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 K-Dense-AI/drug-discovery-agent-skills --skill immunogenicitygit clone --depth 1 https://github.com/K-Dense-AI/drug-discovery-agent-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/k-dense-ai/drug-discovery-agent-skills/immunogenicity)<a href="https://agentmods.dev/skills/k-dense-ai/drug-discovery-agent-skills/immunogenicity"><img src="https://agentmods.dev/badge/skills/k-dense-ai/drug-discovery-agent-skills/immunogenicity/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/k-dense-ai/drug-discovery-agent-skills/immunogenicity"><img src="https://agentmods.dev/badge/skills/k-dense-ai/drug-discovery-agent-skills/immunogenicity.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00145 | $0.01826 |
| Opus 5 | $0.00072 | $0.00913 |
| Sonnet 5 | $0.00029 | $0.00365 |
| Haiku 4.5 | $0.00015 | $0.00183 |
Grade A, and why
immunogenicity 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 12d 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.
How it starts
The opening of the file, as written. The whole thing — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Immunogenicity Risk
A protein therapeutic can provoke antibodies against itself, and when it does the drug stops working — or worse, cross-reacts with an endogenous counterpart. This skill locates the sequence regions responsible and puts them in proportion against the factors that usually matter more.
No installation, no network, no key for the bundled scripts. Running the predictor needs NetMHCIIpan from DTU Health Tech, free for academic use under a signed licence and not redistributable — which is why these scripts prepare its input and parse its output rather than wrapping it.
Read references/running-netmhciipan.md before your first scan, references/deimmunisation.md before changing a sequence, and references/what-drives-ada.md before drawing a conclusion — that one is judgement, not syntax, and it is mostly about what the scan cannot see.
The two scripts
| Script | Answers |
|---|---|
epitope_scan.py |
Which regions present peptides, on how many alleles? |
ada_risk.py |
What does that add up to, and what else should I be worried about? |
Class II, and %Rank
Two things to get right before anything else.
Anti-drug antibodies need CD4 T-cell help, which is class II restricted. Scanning a biologic against MHC-I answers a question about cytotoxic T cells that is rarely the one being asked. Use NetMHCIIpan.
Use %Rank, not affinity. Predicted IC50 is not comparable between alleles — each has its own affinity distribution — so nM cannot be thresholded uniformly. %Rank normalises against a background of random peptides and can. Conventionally ≤2% is a strong binder, ≤10% weak.
Collapse peptides to cores, then count alleles
python skills/immunogenicity/scripts/epitope_scan.py peptides --sequence-file mab.fa > peptides.txt
# netMHCIIpan -f peptides.txt -inptype 1 -a DRB1_0101,... -xls -xlsfile out.txt
python skills/immunogenicity/scripts/epitope_scan.py parse --output out.txt
What ships with it
5 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.
- 12d ago First seen · 143 lines · 145 tokens per session scan A 6de6105d0dbf
immunogenicity is a skill published in the GitHub repository K-Dense-AI/drug-discovery-agent-skills (28 stars, last pushed 5d ago), licensed MIT. It adds 145 tokens to every session and 1,826 once invoked, about $0.0007 per session on Opus 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 2026-08-30.
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