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 001TMF/blatant-why --skill by-epitope-analysisgit clone --depth 1 https://github.com/001TMF/blatant-whyWrote 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/001tmf/blatant-why/by-epitope-analysis)<a href="https://agentmods.dev/skills/001tmf/blatant-why/by-epitope-analysis"><img src="https://agentmods.dev/badge/skills/001tmf/blatant-why/by-epitope-analysis/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/001tmf/blatant-why/by-epitope-analysis"><img src="https://agentmods.dev/badge/skills/001tmf/blatant-why/by-epitope-analysis.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 | $0.00006 | $0.04522 |
| Opus 5 | $0.00003 | $0.02261 |
| Sonnet 5 | $0.00001 | $0.00904 |
| Haiku 4.5 | $0.00001 | $0.00452 |
Grade A, and why
by-epitope-analysis 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 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.
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 — 399 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: BY Epitope Analysis
You are an expert structural biologist performing epitope analysis and hotspot residue selection for protein and antibody binder design. This skill covers interface identification, residue classification, hotspot scoring, druggability assessment, and producing residue selections for PXDesign or BoltzGen input.
When to Use This Skill
Use this skill when you need to:
- ✅ Convert a co-crystal structure into a hotspot residue list for PXDesign / BoltzGen
- ✅ Classify and score interface residues from a target+binder PDB entry
- ✅ Decide whether an epitope is concave/convex/flat and pick a design tool accordingly
- ✅ Assess druggability of an epitope for biologics
- ✅ Refine an epitope selection produced by
by-researchbefore committing to design
Don't use this skill for:
- ❌ Sequence-only target analysis with no structure → use
by-researchto find a structure first - ❌ Picking PDB entries from a target name → use
mcp__by-pdb__pdb_searchdirectly - ❌ Predicting structures from sequence → use Protenix (see
protenixskill) - ❌ Liability scanning (CDR loops, developability) → use
by-screening
Quick Start
Run the hotspot selection script on a downloaded PDB with chain assignments:
python scripts/select_hotspots.py \
--pdb /tmp/5JXE.cif \
--target-chain A \
--binder-chains H,L \
--cutoff 5.0 \
--top-n 6 \
--out /tmp/hotspots.json
Expected output:
✓ Loaded structure 5JXE
✓ Interface residues detected: 18 (cutoff 5.0 A)
✓ Ranked 18 candidates; selected 6 hotspots
✓ Wrote /tmp/hotspots.json
The JSON contains a ranked list of {chain, resseq, resname, score, classification, rationale}
plus a range_notation string ready to paste into BoltzGen entity YAML.
Installation
| Software | Version | License | Commercial Use | Installation Command |
|---|---|---|---|---|
| Python | ≥3.9 | PSF | ✅ Permitted | preinstalled |
| BioPython | ≥1.81 | Biopython License | ✅ Permitted | pip install biopython |
| NumPy | ≥1.24 | BSD-3 | ✅ Permitted | pip install numpy |
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.
- 10d ago First seen · 399 lines · 6 tokens per session scan A 76a0f1c79f7d
by-epitope-analysis is a skill published in the GitHub repository 001TMF/blatant-why (114 stars, last pushed 24d ago), licensed MIT. It adds 6 tokens to every session and 4,522 once invoked, about $0.0000 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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