Vibe-Skills is a collection and routing system that helps AI agents discover, select, and coordinate specialized skills for completing tasks. It is intended for agents that need to organize workflows across many installed capabilities. The catalogue entries are skills and an agent belonging to this system.
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 foryourhealth111-pixel/Vibe-Skills --skill adaptyvgit clone --depth 1 https://github.com/foryourhealth111-pixel/Vibe-SkillsWrote 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/foryourhealth111-pixel/vibe-skills/adaptyv)<a href="https://agentmods.dev/skills/foryourhealth111-pixel/vibe-skills/adaptyv"><img src="https://agentmods.dev/badge/skills/foryourhealth111-pixel/vibe-skills/adaptyv/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/foryourhealth111-pixel/vibe-skills/adaptyv"><img src="https://agentmods.dev/badge/skills/foryourhealth111-pixel/vibe-skills/adaptyv.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 | $0.00095 | $0.00791 |
| Opus 5.5 | $0.00038 | $0.00316 |
| Sonnet 5.5 | $0.00019 | $0.00158 |
| Haiku 4.5 | $0.00010 | $0.00079 |
Grade A, and why
adaptyv scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
response = requests.post( This is a copy
89% identical to adaptyv — 11 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 — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Adaptyv
Adaptyv is a cloud laboratory platform that provides automated protein testing and validation services. Submit protein sequences via API or web interface and receive experimental results in approximately 21 days.
Quick Start
Authentication Setup
Adaptyv requires API authentication. Set up your credentials:
- Contact [email protected] to request API access (platform is in alpha/beta)
- Receive your API access token
- Set environment variable:
export ADAPTYV_API_KEY="your_api_key_here"
Or create a .env file:
ADAPTYV_API_KEY=your_api_key_here
Installation
Install the required package using uv:
uv pip install requests python-dotenv
Basic Usage
Submit protein sequences for testing:
import os
import requests
from dotenv import load_dotenv
load_dotenv()
api_key = os.getenv("ADAPTYV_API_KEY")
base_url = "https://kq5jp7qj7wdqklhsxmovkzn4l40obksv.lambda-url.eu-central-1.on.aws"
headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json"
}
# Submit experiment
response = requests.post(
f"{base_url}/experiments",
headers=headers,
json={
"sequences": ">protein1\nMKVLWALLGLLGAA...",
"experiment_type": "binding",
"webhook_url": "https://your-webhook.com/callback"
}
)
experiment_id = response.json()["experiment_id"]
Available Experiment Types
Adaptyv supports multiple assay types:
- Binding assays - Test protein-target interactions using biolayer interferometry
- Expression testing - Measure protein expression levels
- Thermostability - Characterize protein thermal stability
- Enzyme activity - Assess enzymatic function
See reference/experiments.md for detailed information on each experiment type and workflows.
Protein Sequence Optimization
Before submitting sequences, optimize them for better expression and stability:
Common issues to address:
- Unpaired cysteines that create unwanted disulfides
- Excessive hydrophobic regions causing aggregation
- Poor solubility predictions
What ships with it
4 files 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 · 115 lines · 95 tokens per session scan A b6013c70b2cf
adaptyv is a skill published in the GitHub repository foryourhealth111-pixel/Vibe-Skills (3,772 stars, last pushed 2d ago), licensed Apache-2.0. It adds 95 tokens to every session and 791 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). It is 89% identical to adaptyv, differing in 11 lines, and is treated as a copy.
Other skills, from other repositories
solo
Work on one hard mathematics problem in this session yourself, with no sub-agents: reason in stages, record each settled step in a notes file so that nothing written is lost if a response is cut off, and end with a self-contained proof.md. Usage: /math-proof:solo .
vss-generate-video-calibration
Use this skill when running or tuning AutoMagicCalib on local MP4s, RTSP, or the bundled sample dataset, or when deploying vss-auto-calibration. Do not use for non-AMC calibration or runtime analytics.
paper-planning
Guides pre-writing planning for academic papers with 4 structured steps: story design (task-challenge-insight-contribution-advantage), experiment planning (comparisons + ablations), figure design (pipeline + teaser), and 4-week timeline management. Includes counterintuitive planning tactics (write a mock rejection…
arxiv-package
Package a TeX/LaTeX project into a clean tarball or zip for arXiv upload: file selection, build-artifact exclusion, 00README.XXX generation, ancillary file organization, archive validation. Triggers on: "package for arXiv", "create arXiv tarball", "bundle submission", "zip for arXiv", "prepare arXiv upload", "arXiv…
paper-review
Guides self-review of YOUR OWN academic paper before submission with adversarial stress-testing. Core method: three passes (adversarial deep read; 5-aspect checklist — contribution sufficiency, writing clarity, results quality, testing completeness, method design; mechanical consistency scans and experimental protocol…
evaluate-ml-pipeline
Evaluate one learner with skore.evaluate. The splitter and any non-default score are already on the DataOp from build-ml-pipeline. This skill writes the call without splitter=, persists the report, and records the locator. When the locked split is the training table and test table that shipped with the data, fit on…