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 zongtingwei/Bioclaw_Skills_Hub --skill protein-design-qcgit clone --depth 1 https://github.com/zongtingwei/Bioclaw_Skills_HubWrote 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/zongtingwei/bioclaw_skills_hub/protein-design-qc)<a href="https://agentmods.dev/skills/zongtingwei/bioclaw_skills_hub/protein-design-qc"><img src="https://agentmods.dev/badge/skills/zongtingwei/bioclaw_skills_hub/protein-design-qc/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/zongtingwei/bioclaw_skills_hub/protein-design-qc"><img src="https://agentmods.dev/badge/skills/zongtingwei/bioclaw_skills_hub/protein-design-qc.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 | $0.00143 | $0.02989 |
| Opus 5.5 | $0.00057 | $0.01196 |
| Sonnet 5.5 | $0.00029 | $0.00598 |
| Haiku 4.5 | $0.00014 | $0.00299 |
Grade A, and why
protein-design-qc 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 — 324 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Protein Design Quality Control And Ranking
Plain-language role: Use this skill to decide which designs pass QC and which ones should move forward.
Critical Limitation
Individual metrics have weak predictive power for binding. Research shows:
- Individual metric ROC AUC: 0.64-0.66 (slightly better than random)
- Metrics are pre-screening filters, not affinity predictors
- Composite scoring is essential for meaningful ranking
These thresholds filter out poor designs but do NOT predict binding affinity.
QC Organization
QC is organized by purpose and level:
| Purpose | What it assesses | Key metrics |
|---|---|---|
| Binding | Interface quality, binding geometry | ipTM, PAE, SC, dG, dSASA |
| Expression | Manufacturability, solubility | Instability, GRAVY, pI, cysteines |
| Structural | Fold confidence, consistency | pLDDT, pTM, scRMSD |
Each category has two levels:
- Metric-level: Calculated values with thresholds (pLDDT > 0.85)
- Design-level: Pattern/motif detection (odd cysteines, NG sites)
Quick Reference: All Thresholds
| Category | Metric | Standard | Stringent | Source |
|---|---|---|---|---|
| Structural | pLDDT | > 0.85 | > 0.90 | AF2/Chai/Boltz |
| pTM | > 0.70 | > 0.80 | AF2/Chai/Boltz | |
| scRMSD | < 2.0 Å | < 1.5 Å | Design vs pred | |
| Binding | ipTM | > 0.50 | > 0.60 | AF2/Chai/Boltz |
| PAE_interaction | < 12 Å | < 10 Å | AF2/Chai/Boltz | |
| Shape Comp (SC) | > 0.50 | > 0.60 | PyRosetta | |
| interface_dG | < -10 | < -15 | PyRosetta | |
| Expression | Instability | < 40 | < 30 | BioPython |
| GRAVY | < 0.4 | < 0.2 | BioPython | |
| ESM2 PLL | > 0.0 | > 0.2 | ESM2 |
Design-Level Checks (Expression)
| Pattern | Risk | Action |
|---|---|---|
| Odd cysteine count | Unpaired disulfides | Redesign |
| NG/NS/NT motifs | Deamidation | Flag/avoid |
| K/R >= 3 consecutive | Proteolysis | Flag |
| >= 6 hydrophobic run | Aggregation | Redesign |
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 · 324 lines · 143 tokens per session scan A b3a2eb11b081
protein-design-qc is a skill published in the GitHub repository zongtingwei/Bioclaw_Skills_Hub (27 stars, last pushed 6mo ago), licensed MIT. It adds 143 tokens to every session and 2,989 once invoked, about $0.0006 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.
Other skills, from other repositories
protein-design-qc
Protein design quality control, filtering thresholds, and ranking guidance. Use this skill when: (1) Evaluating design quality for binding, expression, or structure, (2) Setting filtering thresholds for pLDDT, ipTM, PAE, (3) Checking sequence liabilities (cysteines, deamidation, polybasic clusters), (4) Creating…
protein-qc
Quality control metrics and filtering thresholds for protein design. Use this skill when: (1) Evaluating design quality for binding, expression, or structure, (2) Setting filtering thresholds for pLDDT, ipTM, PAE, (3) Checking sequence liabilities (cysteines, deamidation, polybasic clusters), (4) Creating multi-stage…
protein-qc
Quality control metrics and filtering thresholds for protein design. Use this skill when: (1) Evaluating design quality for binding, expression, or structure, (2) Setting filtering thresholds for pLDDT, ipTM, PAE, (3) Checking sequence liabilities (cysteines, deamidation, polybasic clusters), (4) Creating multi-stage…
protein-qc
Quality control metrics and filtering thresholds for protein design. Use this skill when: (1) Evaluating design quality for binding, expression, or structure, (2) Setting filtering thresholds for pLDDT, ipTM, PAE, (3) Checking sequence liabilities (cysteines, deamidation, polybasic clusters), (4) Creating multi-stage…
Model Evaluation
Evaluation methodology — split strategy, metric selection, baseline comparison, failure mode analysis.
academic-paper-to-chinese-insight-pdf
Turn an academic paper PDF into a high-quality Chinese insight report and export it as a polished Chinese PDF. Use when asked to read a paper, translate or summarize a paper into Chinese, extract the paper's core ideas, produce original insights/opinions, explain the paper in simpler language, or deliver a Chinese PDF…