generic classifier.
- If the index is unavailable, use the direct links below or the site's navigation. If Markdown fetching fails, try the normal page. If live access is unavailable, use available local docs or installed SDK types, state that limitation, and avoid inventing version-dependent details.
| Task | Start here; follow the relevant details |
|---|---|
| Understand the programming model | System One, building guide |
| Explore what to build | Use-case map, then relevant cookbooks from the index |
| Prepare inputs and questions | State, primitives, then the chosen primitive's page |
| Decide how to handle uncertainty | Confidence |
| Write API code | HTTP API, Python SDK, or JavaScript SDK |
| Update an older integration | Migration guide and the installed SDK's current reference |
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.
- 14d ago First seen · 150 lines · 122 tokens per session scan A ebfb7c76a552
typesafe is a skill published in the GitHub repository wunki/dotfiles (47 stars, last pushed 9d ago), licensed MIT. It adds 122 tokens to every session and 2,064 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to typesafe-ai, differing in 2 lines, and is treated as a copy.
Other skills, from other repositories
typesafe-ai
Build AI-powered software with TypeSafe: small units of AI intelligence you can use like programming primitives. Its System One models, including Jev, turn natural language and application state into typed judgments and probabilities that code can combine. Use when a feature needs programmable common sense, when…
esm
Comprehensive toolkit for EvolutionaryScale protein language models including ESM3 (generative multimodal design across sequence, structure, and function) and ESM C (efficient embeddings). Use for protein sequence/structure/function tasks, inverse folding, embeddings, variant design, and ESMFold2 structure prediction…
factory-prompting
Structured prompting with XML tags. A small named vocabulary (instructions, context, input, outputformat, examples, constraints, role, thinking) that turns a prose ask into something the model can parse without ambiguity. Read whenever you're authoring a prompt by hand, designing a prompt template for production, or…
prompt-engineering
Draft, review, or improve a prompt for an LLM or agent against a research-backed rubric — explicit instructions, instruction/data separation, output contracts, reasoning scaffolds, grounding, verification, and evals. Use when writing a prompt, auditing one that misbehaves, or explaining a prompting principle.
factory-prompt
Convert a rough ask into a structured XML-tagged prompt using the factory-prompting vocabulary.
orchestrate-agents
Orchestrate multiple agent CLIs (Claude, Codex, Antigravity) via tmux with a shared fleet store, dispatching one guardian subagent per pane. Survey-first: inspects and adopts existing tmux sessions, windows, and agent panes before creating anything new. Use when running a multi-agent session, dispatching parallel…