Borrowing it
Nothing to install: this file belongs to whshang/herdr-mcp. 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/whshang/herdr-mcp/main/.agents/skills/typesafe-ai/SKILL.mdgit clone --depth 1 https://github.com/whshang/herdr-mcpWrote 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/whshang/herdr-mcp/typesafe-ai)<a href="https://agentmods.dev/skills/whshang/herdr-mcp/typesafe-ai"><img src="https://agentmods.dev/badge/skills/whshang/herdr-mcp/typesafe-ai/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/whshang/herdr-mcp/typesafe-ai"><img src="https://agentmods.dev/badge/skills/whshang/herdr-mcp/typesafe-ai.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 | $0.00123 | $0.02065 |
| Opus 5.5 | $0.00049 | $0.00826 |
| Sonnet 5.5 | $0.00025 | $0.00413 |
| Haiku 4.5 | $0.00012 | $0.00206 |
Grade A, and why
typesafe-ai 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.
The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 150 lines · 123 tokens per session scan A 71ea90d7906c
typesafe-ai is a skill published in the GitHub repository whshang/herdr-mcp (10 stars, last pushed yesterday), with no licence file. It adds 123 tokens to every session and 2,065 once invoked, about $0.0005 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 2026-09-20.
Other skills, from other repositories
outlines
Outlines: structured JSON/regex/Pydantic LLM generation.
instructor
Structured LLM outputs validated with Pydantic.
evaluating-llms-harness
Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.
weights-and-biases
W&B: log ML experiments, sweeps, model registry, dashboards.
guidance
Constrain LLM output with grammars; guarantee valid JSON.
huggingface-tokenizers
Fast BPE/WordPiece tokenization and custom vocab training.