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 pxdesigngit 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/pxdesign)<a href="https://agentmods.dev/skills/001tmf/blatant-why/pxdesign"><img src="https://agentmods.dev/badge/skills/001tmf/blatant-why/pxdesign/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/pxdesign"><img src="https://agentmods.dev/badge/skills/001tmf/blatant-why/pxdesign.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 | $0.00002 | $0.05401 |
| Opus 5.5 | $0.00001 | $0.02160 |
| Sonnet 5.5 | $0.00000 | $0.01080 |
| Haiku 4.5 | $0.00000 | $0.00540 |
Grade A, and why
pxdesign 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.
How it starts
The opening of the file, as written. The whole thing — 479 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PXDesign — De Novo Protein Binder Design
You are an expert at designing de novo protein binders using PXDesign. This skill
covers YAML config construction, CLI invocation, output parsing, and result
interpretation. PXDesign achieves 17–82% pass rate for de novo binder design
depending on target difficulty (see references/de-novo-strategy.md).
PXDesign is invoked from the command line via the pxdesign binary. This skill
uses a strict Write → Bash → Read pattern: write a YAML config, run the CLI,
read the summary.csv. Do not call internal Python wrappers.
When to Use This Skill
Use PXDesign when you have:
- ✅ A non-antibody, non-nanobody binder target — a small globular protein binder is acceptable
- ✅ A target structure file (CIF preferred, PDB acceptable)
- ✅ A defined epitope or hotspot list (or willingness to let PXDesign explore)
- ✅ Local GPU available (A100/H100/RTX PRO 6000) OR HPC/Tamarind fallback configured
- ✅ A clear quality bar — preview for exploration, extended for production designs
Do NOT use this skill when:
- ❌ The user wants an antibody or nanobody → use the
boltzgenskill - ❌ The user wants structure prediction only → use the
protenixskill - ❌ The user wants to score or screen existing designs → use
by-scoring/by-screening - ❌ No target structure exists → predict it with Protenix first
- ❌ The target is membrane-embedded with no soluble construct → restate the problem; PXDesign requires a soluble target
Quick Start
Design a 100-residue binder against chain A of IL6R.cif, preview preset, 10 samples:
# Step 1 — Write config.yaml (see references/yaml-config-spec.md)
cat > /tmp/il6r/config.yaml <<'EOF'
target:
file: "/data/targets/IL6R.cif"
chains:
A: "all"
binder_length: 100
EOF
# Step 2 — Run PXDesign on local GPU
PATH=/path/to/conda/envs/protenix/bin:$PATH \
PROTENIX_DATA_ROOT_DIR=$PROTEUS_PROT_DIR/release_data/ccd_cache \
TOOL_WEIGHTS_ROOT=$PROTEUS_PROT_DIR/tool_weights \
CUTLASS_PATH=$HOME/cutlass \
CUDA_HOME=$CUDA_HOME \
pxdesign pipeline \
--preset preview \
-i /tmp/il6r/config.yaml \
--N_sample 10 \
--dtype bf16 \
--use_fast_ln True \
-o /tmp/il6r/output
# Step 3 — Parse outputs
python scripts/parse_pxdesign_output.py \
--output-dir /tmp/il6r/output \
--out /tmp/il6r/designs.csv
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.
- 1mo ago First seen · 479 lines · 2 tokens per session scan A c768bf5eee75
pxdesign is a skill published in the GitHub repository 001TMF/blatant-why (119 stars, last pushed 9d ago), licensed MIT. It adds 2 tokens to every session and 5,401 once invoked, about $0.0000 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 2026-08-30.
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