[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M73GYP3QB47B474PJCWX0X86":3,"skill-same:01M73GYP3QB47B474PJCWX0X86":53},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":19,"linkOnly":20,"markdownContent":21,"markdownBody":22,"frontMatter":23,"refName":29,"commitSha":30,"skillDirectory":31,"forksCount":32,"repositoryHtmlUrl":33,"repositoryHomepageUrl":34,"ownerHtmlUrl":35,"ownerBlogUrl":36,"ownerDescription":37,"updatedAt":38,"install":39,"resources":52},"01M73GYP3QB47B474PJCWX0X86","ipsae",null,"Binder design ranking using ipSAE (interprotein Score from Aligned Errors). Use this skill when: (1) Ranking binder designs for experimental testing, (2) Filtering BindCraft or RFdiffusion outputs, (3) Comparing AF2\u002FAF3\u002FBoltz predictions, (4) Predicting binding success rates, (5) Need better ranking than ipTM or iPAE.\nFor structure prediction, use chai or alphafold. For QC thresholds, use protein-qc.","bg-szy\u002FTOP-SKILLS","bg-szy","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F106576190?v=4","01M73A4E8KPV67SFXRY8XZGNBX","ShuZhiyong","01M73A4E96MQ294MHM74TZBVQD",6,"MIT","https:\u002F\u002Fgithub.com\u002Fbg-szy\u002FTOP-SKILLS\u002Fblob\u002Fmaster\u002Fskills\u002Fawesome-skills\u002Fipsae\u002FSKILL.md",[],[],"2026-11-11 15:11:14",false,"---\nname: ipsae\ndescription: \"Binder design ranking using ipSAE (interprotein Score from Aligned Errors). Use this skill when: (1) Ranking binder designs for experimental testing, (2) Filtering BindCraft or RFdiffusion outputs, (3) Comparing AF2\u002FAF3\u002FBoltz predictions, (4) Predicting binding success rates, (5) Need better ranking than ipTM or iPAE.\n\n  For structure prediction, use chai or alphafold. For QC thresholds, use protein-qc.\"\nlicense: MIT\ncategory: evaluation\ntags: [ranking, scoring, binding]\n---\n\n# ipSAE Binder Ranking\n\n## Prerequisites\n\n| Requirement | Minimum | Recommended |\n|-------------|---------|-------------|\n| Python | 3.8+ | 3.10 |\n| NumPy | 1.20+ | Latest |\n| RAM | 8GB | 16GB |\n\n## Overview\n\nipSAE (interprotein Score from Aligned Errors) is a scoring function for ranking protein-protein interactions predicted by AlphaFold2, AlphaFold3, and Boltz1. It separates true from false predicted complexes more reliably than ipTM, which dilutes interface confidence across disordered or accessory regions. In a separate binder meta-analysis (Overath et al. 2025), AF3 ipSAE_min gave a 1.4-fold gain in average precision over the ipAE score that RFdiffusion pipelines commonly filter on.\n\n**Paper**: Dunbrack, \"Rēs ipSAE loquuntur: What's wrong with AlphaFold's ipTM score and how to fix it\", [bioRxiv 2025.02.10.637595](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2025.02.10.637595v2)\n\n## How to run\n\n### Installation\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002FDunbrackLab\u002FIPSAE.git\ncd IPSAE\npip install numpy\n```\n\n### AlphaFold2\n```bash\npython ipsae.py scores_rank_001.json unrelaxed_rank_001.pdb 15 15\n```\n\n### AlphaFold3\n```bash\npython ipsae.py fold_model_full_data_0.json fold_model_0.cif 10 10\n```\n\n### Boltz1\n```bash\npython ipsae.py pae_model_0.npz model_0.cif 10 10\n```\n\n## Key parameters\n\n| Parameter | Description | Recommended |\n|-----------|-------------|-------------|\n| PAE file | JSON (AF2\u002FAF3) or NPZ (Boltz) | Match predictor |\n| Structure file | PDB or CIF structure | Match PAE |\n| PAE cutoff | Threshold for contacts | 10-15 |\n| Distance cutoff | Max CA-CA distance (A) | 10-15 |\n\n## Output format\n\nTwo output files are generated:\n\n**Chain-pair scores** (`_chains.csv`):\n```\nchain_A,chain_B,ipSAE_min,pDockQ,pDockQ2,LIS,n_contacts,interface_dist\nA,B,0.72,0.65,0.58,0.45,42,8.5\n```\n\n**Residue-level scores** (`_residues.csv`):\n```\nchain,resnum,pSAE,pLDDT\nA,45,0.85,92.3\nA,67,0.78,88.1\n```\n\n## Sample output\n\n### Successful run\n```\n$ python ipsae.py scores_rank_001.json design_0.pdb 10 10\nProcessing design_0...\nFound 2 chains: A, B\nComputing ipSAE scores...\n\nResults written to:\n  design_0_chains.csv\n  design_0_residues.csv\n\nSummary:\n  ipSAE_min: 0.72\n  pDockQ: 0.65\n  LIS: 0.45\n  Interface contacts: 42\n```\n\n**What good output looks like:**\n- ipSAE_min > 0.61 (primary filter)\n- pDockQ > 0.5 (supporting metric)\n- Reasonable number of interface contacts (20-100)\n\n## Decision tree\n\n```\nShould I use ipSAE?\n│\n├─ What are you ranking?\n│  ├─ Designed binders → ipSAE ✓\n│  ├─ Natural complexes → ipTM is fine\n│  └─ Single proteins → Not applicable\n│\n├─ What predictor did you use?\n│  ├─ AlphaFold2 → ipSAE ✓\n│  ├─ AlphaFold3 → ipSAE ✓\n│  ├─ Boltz1 → ipSAE ✓\n│  ├─ Chai → ipSAE (use PAE output)\n│  └─ ESMFold → Not applicable (no PAE)\n│\n└─ Why ipSAE over ipTM?\n   ├─ Different length constructs → ipSAE ✓\n   ├─ Designs with disordered regions → ipSAE ✓\n   └─ Standard complexes → Either works\n```\n\n## Recommended thresholds\n\n| Metric | Standard | Stringent | Use Case |\n|--------|----------|-----------|----------|\n| ipSAE_min | > 0.61 | > 0.70 | Primary filter |\n| LIS | > 0.35 | > 0.45 | Interface quality |\n| pDockQ | > 0.5 | > 0.6 | Supporting |\n\n## Batch processing\n\n```python\nimport subprocess\nimport os\nfrom pathlib import Path\n\ndef score_designs(pae_dir, struct_dir, output_dir):\n    \"\"\"Score all designs in a directory.\"\"\"\n    Path(output_dir).mkdir(exist_ok=True)\n\n    for pae_file in Path(pae_dir).glob(\"*_scores*.json\"):\n        name = pae_file.stem.replace(\"_scores_rank_001\", \"\")\n        struct_file = Path(struct_dir) \u002F f\"{name}.pdb\"\n\n        if struct_file.exists():\n            subprocess.run([\n                \"python\", \"ipsae.py\",\n                str(pae_file),\n                str(struct_file),\n                \"10\", \"10\"\n            ])\n```\n\n---\n\n## Verify\n\n```bash\nls *_chains.csv | wc -l  # Should match number of predictions\n```\n\n---\n\n## Troubleshooting\n\n**Low scores for good designs**: Check PAE\u002Fdistance cutoffs\n**Missing output**: Verify PAE file format matches predictor\n**Inconsistent scores**: Use same cutoffs across all designs\n\n### Error interpretation\n\n| Error | Cause | Fix |\n|-------|-------|-----|\n| `KeyError: 'pae'` | Wrong PAE format | Check if AF2\u002FAF3\u002FBoltz format |\n| `FileNotFoundError` | Structure not found | Verify file paths |\n| `ValueError: no contacts` | No interface detected | Check chain IDs, reduce cutoffs |\n\n---\n\n**Next**: Select top designs (ipSAE_min > 0.61) → experimental validation.\n","# ipSAE Binder Ranking\n\n## Prerequisites\n\n| Requirement | Minimum | Recommended |\n|-------------|---------|-------------|\n| Python | 3.8+ | 3.10 |\n| NumPy | 1.20+ | Latest |\n| RAM | 8GB | 16GB |\n\n## Overview\n\nipSAE (interprotein Score from Aligned Errors) is a scoring function for ranking protein-protein interactions predicted by AlphaFold2, AlphaFold3, and Boltz1. It separates true from false predicted complexes more reliably than ipTM, which dilutes interface confidence across disordered or accessory regions. In a separate binder meta-analysis (Overath et al. 2025), AF3 ipSAE_min gave a 1.4-fold gain in average precision over the ipAE score that RFdiffusion pipelines commonly filter on.\n\n**Paper**: Dunbrack, \"Rēs ipSAE loquuntur: What's wrong with AlphaFold's ipTM score and how to fix it\", [bioRxiv 2025.02.10.637595](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2025.02.10.637595v2)\n\n## How to run\n\n### Installation\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002FDunbrackLab\u002FIPSAE.git\ncd IPSAE\npip install numpy\n```\n\n### AlphaFold2\n```bash\npython ipsae.py scores_rank_001.json unrelaxed_rank_001.pdb 15 15\n```\n\n### AlphaFold3\n```bash\npython ipsae.py fold_model_full_data_0.json fold_model_0.cif 10 10\n```\n\n### Boltz1\n```bash\npython ipsae.py pae_model_0.npz model_0.cif 10 10\n```\n\n## Key parameters\n\n| Parameter | Description | Recommended |\n|-----------|-------------|-------------|\n| PAE file | JSON (AF2\u002FAF3) or NPZ (Boltz) | Match predictor |\n| Structure file | PDB or CIF structure | Match PAE |\n| PAE cutoff | Threshold for contacts | 10-15 |\n| Distance cutoff | Max CA-CA distance (A) | 10-15 |\n\n## Output format\n\nTwo output files are generated:\n\n**Chain-pair scores** (`_chains.csv`):\n```\nchain_A,chain_B,ipSAE_min,pDockQ,pDockQ2,LIS,n_contacts,interface_dist\nA,B,0.72,0.65,0.58,0.45,42,8.5\n```\n\n**Residue-level scores** (`_residues.csv`):\n```\nchain,resnum,pSAE,pLDDT\nA,45,0.85,92.3\nA,67,0.78,88.1\n```\n\n## Sample output\n\n### Successful run\n```\n$ python ipsae.py scores_rank_001.json design_0.pdb 10 10\nProcessing design_0...\nFound 2 chains: A, B\nComputing ipSAE scores...\n\nResults written to:\n  design_0_chains.csv\n  design_0_residues.csv\n\nSummary:\n  ipSAE_min: 0.72\n  pDockQ: 0.65\n  LIS: 0.45\n  Interface contacts: 42\n```\n\n**What good output looks like:**\n- ipSAE_min > 0.61 (primary filter)\n- pDockQ > 0.5 (supporting metric)\n- Reasonable number of interface contacts (20-100)\n\n## Decision tree\n\n```\nShould I use ipSAE?\n│\n├─ What are you ranking?\n│  ├─ Designed binders → ipSAE ✓\n│  ├─ Natural complexes → ipTM is fine\n│  └─ Single proteins → Not applicable\n│\n├─ What predictor did you use?\n│  ├─ AlphaFold2 → ipSAE ✓\n│  ├─ AlphaFold3 → ipSAE ✓\n│  ├─ Boltz1 → ipSAE ✓\n│  ├─ Chai → ipSAE (use PAE output)\n│  └─ ESMFold → Not applicable (no PAE)\n│\n└─ Why ipSAE over ipTM?\n   ├─ Different length constructs → ipSAE ✓\n   ├─ Designs with disordered regions → ipSAE ✓\n   └─ Standard complexes → Either works\n```\n\n## Recommended thresholds\n\n| Metric | Standard | Stringent | Use Case |\n|--------|----------|-----------|----------|\n| ipSAE_min | > 0.61 | > 0.70 | Primary filter |\n| LIS | > 0.35 | > 0.45 | Interface quality |\n| pDockQ | > 0.5 | > 0.6 | Supporting |\n\n## Batch processing\n\n```python\nimport subprocess\nimport os\nfrom pathlib import Path\n\ndef score_designs(pae_dir, struct_dir, output_dir):\n    \"\"\"Score all designs in a directory.\"\"\"\n    Path(output_dir).mkdir(exist_ok=True)\n\n    for pae_file in Path(pae_dir).glob(\"*_scores*.json\"):\n        name = pae_file.stem.replace(\"_scores_rank_001\", \"\")\n        struct_file = Path(struct_dir) \u002F f\"{name}.pdb\"\n\n        if struct_file.exists():\n            subprocess.run([\n                \"python\", \"ipsae.py\",\n                str(pae_file),\n                str(struct_file),\n                \"10\", \"10\"\n            ])\n```\n\n---\n\n## Verify\n\n```bash\nls *_chains.csv | wc -l  # Should match number of predictions\n```\n\n---\n\n## Troubleshooting\n\n**Low scores for good designs**: Check PAE\u002Fdistance cutoffs\n**Missing output**: Verify PAE file format matches predictor\n**Inconsistent scores**: Use same cutoffs across all designs\n\n### Error interpretation\n\n| Error | Cause | Fix |\n|-------|-------|-----|\n| `KeyError: 'pae'` | Wrong PAE format | Check if AF2\u002FAF3\u002FBoltz format |\n| `FileNotFoundError` | Structure not found | Verify file paths |\n| `ValueError: no contacts` | No interface detected | Check chain IDs, reduce cutoffs |\n\n---\n\n**Next**: Select top designs (ipSAE_min > 0.61) → experimental validation.\n",{"name":5,"tags":24,"license":15,"category":28,"description":7},[25,26,27],"ranking","scoring","binding","evaluation","master","fd76f882a04e445e899d5cfb2732f2105ea054ee","skills\u002Fawesome-skills\u002Fipsae",0,"https:\u002F\u002Fgithub.com\u002Fbg-szy\u002FTOP-SKILLS","https:\u002F\u002Fbg-szy.github.io\u002FTOP-SKILLS\u002F","https:\u002F\u002Fgithub.com\u002Fbg-szy","https:\u002F\u002Fwww.ecust.edu.cn\u002F","a student of East China University of Science and Technology","2026-11-12 05:55:03",{"source":8,"skillName":5,"commands":40,"prompt":50,"zipAvailable":51},[41,44,47],{"tool":42,"command":43},"npx","npx skills add bg-szy\u002FTOP-SKILLS --skill ipsae",{"tool":45,"command":46},"bunx","bunx skills add bg-szy\u002FTOP-SKILLS --skill ipsae",{"tool":48,"command":49},"pnpm","pnpm dlx skills add bg-szy\u002FTOP-SKILLS --skill ipsae","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fbg-szy\u002FTOP-SKILLS\u002Fblob\u002Fmaster\u002Fskills\u002Fawesome-skills\u002Fipsae\u002FSKILL.md",true,[],[54,61,68,77,85,93],{"skillNo":55,"name":56,"nameZh":6,"description":57,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":58,"categories":59,"keywords":60,"publishedAt":19},"01M748C5DS5BQBVBMG22FCGG34","branding","When the user wants to define, audit, or apply brand strategy—purpose, values, positioning, storytelling, voice, narrative (not only visuals). Also use when the user mentions \"brand strategy\", \"brand story\", \"brand storytelling\" or \"brand voice\". For typography, colors, design tokens, and frontend visuals, use brand-visual-generator.","https:\u002F\u002Fgithub.com\u002Fbg-szy\u002FTOP-SKILLS\u002Fblob\u002Fmaster\u002Fskills\u002Fsuperskills\u002Fbranding\u002FSKILL.md",[],[],{"skillNo":62,"name":63,"nameZh":6,"description":64,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":65,"categories":66,"keywords":67,"publishedAt":19},"01M748C1GXJ9WRKP18X45DKP6G","brand-visual-generator","When the user wants to define, audit, or apply visual identity (typography, colors, spacing, design tokens, frontend aesthetics). Also use when the user mentions \"brand style guide\", \"visual identity\", \"design system\" or \"typography\". For brand story, positioning, and voice, use branding.","https:\u002F\u002Fgithub.com\u002Fbg-szy\u002FTOP-SKILLS\u002Fblob\u002Fmaster\u002Fskills\u002Fsuperskills\u002Fbrand-visual-generator\u002FSKILL.md",[],[],{"skillNo":69,"name":70,"nameZh":6,"description":71,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":72,"licenseFileUrl":73,"sourceHtmlUrl":74,"categories":75,"keywords":76,"publishedAt":19},"01M747T565TFEP3RAVWM0Y899Y","mcp-builder","Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node\u002FTypeScript (MCP SDK).","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fbg-szy\u002FTOP-SKILLS\u002Fblob\u002Fmaster\u002Fskills\u002Fskills\u002Fmcp-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fbg-szy\u002FTOP-SKILLS\u002Fblob\u002Fmaster\u002Fskills\u002Fskills\u002Fmcp-builder\u002FSKILL.md",[],[],{"skillNo":78,"name":79,"nameZh":6,"description":80,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":72,"licenseFileUrl":81,"sourceHtmlUrl":82,"categories":83,"keywords":84,"publishedAt":19},"01M747RAJGMBZ2QN4563STB5SH","frontend-design","Guidance for distinctive, intentional visual design when building new UI or reshaping an existing one. Helps with aesthetic direction, typography, and making choices that don't read as templated defaults.","https:\u002F\u002Fgithub.com\u002Fbg-szy\u002FTOP-SKILLS\u002Fblob\u002Fmaster\u002Fskills\u002Fskills\u002Ffrontend-design\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fbg-szy\u002FTOP-SKILLS\u002Fblob\u002Fmaster\u002Fskills\u002Fskills\u002Ffrontend-design\u002FSKILL.md",[],[],{"skillNo":86,"name":87,"nameZh":6,"description":88,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":72,"licenseFileUrl":89,"sourceHtmlUrl":90,"categories":91,"keywords":92,"publishedAt":19},"01M747NHAY5BE9GXEB31H255R1","claude-api","Reference for the Claude API \u002F Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration.\nTRIGGER — read BEFORE opening the target file; don't skip because it \"looks like a one-liner\" — whenever: the prompt names Claude\u002FAnthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing\u002Fmodel choice\u002Flimits\u002Fcaching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent\u002FMCP\u002Ftool-definition\u002Fmulti-agent\u002FRAG\u002FLLM-judge\u002Fcomputer-use; generate\u002Fsummarize\u002Fextract\u002Fclassify\u002Frewrite\u002Fconverse over NL; debugging refusals\u002Fcutoffs\u002Fstreaming\u002Ftool-calls\u002Ftokens).\nSKIP only when another provider is being worked on (overrides all triggers): OpenAI\u002FGPT\u002FGemini\u002FLlama\u002FMistral\u002FCohere\u002FOllama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST if no provider named — don't Read the file).","https:\u002F\u002Fgithub.com\u002Fbg-szy\u002FTOP-SKILLS\u002Fblob\u002Fmaster\u002Fskills\u002Fskills\u002Fclaude-api\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fbg-szy\u002FTOP-SKILLS\u002Fblob\u002Fmaster\u002Fskills\u002Fskills\u002Fclaude-api\u002FSKILL.md",[],[],{"skillNo":94,"name":95,"nameZh":6,"description":96,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":97,"sourceHtmlUrl":98,"categories":99,"keywords":100,"publishedAt":19},"01M73WM4TPHEJ57D7G2AZ0ABVT","inbox-guardian-for-gmail","Review a personal Gmail inbox with local, owner-approved spam rules. Use for audit-first quarantine, Trash, repeated-evidence sender learning, and header review. Do not use for automatic unsubscribe requests or unreviewed mailbox actions.","https:\u002F\u002Fgithub.com\u002Fbg-szy\u002FTOP-SKILLS\u002Fblob\u002Fmaster\u002Fskills\u002Fmarketplace\u002Finbox-guardian-for-gmail\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fbg-szy\u002FTOP-SKILLS\u002Fblob\u002Fmaster\u002Fskills\u002Fmarketplace\u002Finbox-guardian-for-gmail\u002FSKILL.md",[],[]]