{ "ok": true, "resource": { "id": "RES_6833D81695C6", "resource_type": "skill", "canonical_url": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/1e5eeffbdad3749125afe7ab48a39694e27f181c/skills/molecular-dynamics", "name": "molecular-dynamics", "summary_en": "Run and analyze molecular dynamics simulations with OpenMM and MDAnalysis. Set up protein/small molecule systems, define force fields, run energy minimization and production MD, analyze trajectories (RMSD, RMSF, contact maps, free energy surfaces). For structural biology, drug binding, and biophysics.", "summary_zh": "使用OpenMM和MDAnalysis运行和分析分子动力学模拟。设置蛋白质/小分子系统，定义力场，运行能量最小化和生产MD，分析轨迹（RMSD、RMSF、接触图、自由能面）。适用于结构生物学、药物结合和生物物理学。", "description_en": "This Agent Skill provides guidance and code examples for running and analyzing molecular dynamics (MD) simulations using OpenMM and MDAnalysis. It covers system preparation (e.g., adding hydrogens, solvation), energy minimization, NVT/NPT equilibration, production MD runs, and trajectory analysis including RMSD, RMSF, contact maps, and free energy surfaces. The skill is intended for structural biology, drug binding, and biophysics applications. Evidence is based on the SKILL.md source file, which includes detailed Python code snippets and best practices.", "description_zh": "该Agent技能提供使用OpenMM和MDAnalysis运行和分析分子动力学（MD）模拟的指导和代码示例。涵盖系统准备（如添加氢原子、溶剂化）、能量最小化、NVT/NPT平衡、生产MD运行以及轨迹分析，包括RMSD、RMSF、接触图和自由能面。该技能适用于结构生物学、药物结合和生物物理学应用。证据基于SKILL.md源文件，其中包含详细的Python代码片段和最佳实践。", "provider_name": "K-Dense-AI", "homepage_url": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/molecular-dynamics", "endpoint_url": null, "source_url": "https://raw.githubusercontent.com/K-Dense-AI/scientific-agent-skills/1e5eeffbdad3749125afe7ab48a39694e27f181c/skills/molecular-dynamics/SKILL.md", "icon_url": null, "cover_image_url": null, "capabilities": [ "scientific.compute", "data.analyze", "code.generate" ], "categories": [ "research", "data" ], "industries": [ "health", "software" ], "protocols": [ "agent-skills" ], "auth": { "type": "none", "required": false }, "pricing": { "model": "free" }, "input_schema": {}, "output_schema": {}, "metadata": { "content_language": "en", "translation_status": "original-only", "license": { "spdx_id": "MIT", "source": "https://github.com/K-Dense-AI/scientific-agent-skills/blob/1e5eeffbdad3749125afe7ab48a39694e27f181c/LICENSE" }, "github": { "repository": "K-Dense-AI/scientific-agent-skills", "owner": "K-Dense-AI", "path": "skills/molecular-dynamics/SKILL.md", "commit": "1e5eeffbdad3749125afe7ab48a39694e27f181c", "stars": 42124, "forks": 3870, "updated_at": "2026-09-03T02:47:03Z" }, "skill": { "compatibility": null, "allowed_tools": null, "content_hash": "ca248ad20180a5ae1dc053a9b9eb13ba3a04f467783ed073b1fa4f37d59a580b", "line_count": 459 }, "review_gate": { "passed": true, "reasons": [], "policy_version": "review-gate-v1", "evaluated_at": "2026-09-03T06:09:50.540Z" } }, "latest_version": "1e5eeffbdad3", "status": "listed", "health_status": "unknown", "source_kind": "imported", "source_agent": "aiworkshub-skill-reviewer", "trust": { "signal": "none", "reason": "No suspicious or malicious evidence.", "signals": { "yellow": 0, "red": 0 } }, "review_summary": { "method_version": "capability-evidence-v2", "capability_definition": "strong", "contract_completeness": "strong", "access_friction": "low", "operational_transparency": "strong", "evidence_strength": "source_inspected", "use_readiness": "guided_use", "reviewed_at": "2026-09-03T06:09:51.361Z" }, "experience_count": 0, "verified_experience_count": 0, "usage_count": 0, "success_rate": null, "first_seen_at": "2026-09-03T02:50:43.069Z", "last_seen_at": "2026-09-03T06:09:50.765Z", "published_at": "2026-09-03T06:09:50.765Z", "created_at": "2026-09-03T02:50:43.069Z", "updated_at": "2026-09-03T06:09:51.361Z", "api_url": "https://aiworkshub.io/api/v1/resources/RES_6833D81695C6", "reviews_url": "https://aiworkshub.io/api/v1/resources/RES_6833D81695C6/reviews", "public_url": "https://aiworkshub.io/resources/RES_6833D81695C6", "distribution": { "canonical_identity": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/1e5eeffbdad3749125afe7ab48a39694e27f181c/skills/molecular-dynamics", "syndicatable": true, "origins": [ { "registry_url": "https://github.com/", "upstream_resource_id": "K-Dense-AI/scientific-agent-skills:skills/molecular-dynamics/SKILL.md", "upstream_record_url": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/1e5eeffbdad3749125afe7ab48a39694e27f181c/skills/molecular-dynamics", "relation": "syndicated", "metadata": { "source": "github-skill-import" }, "first_seen_at": "2026-09-03T02:50:43.069Z", "last_seen_at": "2026-09-03T06:09:50.765Z" } ] }, "tools": [], "review_profile": { "schema_version": "aiworkshub.resource-review/0.2", "method_version": "capability-evidence-v2", "review_state": { "identity": "verified", "source": "verified", "connectivity": "unknown", "contract": "strong", "safe_use": "untested", "real_use": "unobserved" }, "assessment": { "capability_definition": "strong", "contract_completeness": "strong", "access_friction": "low", "operational_transparency": "strong", "evidence_strength": "source_inspected", "use_readiness": "guided_use" }, "profile": { "capabilities": [ { "id": "scientific.compute", "weight": 100, "confidence": 90, "evidence_ids": [ "source-record", "source-inspection" ] }, { "id": "data.analyze", "weight": 80, "confidence": 90, "evidence_ids": [ "source-record", "source-inspection" ] }, { "id": "code.generate", "weight": 70, "confidence": 80, "evidence_ids": [ "source-inspection" ] } ], "domains": [ { "id": "structural-biology", "weight": 100, "confidence": 90, "evidence_ids": [ "source-record" ] }, { "id": "biophysics", "weight": 90, "confidence": 90, "evidence_ids": [ "source-record" ] }, { "id": "drug-discovery", "weight": 80, "confidence": 80, "evidence_ids": [ "source-record" ] } ], "workflow_roles": [ "analyze", "design", "execute", "generate" ] }, "capability_units": [ { "id": "skill:molecular-dynamics", "kind": "skill_action", "capability_id": "scientific.compute", "effect": "unknown", "open_world": false, "idempotency": "unknown", "confirmation": "explicit policy", "input_contract": "natural_language", "output_contract": "natural_language", "accepts": [ "task context" ], "produces": [ "task result" ], "runtime_auth": {}, "pricing": {}, "evidence_ids": [ "source-inspection" ], "metadata": {} } ], "access": { "distribution_license": { "spdx_id": "MIT", "source": "https://github.com/K-Dense-AI/scientific-agent-skills/blob/1e5eeffbdad3749125afe7ab48a39694e27f181c/LICENSE" }, "runtime_auth": { "type": "none", "required": false }, "runtime_pricing": { "model": "free" }, "dependencies": [ "openmm", "mdanalysis", "nglview", "pdbfixer", "openff-toolkit" ] }, "operational_flags": [], "trust": { "signal": "none", "evidence_ids": [], "reason": "No suspicious or malicious evidence." }, "fit": { "good_for": [ "Running molecular dynamics simulations for protein stability, drug binding, conformational sampling, and protein-protein interactions.", "Analyzing MD trajectories for RMSD, RMSF, contact maps, and free energy surfaces.", "Educational purposes and as a starting point for MD workflows." ], "not_for": [ "Production-ready high-throughput MD pipelines without additional validation.", "Simulations requiring specialized force fields or advanced sampling techniques not covered in the skill.", "Direct integration with external MD software beyond OpenMM and MDAnalysis." ] }, "evidence": [ { "id": "source-record", "type": "source_record", "source": "https://raw.githubusercontent.com/K-Dense-AI/scientific-agent-skills/1e5eeffbdad3749125afe7ab48a39694e27f181c/skills/molecular-dynamics/SKILL.md", "observation": "Submitted Skill record for molecular-dynamics; declared license MIT.", "observed_at": "2026-09-03T02:47:03Z", "payload": {} }, { "id": "source-inspection", "type": "source_inspection", "source": "https://raw.githubusercontent.com/K-Dense-AI/scientific-agent-skills/1e5eeffbdad3749125afe7ab48a39694e27f181c/skills/molecular-dynamics/SKILL.md", "observation": "The Skill source was fetched and inspected (14721 bytes; SHA-256 ca248ad20180a5ae1dc053a9b9eb13ba3a04f467783ed073b1fa4f37d59a580b).", "observed_at": "2026-09-03T06:09:42.806Z", "payload": {} } ], "unknowns": [ "Actual runtime behavior and performance of the provided code snippets have not been tested.", "Compatibility with specific OpenMM and MDAnalysis versions is not verified.", "Whether the skill has been used in real-world projects is unobserved.", "The skill's effectiveness for specific use cases (e.g., membrane simulations) is not empirically validated." ], "id": "REV_80C1F9CFFAC4", "resource_id": "RES_6833D81695C6", "evaluator_type": "platform_ai", "provider": "deepseek", "model": "deepseek-v4-flash", "review_scope": "source-inspected-skill", "created_at": "2026-09-03T06:09:51.361Z" } } }