{
  "ok": true,
  "resource": {
    "id": "RES_04CF577D2FA9",
    "resource_type": "skill",
    "canonical_url": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/1e5eeffbdad3749125afe7ab48a39694e27f181c/skills/bulk-rnaseq",
    "name": "bulk-rnaseq",
    "summary_en": "End-to-end bulk RNA-seq orchestrator — takes raw FASTQ reads through QC and trimming (FastQC, fastp/Trim Galore), alignment and quantification (STAR, Salmon, featureCounts), assembles a gene-level counts matrix, then hands off to differential expression (pydeseq2), pathway/GSEA enrichment (pathway-enrichment), and publication figures (scientific-visualization). Use whenever the user has bulk RNA-seq reads or quant output and wants a complete, reproducible differential-expression workflow — e.g. \"analyze my RNA-seq\", \"FASTQ to DESeq2\", \"run nf-core/rnaseq\", \"STAR/Salmon quantification\", \"build a counts matrix for DESeq2\", or \"go from reads to differentially expressed genes and enriched pathways\". Routes between an nf-core/rnaseq (Nextflow) path and a standalone STAR/Salmon path, and covers experimental design, strandedness, and QC gates. For single-cell RNA-seq use the scanpy skill instead.",
    "summary_zh": "端到端批量RNA-seq分析编排，从FASTQ到差异表达与通路富集。",
    "description_en": "This skill orchestrates a complete bulk RNA-seq differential-expression workflow, from raw FASTQ reads through QC, trimming, alignment, and quantification to a gene-level counts matrix, then hands off to downstream skills for differential expression, pathway enrichment, and figure generation. It supports both an nf-core/rnaseq (Nextflow) path and a standalone STAR/Salmon path, and includes guidance on experimental design and QC. The skill is intended for users with bulk RNA-seq data who want a reproducible analysis; for single-cell data, the scanpy skill should be used instead.",
    "description_zh": "该技能编排完整的批量RNA-seq差异表达工作流，从原始FASTQ读段经过QC、修剪、比对和定量，生成基因水平计数矩阵，然后移交给下游技能进行差异表达、通路富集和图表生成。它支持nf-core/rnaseq（Nextflow）路径和独立STAR/Salmon路径，并包含实验设计和QC指导。该技能适用于拥有批量RNA-seq数据并希望进行可复现分析的用户；对于单细胞数据，应改用scanpy技能。",
    "provider_name": "K-Dense-AI",
    "homepage_url": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/bulk-rnaseq",
    "endpoint_url": null,
    "source_url": "https://raw.githubusercontent.com/K-Dense-AI/scientific-agent-skills/1e5eeffbdad3749125afe7ab48a39694e27f181c/skills/bulk-rnaseq/SKILL.md",
    "icon_url": null,
    "cover_image_url": null,
    "capabilities": [
      "object.action"
    ],
    "categories": [],
    "industries": [],
    "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/bulk-rnaseq/SKILL.md",
        "commit": "1e5eeffbdad3749125afe7ab48a39694e27f181c",
        "stars": 42124,
        "forks": 3870,
        "updated_at": "2026-09-03T02:47:03Z"
      },
      "skill": {
        "compatibility": null,
        "allowed_tools": null,
        "content_hash": "d1ec11577f5693a2490abd53e31ef83799358d6da1e44a1769636823565e68f7",
        "line_count": 216
      },
      "review_gate": {
        "passed": true,
        "reasons": [],
        "policy_version": "review-gate-v1",
        "evaluated_at": "2026-09-04T04:30:09.479Z"
      },
      "catalog_profile": {
        "version": "catalog-profile-v1",
        "primary_category": null,
        "integrations": [
          "GitHub"
        ],
        "use_cases": {
          "en": [
            "Run a complete bulk RNA-seq differential expression analysis from raw FASTQ files.",
            "Generate a gene-level counts matrix from STAR, Salmon, or featureCounts output for DESeq2.",
            "Configure and run nf-core/rnaseq pipeline for reproducible RNA-seq analysis."
          ],
          "zh": [
            "从原始FASTQ文件运行完整的批量RNA-seq差异表达分析。",
            "从STAR、Salmon或featureCounts输出生成用于DESeq2的基因水平计数矩阵。",
            "配置并运行nf-core/rnaseq流程，实现可复现的RNA-seq分析。"
          ]
        },
        "categories": [],
        "industries": [],
        "copy": {
          "source_language": "en",
          "summary_zh_state": "present",
          "description_zh_state": "present",
          "description_en_state": "present"
        }
      }
    },
    "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-v3",
      "capability_definition": "strong",
      "contract_completeness": "strong",
      "access_friction": "low",
      "operational_transparency": "strong",
      "evidence_strength": "source_inspected",
      "use_readiness": "ready_for_guidance",
      "reviewed_at": "2026-09-04T04:30:10.392Z"
    },
    "experience_count": 0,
    "verified_experience_count": 0,
    "usage_count": 0,
    "success_rate": null,
    "first_seen_at": "2026-09-03T02:50:29.774Z",
    "last_seen_at": "2026-09-04T04:30:09.721Z",
    "published_at": "2026-09-03T06:24:55.069Z",
    "created_at": "2026-09-03T02:50:29.774Z",
    "updated_at": "2026-09-04T04:30:10.392Z",
    "api_url": "https://aiworkshub.io/api/v1/resources/RES_04CF577D2FA9",
    "reviews_url": "https://aiworkshub.io/api/v1/resources/RES_04CF577D2FA9/reviews",
    "public_url": "https://aiworkshub.io/resources/RES_04CF577D2FA9",
    "distribution": {
      "canonical_identity": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/1e5eeffbdad3749125afe7ab48a39694e27f181c/skills/bulk-rnaseq",
      "syndicatable": true,
      "origins": [
        {
          "registry_url": "https://github.com/",
          "upstream_resource_id": "K-Dense-AI/scientific-agent-skills:skills/bulk-rnaseq/SKILL.md",
          "upstream_record_url": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/1e5eeffbdad3749125afe7ab48a39694e27f181c/skills/bulk-rnaseq",
          "relation": "syndicated",
          "metadata": {
            "source": "github-skill-import"
          },
          "first_seen_at": "2026-09-03T02:50:29.774Z",
          "last_seen_at": "2026-09-04T04:30:09.721Z"
        }
      ]
    },
    "tools": [],
    "review_profile": {
      "schema_version": "aiworkshub.resource-review/0.2",
      "method_version": "capability-evidence-v3",
      "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": "ready_for_guidance"
      },
      "profile": {
        "capabilities": [
          {
            "id": "object.action",
            "weight": 100,
            "confidence": 90,
            "evidence_ids": [
              "source-record"
            ]
          }
        ],
        "domains": [
          {
            "id": "science-simulation",
            "weight": 100,
            "confidence": 90,
            "evidence_ids": [
              "source-record"
            ]
          }
        ],
        "workflow_roles": [
          "analyze",
          "execute",
          "instruct"
        ]
      },
      "catalog_profile": {
        "version": "catalog-profile-v1",
        "primary_category": "science-simulation",
        "integrations": [
          "GitHub"
        ],
        "use_cases": {
          "en": [
            "Run a complete bulk RNA-seq differential expression analysis from raw FASTQ files.",
            "Generate a gene-level counts matrix from STAR, Salmon, or featureCounts output for DESeq2.",
            "Configure and run nf-core/rnaseq pipeline for reproducible RNA-seq analysis."
          ],
          "zh": [
            "从原始FASTQ文件运行完整的批量RNA-seq差异表达分析。",
            "从STAR、Salmon或featureCounts输出生成用于DESeq2的基因水平计数矩阵。",
            "配置并运行nf-core/rnaseq流程，实现可复现的RNA-seq分析。"
          ]
        }
      },
      "capability_units": [
        {
          "id": "skill:bulk-rnaseq",
          "kind": "skill_action",
          "capability_id": "object.action",
          "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": {},
      "operational_flags": [],
      "trust": {
        "signal": "none",
        "evidence_ids": [],
        "reason": null
      },
      "fit": {
        "good_for": [
          "Bulk RNA-seq differential expression analysis from raw FASTQ files",
          "Generating gene-level counts matrices for DESeq2",
          "Running nf-core/rnaseq pipelines"
        ],
        "not_for": [
          "Single-cell RNA-seq analysis (use scanpy skill)"
        ]
      },
      "evidence": [
        {
          "id": "source-record",
          "type": "source_record",
          "source": "https://raw.githubusercontent.com/K-Dense-AI/scientific-agent-skills/1e5eeffbdad3749125afe7ab48a39694e27f181c/skills/bulk-rnaseq/SKILL.md",
          "observation": "Submitted Skill record for bulk-rnaseq; 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/bulk-rnaseq/SKILL.md",
          "observation": "The Skill source was fetched and inspected (15621 bytes; SHA-256 d1ec11577f5693a2490abd53e31ef83799358d6da1e44a1769636823565e68f7).",
          "observed_at": "2026-09-04T04:30:03.324Z",
          "payload": {}
        }
      ],
      "unknowns": [
        "Actual runtime behavior not tested",
        "Real-world usage not observed"
      ],
      "id": "REV_E0E9D089569E",
      "resource_id": "RES_04CF577D2FA9",
      "evaluator_type": "platform_ai",
      "provider": "deepseek",
      "model": "deepseek-v4-flash",
      "review_scope": "source-inspected-skill",
      "created_at": "2026-09-04T04:30:10.392Z"
    }
  }
}