{
  "ok": true,
  "resource": {
    "id": "RES_C83B2EE306C8",
    "resource_type": "skill",
    "canonical_url": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/1e5eeffbdad3749125afe7ab48a39694e27f181c/skills/pennylane",
    "name": "pennylane",
    "summary_en": "Hardware-agnostic quantum ML framework with automatic differentiation. Use when training quantum circuits via gradients, building hybrid quantum-classical models, or needing device portability across IBM/Google/Rigetti/IonQ. Best for variational algorithms (VQE, QAOA), quantum neural networks, and integration with PyTorch or JAX. For hardware-specific optimizations use qiskit (IBM) or cirq (Google); for open quantum systems use qutip.",
    "summary_zh": "硬件无关的量子机器学习框架，支持自动微分。适用于通过梯度训练量子电路、构建混合量子-经典模型，或需要在IBM/Google/Rigetti/IonQ等设备间移植的场景。最适合变分算法（VQE、QAOA）、量子神经网络以及与PyTorch或JAX的集成。如需硬件特定优化，请使用qiskit（IBM）或cirq（Google）；如需开放量子系统，请使用qutip。",
    "description_en": "This Agent Skill provides procedural knowledge for using PennyLane, a hardware-agnostic quantum machine learning framework with automatic differentiation. The skill covers quantum circuit construction, quantum machine learning, quantum chemistry, device management, optimization, and advanced features. It includes installation instructions for PennyLane and plugins, quick-start examples, common workflows (e.g., variational classifier, VQE), and best practices. The skill is designed for training quantum circuits via gradients, building hybrid quantum-classical models, and achieving device portability across IBM, Google, Rigetti, and IonQ. It is best suited for variational algorithms (VQE, QAOA), quantum neural networks, and integration with PyTorch or JAX. The skill also provides guidance on when to use alternative frameworks (qiskit, cirq, qutip) for specific use cases.",
    "description_zh": "该Agent技能提供了使用PennyLane的程序性知识，PennyLane是一个硬件无关的量子机器学习框架，支持自动微分。技能涵盖量子电路构建、量子机器学习、量子化学、设备管理、优化和高级功能。包括PennyLane及其插件的安装说明、快速入门示例、常见工作流（如变分分类器、VQE）和最佳实践。该技能旨在通过梯度训练量子电路、构建混合量子-经典模型，并实现跨IBM、Google、Rigetti和IonQ的设备可移植性。最适合变分算法（VQE、QAOA）、量子神经网络以及与PyTorch或JAX的集成。技能还提供了在特定用例下何时使用替代框架（qiskit、cirq、qutip）的指导。",
    "provider_name": "K-Dense-AI",
    "homepage_url": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/pennylane",
    "endpoint_url": null,
    "source_url": "https://raw.githubusercontent.com/K-Dense-AI/scientific-agent-skills/1e5eeffbdad3749125afe7ab48a39694e27f181c/skills/pennylane/SKILL.md",
    "icon_url": null,
    "cover_image_url": null,
    "capabilities": [
      "quantum-circuit.construct",
      "quantum-ml.train",
      "quantum-chemistry.simulate",
      "device.manage",
      "optimization.run",
      "advanced-features.use"
    ],
    "categories": [
      "developer-tools",
      "research"
    ],
    "industries": [
      "software",
      "education"
    ],
    "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/pennylane/SKILL.md",
        "commit": "1e5eeffbdad3749125afe7ab48a39694e27f181c",
        "stars": 42124,
        "forks": 3870,
        "updated_at": "2026-09-03T02:47:03Z"
      },
      "skill": {
        "compatibility": null,
        "allowed_tools": "Read Bash Python",
        "content_hash": "1d24f143290a3b2115853308873a5297af64b0343b09472062fc288b49201f84",
        "line_count": 257
      },
      "review_gate": {
        "passed": true,
        "reasons": [],
        "policy_version": "review-gate-v1",
        "evaluated_at": "2026-09-03T05:54:05.749Z"
      }
    },
    "latest_version": "1e5eeffbdad3",
    "status": "listed",
    "health_status": "unknown",
    "source_kind": "imported",
    "source_agent": "aiworkshub-skill-reviewer",
    "trust": {
      "signal": "none",
      "reason": null,
      "signals": {
        "yellow": 0,
        "red": 0
      }
    },
    "review_summary": {
      "method_version": "capability-evidence-v2",
      "capability_definition": "strong",
      "contract_completeness": "natural_language",
      "access_friction": "low",
      "operational_transparency": "partial",
      "evidence_strength": "source_inspected",
      "use_readiness": "guided_use",
      "reviewed_at": "2026-09-03T05:54:06.566Z"
    },
    "experience_count": 0,
    "verified_experience_count": 0,
    "usage_count": 0,
    "success_rate": null,
    "first_seen_at": "2026-09-03T02:50:48.010Z",
    "last_seen_at": "2026-09-03T05:54:05.970Z",
    "published_at": "2026-09-03T05:54:05.970Z",
    "created_at": "2026-09-03T02:50:48.010Z",
    "updated_at": "2026-09-03T05:54:06.566Z",
    "api_url": "https://aiworkshub.io/api/v1/resources/RES_C83B2EE306C8",
    "reviews_url": "https://aiworkshub.io/api/v1/resources/RES_C83B2EE306C8/reviews",
    "public_url": "https://aiworkshub.io/resources/RES_C83B2EE306C8",
    "distribution": {
      "canonical_identity": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/1e5eeffbdad3749125afe7ab48a39694e27f181c/skills/pennylane",
      "syndicatable": true,
      "origins": [
        {
          "registry_url": "https://github.com/",
          "upstream_resource_id": "K-Dense-AI/scientific-agent-skills:skills/pennylane/SKILL.md",
          "upstream_record_url": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/1e5eeffbdad3749125afe7ab48a39694e27f181c/skills/pennylane",
          "relation": "syndicated",
          "metadata": {
            "source": "github-skill-import"
          },
          "first_seen_at": "2026-09-03T02:50:48.010Z",
          "last_seen_at": "2026-09-03T05:54:05.970Z"
        }
      ]
    },
    "tools": [],
    "review_profile": {
      "schema_version": "aiworkshub.resource-review/0.2",
      "method_version": "capability-evidence-v2",
      "review_state": {
        "identity": "verified",
        "source": "verified",
        "connectivity": "unknown",
        "contract": "partial",
        "safe_use": "untested",
        "real_use": "unobserved"
      },
      "assessment": {
        "capability_definition": "strong",
        "contract_completeness": "natural_language",
        "access_friction": "low",
        "operational_transparency": "partial",
        "evidence_strength": "source_inspected",
        "use_readiness": "guided_use"
      },
      "profile": {
        "capabilities": [
          {
            "id": "quantum-circuit.construct",
            "weight": 90,
            "confidence": 90,
            "evidence_ids": [
              "source-record",
              "source-inspection"
            ]
          },
          {
            "id": "quantum-ml.train",
            "weight": 90,
            "confidence": 90,
            "evidence_ids": [
              "source-record",
              "source-inspection"
            ]
          },
          {
            "id": "quantum-chemistry.simulate",
            "weight": 80,
            "confidence": 80,
            "evidence_ids": [
              "source-record",
              "source-inspection"
            ]
          },
          {
            "id": "device.manage",
            "weight": 80,
            "confidence": 80,
            "evidence_ids": [
              "source-record",
              "source-inspection"
            ]
          },
          {
            "id": "optimization.run",
            "weight": 80,
            "confidence": 80,
            "evidence_ids": [
              "source-record",
              "source-inspection"
            ]
          },
          {
            "id": "advanced-features.use",
            "weight": 70,
            "confidence": 70,
            "evidence_ids": [
              "source-record",
              "source-inspection"
            ]
          }
        ],
        "domains": [
          {
            "id": "quantum-computing",
            "weight": 100,
            "confidence": 90,
            "evidence_ids": [
              "source-record",
              "source-inspection"
            ]
          },
          {
            "id": "machine-learning",
            "weight": 80,
            "confidence": 80,
            "evidence_ids": [
              "source-record",
              "source-inspection"
            ]
          },
          {
            "id": "chemistry",
            "weight": 60,
            "confidence": 60,
            "evidence_ids": [
              "source-record",
              "source-inspection"
            ]
          }
        ],
        "workflow_roles": [
          "design",
          "execute",
          "analyze"
        ]
      },
      "capability_units": [
        {
          "id": "skill:pennylane",
          "kind": "skill_action",
          "capability_id": "quantum-computing.execute",
          "effect": "compute",
          "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": {},
        "runtime_pricing": {},
        "dependencies": []
      },
      "operational_flags": [],
      "trust": {
        "signal": "none",
        "evidence_ids": [],
        "reason": null
      },
      "fit": {
        "good_for": [
          "Training quantum circuits via gradient-based optimization",
          "Building hybrid quantum-classical models with PyTorch or JAX",
          "Running variational algorithms (VQE, QAOA)",
          "Simulating quantum chemistry and molecular ground states",
          "Porting quantum circuits across different hardware backends"
        ],
        "not_for": [
          "Hardware-specific optimizations (use qiskit or cirq)",
          "Open quantum systems simulation (use qutip)"
        ]
      },
      "evidence": [
        {
          "id": "source-record",
          "type": "source_record",
          "source": "https://raw.githubusercontent.com/K-Dense-AI/scientific-agent-skills/1e5eeffbdad3749125afe7ab48a39694e27f181c/skills/pennylane/SKILL.md",
          "observation": "Submitted Skill record for pennylane; 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/pennylane/SKILL.md",
          "observation": "The Skill source was fetched and inspected (9295 bytes; SHA-256 1d24f143290a3b2115853308873a5297af64b0343b09472062fc288b49201f84).",
          "observed_at": "2026-09-03T05:53:56.654Z",
          "payload": {}
        }
      ],
      "unknowns": [
        "Actual runtime behavior and performance not tested",
        "Compatibility with specific agent frameworks not verified",
        "Real-world usage not observed"
      ],
      "id": "REV_E4667BD7F61D",
      "resource_id": "RES_C83B2EE306C8",
      "evaluator_type": "platform_ai",
      "provider": "deepseek",
      "model": "deepseek-v4-flash",
      "review_scope": "source-inspected-skill",
      "created_at": "2026-09-03T05:54:06.566Z"
    }
  }
}