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 crazymsn/academic-skills --skill adaptyvgit clone --depth 1 https://github.com/crazymsn/academic-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/crazymsn/academic-skills/adaptyv)<a href="https://agentmods.dev/skills/crazymsn/academic-skills/adaptyv"><img src="https://agentmods.dev/badge/skills/crazymsn/academic-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/crazymsn/academic-skills/adaptyv"><img src="https://agentmods.dev/badge/skills/crazymsn/academic-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 | $0.00113 | $0.02085 |
| Opus 5 | $0.00056 | $0.01043 |
| Sonnet 5 | $0.00023 | $0.00417 |
| Haiku 4.5 | $0.00011 | $0.00209 |
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 10d 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.
curl https://foundry-api-public.adaptyvbio.com/api/v1/targets?limit=3 \ This is a copy
86% identical to adaptyv — 55 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 — 211 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Adaptyv Bio Foundry API
Adaptyv Bio is a cloud lab that turns protein sequences into experimental data. Users submit amino acid sequences via API or UI; Adaptyv's automated lab runs assays (binding, thermostability, expression, fluorescence) and delivers results in ~21 days.
Quick Start
Base URL: https://foundry-api-public.adaptyvbio.com/api/v1
Authentication: Bearer token in the Authorization header. Tokens are obtained from foundry.adaptyvbio.com sidebar.
When writing code, always read the API key from the environment variable ADAPTYV_API_KEY or from a .env file — never hardcode tokens. Check for a .env file in the project root first; if one exists, use a library like python-dotenv to load it.
export FOUNDRY_API_TOKEN="abs0_..."
curl https://foundry-api-public.adaptyvbio.com/api/v1/targets?limit=3 \
-H "Authorization: Bearer $FOUNDRY_API_TOKEN"
Every request except GET /openapi.json requires authentication. Store tokens in environment variables or .env files — never commit them to source control.
Python SDK
Install: uv add adaptyv-sdk (falls back to uv pip install adaptyv-sdk if no pyproject.toml exists)
Environment variables (set in shell or .env file):
ADAPTYV_API_KEY=your_api_key
ADAPTYV_API_URL=https://foundry-api-public.adaptyvbio.com/api/v1
Decorator Pattern
from adaptyv import lab
@lab.experiment(target="PD-L1", experiment_type="screening", method="bli")
def design_binders():
return {"design_a": "MVKVGVNG...", "design_b": "MKVLVAG..."}
result = design_binders()
print(f"Experiment: {result.experiment_url}")
Client Pattern
from adaptyv import FoundryClient
client = FoundryClient(api_key="...", base_url="https://foundry-api-public.adaptyvbio.com/api/v1")
# Browse targets
targets = client.targets.list(search="EGFR", selfservice_only=True)
# Estimate cost
estimate = client.experiments.cost_estimate({
"experiment_spec": {
"experiment_type": "screening",
"method": "bli",
"target_id": "target-uuid",
"sequences": {"seq1": "EVQLVESGGGLVQ..."},
"n_replicates": 3
}
})
# Create and submit
exp = client.experiments.create({...})
client.experiments.submit(exp.experiment_id)
# Later: retrieve results
results = client.experiments.get_results(exp.experiment_id)
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.
- 10d ago First seen · 211 lines · 113 tokens per session scan A fa82db3e1a5f
adaptyv is a skill published in the GitHub repository crazymsn/academic-skills (22 stars, last pushed 3mo ago), licensed MIT. It adds 113 tokens to every session and 2,085 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 86% identical to adaptyv, differing in 55 lines, and is treated as a copy.
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