{"slug":"k-dense-ai-arboreto","name":"arboreto","description":"Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.","long_description":"---\nname: arboreto\ndescription: Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.\nlicense: BSD-3-Clause license\nmetadata:\n  version: \"1.0\"\n  skill-author: K-Dense Inc.\n---\n\n# Arboreto\n\n## Overview\n\nArboreto is a Python library from [Aerts Lab](https://github.com/aertslab/arboreto) for inferring gene regulatory networks (GRNs) from gene expression data. It parallelizes tree-based ensemble regression (GRNBoost2, GENIE3) with [Dask](https://distributed.dask.org/) across local cores or remote clusters.\n\n**Core capability**: Identify which transcription factors (TFs) regulate which target genes based on expression patterns across observations (cells, samples, conditions).\n\n**Upstream**: PyPI **0.1.6** (2021-02-09, latest). Docs: [arboreto.readthedocs.io](https://arboreto.readthedocs.io/en/latest/). Primary downstream consumer: [pySCENIC](https://github.com/aertslab/pySCENIC).\n\n## Quick Start\n\nInstall arboreto:\n```bash\nuv pip install arboreto\n```\n\nBasic GRN inference:\n```python\nimport pandas as pd\nfrom arboreto.algo import grnboost2\n\nif __name__ == '__main__':\n    # Load expression data (genes as columns)\n    expression_matrix = pd.read_csv('expression_data.tsv', sep='\\t')\n\n    # Infer regulatory network\n    network = grnboost2(expression_data=expression_matrix)\n\n    # Save results (TF, target, importance)\n    network.to_csv('network.tsv', sep='\\t', index=False, header=False)\n```\n\n**Critical**: Always use `if __name__ == '__main__':` guard because Dask spawns new processes.\n\n## Core Capabilities\n\n### 1. Basic GRN Inference\n\nFor standard GRN inference workflows including:\n- Input data preparation (Pandas DataFrame or NumPy array)\n- Running inference with GRNBoost2 or GENIE3\n- Filtering by transcription factors\n- Output format and interpretation\n\n**See**: `references/basic_inference.md`\n\n**Use the ready-to-run script**: `scripts/basic_grn_inference.py` for standard inference tasks:\n```bash\npython scripts/basic_grn_inference.py expression_data.tsv output_network.tsv --tf-file tfs.txt --seed 777 --limit 5000\n```\n\n### 2. Algorithm Selection\n\nArboreto provides two algorithms:\n\n**GRNBoost2 (Recommended)**:\n- Fast gradient boosting-based inference\n- Optimized for large datasets (10k+ observations)\n- Default choice for most analyses\n\n**GENIE3**:\n- Random Forest-based inference\n- Original multiple regression approach\n- Use for comparison or validation\n\nQuick comparison:\n```python\nfrom arboreto.algo import grnboost2, genie3\n\n# Fast, recommended\nnetwork_grnboost = grnboost2(expression_data=matrix)\n\n# Classic algorithm\nnetwork_genie3 = genie3(expression_data=matrix)\n```\n\n**For detailed algorithm comparison, parameters, and selection guidance**: `references/algorithms.md`\n\n### 3. Distributed Computing\n\nScale inference from local multi-core to cluster environments:\n\n**Local (default)** - Uses all available cores automatically:\n```python\nnetwork = grnboost2(expression_data=matrix)\n```\n\n**Custom local client** - Control resources:\n```python\nfrom distributed import LocalCluster, Client\n\nlocal_cluster = LocalCluster(n_workers=10, memory_limit='8GB')\nclient = Client(local_cluster)\n\nnetwork = grnboost2(expression_data=matrix, client_or_address=client)\n\nclient.close()\nlocal_cluster.close()\n```\n\n**Cluster computing** - Connect to remote Dask scheduler:\n```python\nfrom distributed import Client\n\nclient = Client('tcp://scheduler:8786')\nnetwork = grnboost2(expression_data=matrix, client_or_address=client)\n```\n\n**For cluster setup, performance optimization, and large-scale workflows**: `references/distributed_computing.md`\n\n## Installation\n\n```bash\nuv pip install arboreto\n```\n\nConda (Bioconda):\n\n```bash\nconda install -c bioconda arboreto\n```\n\n**Dependencies** (from upstream `requirements.txt`): `dask[complete]`, `distributed`, `numpy`, `pandas`, `scikit-learn`, `scipy`\n\n**Input formats**: pandas DataFrame, dense `numpy.ndarray`, or sparse `scipy.sparse.csc_matrix` (rows = observations, columns = genes). For array/matrix inputs, pass `gene_names` explicitly.\n\n## Common Use Cases\n\n### Single-Cell RNA-seq Analysis\n```python\nimport pandas as pd\nfrom arboreto.algo import grnboost2\n\nif __name__ == '__main__':\n    # Load single-cell expression matrix (cells x genes)\n    sc_data = pd.read_csv('scrna_counts.tsv', sep='\\t')\n\n    # Infer cell-type-specific regulatory network\n    network = grnboost2(expression_data=sc_data, seed=42)\n\n    # Filter high-confidence links\n    high_confidence = network[network['importance'] > 0.5]\n    high_confidence.to_csv('grn_high_confidence.tsv', sep='\\t', index=False)\n```\n\n### Bulk RNA-seq with TF Filtering\n```python\nfrom arboreto.utils import load_tf_names\nfrom arboreto.algo import grnboost2\n\nif __name__ == '__main__':\n    # Load data\n    expression_data = pd.read_csv('rnaseq_tpm.tsv', sep='\\t')\n    tf_names = load_tf_names('human_tfs.txt')\n\n    # Infer with TF restriction\n    network = grnboost2(\n        expression_data=expression_data,\n        tf_names=tf_names,\n        seed=123\n    )\n\n    network.to_csv('tf_target_network.tsv', sep='\\t', index=False)\n```\n\n### Comparative Analysis (Multiple Conditions)\n```python\nfrom arboreto.algo import grnboost2\n\nif __name__ == '__main__':\n    # Infer networks for different conditions\n    conditions = ['control', 'treatment_24h', 'treatment_48h']\n\n    for condition in conditions:\n        data = pd.read_csv(f'{condition}_expression.tsv', sep='\\t')\n        network = grnboost2(expression_data=data, seed=42)\n        network.to_csv(f'{condition}_network.tsv', sep='\\t', index=False)\n```\n\n## Output Interpretation\n\nArboreto returns a DataFrame with regulatory links:\n\n| Column | Description |\n|--------|-------------|\n| `TF` | Transcription factor (regulator) |\n| `target` | Target gene |\n| `importance` | Regulatory importance score (higher = stronger) |\n\n**Filtering strategy**:\n- `limit=N` at inference time (return top N links globally)\n- Post-hoc importance threshold (e.g., > 0.5)\n- Top links per target via `groupby('target')`\n- Statistical significance testing (permutation tests, external tools)\n\n## Integration with pySCENIC\n\nArboreto powers the GRN inference step in [pySCENIC](https://github.com/aertslab/pySCENIC). pySCENIC 0.11+ passes sparse expression matrices to `grnboost2` / `genie3`; pySCENIC 0.12+ defaults to `arboreto_with_multiprocessing.py` (no Dask) for compatibility — use standalone arboreto when you need Dask scaling.\n\n```python\n# Standalone: infer co-expression modules before pySCENIC cisTarget pruning\nfrom arboreto.algo import grnboost2\n\nnetwork = grnboost2(expression_data=expression_df, tf_names=tf_list, limit=5000)\n\n# Downstream: pySCENIC ctx pruning, regulon definition, AUCell (see pySCENIC docs)\n```\n\nConvert AnnData to a DataFrame for arboreto directly:\n\n```python\nexpression_df = adata.to_df()  # cells x genes\n```\n\n## Reproducibility\n\nAlways set a seed for reproducible results:\n```python\nnetwork = grnboost2(expression_data=matrix, seed=777)\n```\n\nRun multiple seeds for robustness analysis:\n```python\nfrom distributed import LocalCluster, Client\n\nif __name__ == '__main__':\n    client = Client(LocalCluster())\n\n    seeds = [42, 123, 777]\n    networks = []\n\n    for seed in seeds:\n        net = grnboost2(expression_data=matrix, client_or_address=client, seed=seed)\n        networks.append(net)\n\n    # Consensus: links recurring across runs (example: mean importance per TF-target pair)\n    import pandas as pd\n    combined = pd.concat(networks)\n    consensus = (\n        combined.groupby(['TF', 'target'], as_index=False)['importance']\n        .mean()\n        .query('importance > 0.5')\n    )\n```\n\n## Troubleshooting\n\n**Memory errors**: Reduce dataset size by filtering low-variance genes or use distributed computing\n\n**Slow performance**: Use GRNBoost2 instead of GENIE3, enable distributed client, filter TF list\n\n**Dask errors**: Ensure `if __name__ == '__main__':` guard is present in scripts (required on Windows/macOS with spawn-based multiprocessing)\n\n**Empty results**: Check data format (genes as columns), verify TF names match column names in the expression matrix\n\n**Sparse data**: Use `scipy.sparse.csc_matrix` and pass matching `gene_names`; supported since arboreto 0.1.6 / pySCENIC 0.11\n\n","tagline":"Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). 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adoption","score":100,"weight":0.13,"status":"pass","detail":"34K GitHub stars"},{"id":"repo_activity","label":"Stars/forks activity","score":97,"weight":0.08,"status":"pass","detail":"34K stars, 3.3K forks; issue activity unavailable in current metadata"},{"id":"maintenance","label":"Recent maintenance","score":100,"weight":0.14,"status":"pass","detail":"2d since push"},{"id":"license","label":"License clarity","score":86,"weight":0.09,"status":"pass","detail":"BSD-3-Clause license"},{"id":"documentation","label":"README/SKILL.md completeness","score":86,"weight":0.14,"status":"pass","detail":"Metadata includes enough usage and workflow context"},{"id":"dependency_risk","label":"Dependency/runtime risk","score":62,"weight":0.12,"status":"info","detail":"command execution surface, external package install surface"},{"id":"installability","label":"Install availability","score":92,"weight":0.1,"status":"pass","detail":"npx skills add K-Dense-AI/scientific-agent-skills --skill arboreto"},{"id":"install_safety","label":"Install command safety","score":92,"weight":0.1,"status":"pass","detail":"standard package or runtime install path"},{"id":"permission_surface","label":"Permission surface","score":48,"weight":0.07,"status":"warn","detail":"shell or command execution, filesystem or document access"},{"id":"repository","label":"Repository evidence","score":86,"weight":0.04,"status":"pass","detail":"https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/arboreto"},{"id":"review_status","label":"Review status","score":88,"weight":0.05,"status":"pass","detail":"AI review data available"},{"id":"agent_outcomes","label":"Agent Proven outcomes","score":54,"weight":0.13,"status":"info","detail":"No agent outcome data yet"}],"checks":[{"status":"pass","label":"GitHub adoption","detail":"34K GitHub stars"},{"status":"pass","label":"Stars/forks activity","detail":"34K stars, 3.3K forks; issue activity unavailable in current metadata"},{"status":"pass","label":"Recent maintenance","detail":"2d since push"},{"status":"pass","label":"License clarity","detail":"BSD-3-Clause license"},{"status":"pass","label":"README/SKILL.md completeness","detail":"Metadata includes enough usage and workflow context"},{"status":"info","label":"Dependency/runtime risk","detail":"command execution surface, external package install surface"},{"status":"pass","label":"Install availability","detail":"npx skills add K-Dense-AI/scientific-agent-skills --skill arboreto"},{"status":"pass","label":"Install command safety","detail":"standard package or runtime install path"},{"status":"warn","label":"Permission surface","detail":"shell or command execution, filesystem or document access"},{"status":"pass","label":"Repository evidence","detail":"https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/arboreto"},{"status":"pass","label":"Review status","detail":"AI review data available"},{"status":"info","label":"Agent Proven outcomes","detail":"No agent outcome data yet"},{"status":"warn","label":"Ownership","detail":"No approved owner claim yet"},{"status":"pass","label":"OpenAgentSkill usage","detail":"8 views, 0 install copies"},{"status":"info","label":"Agent outcomes","detail":"No agent outcome data yet"}],"strengths":["AI review approved","Install path is available","Repository evidence is available","Recently maintained repository","Large GitHub adoption signal","Install command has no obvious high-risk pattern"],"warnings":["Quality score needs review","Permission surface needs review: shell or command execution, filesystem or document access","Permission surface: shell or command execution, filesystem or document access"],"evidence":{"stars":"34K GitHub stars","repoActivity":"34K stars, 3.3K forks","lastPushed":"2d since push","license":"BSD-3-Clause license","repository":"https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/arboreto","install":"npx skills add K-Dense-AI/scientific-agent-skills --skill arboreto","installSafety":"standard package or runtime install path","permissionSurface":"shell or command execution, filesystem or document access","documentation":"Strong README/SKILL.md context","agentOutcomes":"No agent outcome data yet"},"installReadiness":{"ready":true,"command":"npx skills add K-Dense-AI/scientific-agent-skills --skill arboreto","policy":"human_review_before_install","label":"Human review before install","notes":["Install path is available","Repository evidence is available","License is declared","No Agent Proven outcome evidence yet","2d since push"]},"agentCompatibility":["Codex","Claude Code","Cursor","OpenAgentSkill CLI"],"riskSummary":{"level":"medium","label":"Review before production","notes":["Quality score needs review","Permission surface needs review: shell or command execution, filesystem or document access","Permission surface: shell or command execution, filesystem or document access"]},"outcomeEvidence":{"total":0,"successes":0,"failures":0,"notRelevant":0,"successRate":null,"installAttempts":0,"riskBlocked":0,"setupRequired":0,"installSuccessRate":null,"avgOutputQuality":null,"avgTimeToUsefulMs":null,"productionOutcomes":0,"humanReviewRequired":0,"recentSuccessRate":null,"recentFailureRate":null,"uniqueAgents":0,"agentProvenScore":0,"agentProvenLabel":"Needs first agent run","lastOutcomeAt":null,"label":"No agent outcome data yet"},"autoInstall":{"allowed":false,"sandboxRequired":true,"policy":"human_review_before_install","reason":"Human review or sandbox validation is required before automatic installation."},"bestFor":["data-analysis","agent-skill"],"doNotUseFor":["Production credentials, payments, or irreversible account changes without explicit human review","Sensitive private data before reviewing repository code, license, and permission surface","Automatic installation in a production workspace"],"knownRisks":["Quality score needs review","Permission surface needs review: shell or command execution, filesystem or document access","Permission surface: shell or command execution, filesystem or document access"]},"agent_proven":{"version":"agent-proven-v1","score":0,"tier":"unproven","label":"Needs first agent run","summary":"No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.","metrics":{"totalOutcomes":0,"successfulOutcomes":0,"failedOutcomes":0,"installAttempts":0,"installSuccessRate":null,"successRate":null,"recentSuccessRate":null,"recentFailureRate":null,"riskBlocked":0,"setupRequired":0,"notRelevant":0,"avgOutputQuality":null,"avgTimeToUsefulMs":null,"productionOutcomes":0,"humanReviewRequired":0,"uniqueAgents":0,"lastOutcomeAt":null},"signals":[],"penalties":["No real agent outcome evidence yet"]},"outcome_stats":null,"safety":{"score":56,"level":"review_before_install","label":"Review before install","safety_tier":{"tier":"experimental","label":"Experimental","badge":"EXPERIMENTAL","summary":"Sparse or mixed signals. Useful for discovery, but not for autonomous installation.","recommended_action":"Test manually in an isolated workspace and compare against safer alternatives.","auto_install_policy":"review","reasons":["High-risk permission hints: Shell or command execution","56/100 agent safety score"]},"auto_install_allowed":false,"human_review_required":true,"blocked":false,"audit_risk":"needs_review","permission_hints":[{"id":"shell","label":"Shell or command execution","reason":"Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.","severity":"high"},{"id":"network","label":"Network access","reason":"Skill likely fetches remote pages, APIs, repositories, or external services.","severity":"medium"},{"id":"filesystem","label":"Filesystem access","reason":"Skill may read or write project files, documents, generated artifacts, or local workspace state.","severity":"medium"},{"id":"database","label":"Database access","reason":"Skill may inspect schemas, query databases, or work with persistent stores.","severity":"medium"}],"policy_warnings":["High-risk permission hints: Shell or command execution","Permission surface may require sandboxing"],"constraints_applied":{"max_risk":"medium","needs_install_command":true,"min_stars":0}},"safety_gate":{"tier":"experimental","label":"Experimental","badge":"EXPERIMENTAL","auto_install_policy":"review","auto_install_allowed":false,"blocked":false,"human_review_required":true,"recommended_action":"Test manually in an isolated workspace and compare against safer alternatives.","reasons":["High-risk permission hints: Shell or command execution","56/100 agent safety score"]},"eval":{"version":"openagentskill-skill-eval-v1","status":"failed","score":80,"risk_level":"high","decision":{"recommendation":"do_not_auto_install","reason":"Permission surface: shell or command execution, filesystem or document access","auto_install_allowed":false,"policy":"block","human_review_required":true},"blockers":["Permission surface: shell or command execution, filesystem or document access"],"warnings":["Audit score: Needs review","Agent safety gate: Sparse or mixed signals. Useful for discovery, but not for autonomous installation.","High-risk permission hints: Shell or command execution","Permission surface may require sandboxing","Quality score needs review","Permission surface needs review: shell or command execution, filesystem or document access","Permission surface: shell or command execution, filesystem or document access"],"validation_plan":["Inspect repository, README/SKILL.md, license, and recent commits before production use.","Install in an isolated workspace or sandbox with no production secrets available.","Run the smallest representative task and record files touched, commands run, network access, and outputs.","Compare the selected skill against at least one alternative when the eval status is review or failed.","Promote only after the agent reports a successful verification result and unresolved warnings are accepted."],"checks":[{"id":"task_fit","label":"Task fit","status":"pass","score":84,"required_for_auto_install":true,"detail":"Task wording matches this skill metadata.","evidence":["Evaluate arboreto before installing it in an agent workflow","data-analysis","Workflow automation workflows; Claude Code teams; teams that value GitHub adoption signals"]},{"id":"install_path","label":"Install path","status":"pass","score":92,"required_for_auto_install":true,"detail":"Install handoff is available.","evidence":["npx skills add K-Dense-AI/scientific-agent-skills --skill arboreto"]},{"id":"install_safety","label":"Install command safety","status":"pass","score":92,"required_for_auto_install":true,"detail":"standard package or runtime install path","evidence":["npx skills add K-Dense-AI/scientific-agent-skills --skill arboreto"]},{"id":"trust_score","label":"Trust score","status":"pass","score":83,"required_for_auto_install":true,"detail":"Good trust signals with a few areas worth checking before rollout.","evidence":["Strong shortlist","34K GitHub stars","BSD-3-Clause license"]},{"id":"audit_score","label":"Audit score","status":"warn","score":88,"required_for_auto_install":true,"detail":"Needs review","evidence":["Permission surface may require sandboxing"]},{"id":"agent_safety_gate","label":"Agent safety gate","status":"warn","score":56,"required_for_auto_install":true,"detail":"Sparse or mixed signals. Useful for discovery, but not for autonomous installation.","evidence":["Test manually in an isolated workspace and compare against safer alternatives.","High-risk permission hints: Shell or command execution"]},{"id":"readme_skillmd_completeness","label":"README/SKILL.md completeness","status":"pass","score":86,"required_for_auto_install":false,"detail":"Metadata includes enough usage and workflow context","evidence":["Strong README/SKILL.md context"]},{"id":"license_clarity","label":"License clarity","status":"pass","score":86,"required_for_auto_install":true,"detail":"BSD-3-Clause license","evidence":["BSD-3-Clause license"]},{"id":"recent_maintenance","label":"Recent maintenance","status":"pass","score":100,"required_for_auto_install":false,"detail":"2d since push","evidence":["2d since push"]},{"id":"permission_surface","label":"Permission surface","status":"fail","score":48,"required_for_auto_install":true,"detail":"shell or command execution, filesystem or document access","evidence":["Shell or command execution: high","Network access: medium","Filesystem access: medium"]},{"id":"alternatives","label":"Alternatives available","status":"info","score":55,"required_for_auto_install":false,"detail":"No close alternatives were found in the current shortlist.","evidence":[]}],"endpoints":{"web":"https://www.openagentskill.com/skills/k-dense-ai-arboreto/evals","api":"/api/agent/evals?slug=k-dense-ai-arboreto","text":"/api/agent/evals?slug=k-dense-ai-arboreto&format=text"}},"agent_readable_metadata":{"version":"openagentskill-agent-metadata-v2","skill":{"slug":"k-dense-ai-arboreto","name":"arboreto","description":"Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.","category":"data-analysis","url":"https://www.openagentskill.com/skills/k-dense-ai-arboreto","repository":"https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/arboreto","github_repo":"K-Dense-AI/scientific-agent-skills"},"suited_tasks":["Workflow automation workflows","Claude Code teams","teams that value GitHub adoption signals","Move data between tools","Transform files","Trigger repeatable actions","Inspect repository metadata","Compare code changes"],"suited_agents":["Codex","Claude Code","Cursor","OpenAgentSkill CLI","CLI"],"install":{"command":"npx skills add K-Dense-AI/scientific-agent-skills --skill arboreto","ready":true,"targets":[{"id":"openagentskill-cli","label":"CLI","kind":"command","value":"npx --yes https://github.com/Leon-Drq/openagentskill/releases/download/cli-v0.2.1/openagentskill-0.2.1.tgz install k-dense-ai-arboreto"},{"id":"codex","label":"Codex","kind":"agent-prompt","value":"Install the \"arboreto\" agent skill from https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/arboreto. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"k-dense-ai-arboreto\",\"task\":\"Install arboreto\",\"agent\":\"codex\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes."},{"id":"claude-code","label":"Claude Code","kind":"agent-prompt","value":"Add \"arboreto\" as a Claude Code skill from https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/arboreto. Inspect the skill instructions, place the reusable skill files in the appropriate local skills location for this project, and report the activation steps. Skill purpose: Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"k-dense-ai-arboreto\",\"task\":\"Install arboreto\",\"agent\":\"claude-code\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes."},{"id":"cursor","label":"Cursor","kind":"agent-prompt","value":"Turn \"arboreto\" from https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/arboreto into a reusable Cursor project rule or agent instruction. Preserve the core workflow, adapt paths to this repo, and keep the rule scoped to tasks where it is relevant. Skill purpose: Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"k-dense-ai-arboreto\",\"task\":\"Install arboreto\",\"agent\":\"cursor\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes."}],"handoff_url":"https://www.openagentskill.com/api/skills/k-dense-ai-arboreto/install","manifest_url":"https://www.openagentskill.com/api/registry/manifest/k-dense-ai-arboreto"},"trust":{"score":83,"label":"Strong shortlist","version":"trust-score-v4","install_policy":"human_review_before_install","evidence":{"stars":"34K GitHub stars","repoActivity":"34K stars, 3.3K forks","lastPushed":"2d since push","license":"BSD-3-Clause license","repository":"https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/arboreto","install":"npx skills add K-Dense-AI/scientific-agent-skills --skill arboreto","installSafety":"standard package or runtime install path","permissionSurface":"shell or command execution, filesystem or document access","documentation":"Strong README/SKILL.md context","agentOutcomes":"No agent outcome data yet"},"outcome_evidence":{"total":0,"successes":0,"failures":0,"not_relevant":0,"success_rate":null,"recent_success_rate":null,"recent_failure_rate":null,"install_attempts":0,"install_success_rate":null,"risk_blocked":0,"setup_required":0,"avg_output_quality":null,"production_outcomes":0,"last_outcome_at":null,"label":"No agent outcome data yet"},"auto_install":{"allowed":false,"sandbox_required":true,"reason":"Human review or sandbox validation is required before automatic installation."},"best_for":["data-analysis","agent-skill"],"known_risks":["Quality score needs review","Permission surface needs review: shell or command execution, filesystem or document access","Permission surface: shell or command execution, filesystem or document access"]},"agent_proven":{"version":"agent-proven-v1","score":0,"tier":"unproven","label":"Needs first agent run","summary":"No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.","metrics":{"totalOutcomes":0,"successfulOutcomes":0,"failedOutcomes":0,"installAttempts":0,"installSuccessRate":null,"successRate":null,"recentSuccessRate":null,"recentFailureRate":null,"riskBlocked":0,"setupRequired":0,"notRelevant":0,"avgOutputQuality":null,"avgTimeToUsefulMs":null,"productionOutcomes":0,"humanReviewRequired":0,"uniqueAgents":0,"lastOutcomeAt":null},"signals":[],"penalties":["No real agent outcome evidence yet"]},"audit":{"score":88,"risk_level":"needs_review","risk_label":"Needs review","warnings":["Permission surface may require sandboxing","Quality score needs review","Permission surface needs review: shell or command execution, filesystem or document access","Permission surface: shell or command execution, filesystem or document access"]},"safety_gate":{"tier":"experimental","label":"Experimental","auto_install_policy":"review","auto_install_allowed":false,"human_review_required":true,"blocked":false,"recommended_action":"Test manually in an isolated workspace and compare against safer alternatives."},"quality":{"score":92,"label":"Excellent"},"supply":{"track":"Coding and developer agents","scenario":"GitHub automation","maintenance":"2d since push","risk":"Needs review"},"alternative_skills":[],"do_not_use_when":["teams that need a vendor-supported SLA","high-compliance environments without internal security review","No major risk signals from current metadata","High-risk permission hints: Shell or command execution","Permission surface may require sandboxing","Quality score needs review","Permission surface needs review: shell or command execution, filesystem or document access","Permission surface: shell or command execution, filesystem or document access"],"agent_contract":{"task_input":"Use arboreto in an agent workflow","recommended_action":"Test manually in an isolated workspace and compare against safer alternatives.","install_policy":"review","minimum_review_before_use":["Trust: 83/100 Strong shortlist","Audit: 88/100 Needs review","Safety: 56/100 Review before install","Review repository, license, install command, and permission surface before production use."],"expected_agent_output":{"selected_skill":"k-dense-ai-arboreto (arboreto)","install_command":"npx skills add K-Dense-AI/scientific-agent-skills --skill arboreto","risk_summary":"Needs review; Experimental; Review before production","verification_result":"Report the smallest successful task, files touched, warnings, and any missing setup."}},"outcome_feedback":{"endpoint":"https://www.openagentskill.com/api/agent/outcome","method":"POST","requires_resolve_event_id":true,"event_id_source":"Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.","expected_outcomes":["success","failed","not_relevant","blocked_by_risk","setup_required"],"payload_template":{"event_id":"<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>","skill_slug":"k-dense-ai-arboreto","task":"Use arboreto in an agent workflow","agent":"codex","outcome":"success","install_used":true,"risk_blocked":false,"setup_required":false,"task_success":true,"output_quality":4,"error_type":null,"human_review_required":false,"workspace":"sandbox","time_to_useful_ms":120000,"notes":"Report the smallest successful task, setup friction, files touched, and risk notes."}},"endpoints":{"web":"https://www.openagentskill.com/skills/k-dense-ai-arboreto","api":"https://www.openagentskill.com/api/agent/skills/k-dense-ai-arboreto","audit":"https://www.openagentskill.com/skills/k-dense-ai-arboreto/audit","eval":"https://www.openagentskill.com/api/agent/evals?slug=k-dense-ai-arboreto&task=Use%20arboreto%20in%20an%20agent%20workflow&max_risk=medium","resolve":"https://www.openagentskill.com/api/agent/resolve?task=Use%20arboreto%20in%20an%20agent%20workflow&agent=codex&max_risk=medium","receipt":"https://www.openagentskill.com/api/agent/receipt?task=Use%20arboreto%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text","install":"https://www.openagentskill.com/api/skills/k-dense-ai-arboreto/install","manifest":"https://www.openagentskill.com/api/registry/manifest/k-dense-ai-arboreto"}},"machine_metadata":{"version":"openagentskill-agent-metadata-v2","skill":{"slug":"k-dense-ai-arboreto","name":"arboreto","description":"Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.","category":"data-analysis","url":"https://www.openagentskill.com/skills/k-dense-ai-arboreto","repository":"https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/arboreto","github_repo":"K-Dense-AI/scientific-agent-skills"},"suited_tasks":["Workflow automation workflows","Claude Code teams","teams that value GitHub adoption signals","Move data between tools","Transform files","Trigger repeatable actions","Inspect repository metadata","Compare code changes"],"suited_agents":["Codex","Claude Code","Cursor","OpenAgentSkill CLI","CLI"],"install":{"command":"npx skills add K-Dense-AI/scientific-agent-skills --skill arboreto","ready":true,"targets":[{"id":"openagentskill-cli","label":"CLI","kind":"command","value":"npx --yes https://github.com/Leon-Drq/openagentskill/releases/download/cli-v0.2.1/openagentskill-0.2.1.tgz install k-dense-ai-arboreto"},{"id":"codex","label":"Codex","kind":"agent-prompt","value":"Install the \"arboreto\" agent skill from https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/arboreto. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"k-dense-ai-arboreto\",\"task\":\"Install arboreto\",\"agent\":\"codex\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes."},{"id":"claude-code","label":"Claude Code","kind":"agent-prompt","value":"Add \"arboreto\" as a Claude Code skill from https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/arboreto. Inspect the skill instructions, place the reusable skill files in the appropriate local skills location for this project, and report the activation steps. Skill purpose: Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"k-dense-ai-arboreto\",\"task\":\"Install arboreto\",\"agent\":\"claude-code\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes."},{"id":"cursor","label":"Cursor","kind":"agent-prompt","value":"Turn \"arboreto\" from https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/arboreto into a reusable Cursor project rule or agent instruction. Preserve the core workflow, adapt paths to this repo, and keep the rule scoped to tasks where it is relevant. Skill purpose: Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"k-dense-ai-arboreto\",\"task\":\"Install arboreto\",\"agent\":\"cursor\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes."}],"handoff_url":"https://www.openagentskill.com/api/skills/k-dense-ai-arboreto/install","manifest_url":"https://www.openagentskill.com/api/registry/manifest/k-dense-ai-arboreto"},"trust":{"score":83,"label":"Strong shortlist","version":"trust-score-v4","install_policy":"human_review_before_install","evidence":{"stars":"34K GitHub stars","repoActivity":"34K stars, 3.3K forks","lastPushed":"2d since push","license":"BSD-3-Clause license","repository":"https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/arboreto","install":"npx skills add K-Dense-AI/scientific-agent-skills --skill arboreto","installSafety":"standard package or runtime install path","permissionSurface":"shell or command execution, filesystem or document access","documentation":"Strong README/SKILL.md context","agentOutcomes":"No agent outcome data yet"},"outcome_evidence":{"total":0,"successes":0,"failures":0,"not_relevant":0,"success_rate":null,"recent_success_rate":null,"recent_failure_rate":null,"install_attempts":0,"install_success_rate":null,"risk_blocked":0,"setup_required":0,"avg_output_quality":null,"production_outcomes":0,"last_outcome_at":null,"label":"No agent outcome data yet"},"auto_install":{"allowed":false,"sandbox_required":true,"reason":"Human review or sandbox validation is required before automatic installation."},"best_for":["data-analysis","agent-skill"],"known_risks":["Quality score needs review","Permission surface needs review: shell or command execution, filesystem or document access","Permission surface: shell or command execution, filesystem or document access"]},"agent_proven":{"version":"agent-proven-v1","score":0,"tier":"unproven","label":"Needs first agent run","summary":"No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.","metrics":{"totalOutcomes":0,"successfulOutcomes":0,"failedOutcomes":0,"installAttempts":0,"installSuccessRate":null,"successRate":null,"recentSuccessRate":null,"recentFailureRate":null,"riskBlocked":0,"setupRequired":0,"notRelevant":0,"avgOutputQuality":null,"avgTimeToUsefulMs":null,"productionOutcomes":0,"humanReviewRequired":0,"uniqueAgents":0,"lastOutcomeAt":null},"signals":[],"penalties":["No real agent outcome evidence yet"]},"audit":{"score":88,"risk_level":"needs_review","risk_label":"Needs review","warnings":["Permission surface may require sandboxing","Quality score needs review","Permission surface needs review: shell or command execution, filesystem or document access","Permission surface: shell or command execution, filesystem or document access"]},"safety_gate":{"tier":"experimental","label":"Experimental","auto_install_policy":"review","auto_install_allowed":false,"human_review_required":true,"blocked":false,"recommended_action":"Test manually in an isolated workspace and compare against safer alternatives."},"quality":{"score":92,"label":"Excellent"},"supply":{"track":"Coding and developer agents","scenario":"GitHub automation","maintenance":"2d since push","risk":"Needs review"},"alternative_skills":[],"do_not_use_when":["teams that need a vendor-supported SLA","high-compliance environments without internal security review","No major risk signals from current metadata","High-risk permission hints: Shell or command execution","Permission surface may require sandboxing","Quality score needs review","Permission surface needs review: shell or command execution, filesystem or document access","Permission surface: shell or command execution, filesystem or document access"],"agent_contract":{"task_input":"Use arboreto in an agent workflow","recommended_action":"Test manually in an isolated workspace and compare against safer alternatives.","install_policy":"review","minimum_review_before_use":["Trust: 83/100 Strong shortlist","Audit: 88/100 Needs review","Safety: 56/100 Review before install","Review repository, license, install command, and permission surface before production use."],"expected_agent_output":{"selected_skill":"k-dense-ai-arboreto (arboreto)","install_command":"npx skills add K-Dense-AI/scientific-agent-skills --skill arboreto","risk_summary":"Needs review; Experimental; Review before production","verification_result":"Report the smallest successful task, files touched, warnings, and any missing setup."}},"outcome_feedback":{"endpoint":"https://www.openagentskill.com/api/agent/outcome","method":"POST","requires_resolve_event_id":true,"event_id_source":"Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.","expected_outcomes":["success","failed","not_relevant","blocked_by_risk","setup_required"],"payload_template":{"event_id":"<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>","skill_slug":"k-dense-ai-arboreto","task":"Use arboreto in an agent workflow","agent":"codex","outcome":"success","install_used":true,"risk_blocked":false,"setup_required":false,"task_success":true,"output_quality":4,"error_type":null,"human_review_required":false,"workspace":"sandbox","time_to_useful_ms":120000,"notes":"Report the smallest successful task, setup friction, files touched, and risk notes."}},"endpoints":{"web":"https://www.openagentskill.com/skills/k-dense-ai-arboreto","api":"https://www.openagentskill.com/api/agent/skills/k-dense-ai-arboreto","audit":"https://www.openagentskill.com/skills/k-dense-ai-arboreto/audit","eval":"https://www.openagentskill.com/api/agent/evals?slug=k-dense-ai-arboreto&task=Use%20arboreto%20in%20an%20agent%20workflow&max_risk=medium","resolve":"https://www.openagentskill.com/api/agent/resolve?task=Use%20arboreto%20in%20an%20agent%20workflow&agent=codex&max_risk=medium","receipt":"https://www.openagentskill.com/api/agent/receipt?task=Use%20arboreto%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text","install":"https://www.openagentskill.com/api/skills/k-dense-ai-arboreto/install","manifest":"https://www.openagentskill.com/api/registry/manifest/k-dense-ai-arboreto"}},"supply_profile":{"track":{"slug":"coding","label":"Coding and developer agents","shortLabel":"Coding","description":"Code review, repo analysis, testing, CI, GitHub, DevOps, and developer workflow skills."},"scenario":{"label":"GitHub automation","description":"I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.","useCases":[{"slug":"workflow-automation","title":"Workflow automation"},{"slug":"github-automation","title":"GitHub automation"},{"slug":"local-desktop","title":"Local desktop"}]},"applicableAgents":["Claude Code","CLI","Codex","Cursor"],"install":{"ready":true,"command":"npx skills add K-Dense-AI/scientific-agent-skills --skill arboreto","primaryTarget":"CLI","targetCount":4},"githubQuality":{"stars":33974,"starsLabel":"34K","forks":3307,"license":"BSD-3-Clause license","qualityScore":92,"trustScore":83,"auditScore":88},"maintenance":{"status":"fresh","label":"2d since push","daysSincePush":2,"lastPushedAt":"2026-08-20T13:03:17+00:00"},"risk":{"level":"needs_review","label":"Needs review","requiresReview":true,"notes":["Permission surface may require sandboxing","Quality score needs review","Permission surface needs review: shell or command execution, filesystem or document access","Permission surface: shell or command execution, filesystem or document access","Needs review"]},"coverageTags":["Coding","GitHub automation","data-analysis","agent-skill"]},"audit":{"audit_score":88,"risk_level":"needs_review","risk_label":"Needs review","quality_score":92,"trust_score":83,"maintenance_score":100,"security_score":81,"install_score":92,"warnings":["Permission surface may require sandboxing","Quality score needs review","Permission surface needs review: shell or command execution, filesystem or document access","Permission surface: shell or command execution, filesystem or document access"]},"quality_signals":{"model":"v2","star_score":31.72,"usage_score":0,"review_score":5.25,"metadata_score":3,"freshness_score":15},"platforms":["Claude Code"],"use_cases":[{"slug":"workflow-automation","title":"Workflow automation","url":"https://www.openagentskill.com/use-cases/workflow-automation"},{"slug":"github-automation","title":"GitHub automation","url":"https://www.openagentskill.com/use-cases/github-automation"},{"slug":"local-desktop","title":"Local desktop","url":"https://www.openagentskill.com/use-cases/local-desktop"},{"slug":"browser-automation","title":"Browser automation","url":"https://www.openagentskill.com/use-cases/browser-automation"}],"stacks":[{"slug":"content-growth-agent","title":"Content growth agent","url":"https://www.openagentskill.com/collections/content-growth-agent"},{"slug":"browser-qa-agent","title":"Browser QA agent","url":"https://www.openagentskill.com/collections/browser-qa-agent"},{"slug":"coding-review-agent","title":"Coding review agent","url":"https://www.openagentskill.com/collections/coding-review-agent"}],"install":"npx skills add K-Dense-AI/scientific-agent-skills --skill arboreto","install_targets":[{"id":"openagentskill-cli","label":"CLI","title":"OpenAgentSkill CLI","kind":"command","value":"npx --yes https://github.com/Leon-Drq/openagentskill/releases/download/cli-v0.2.1/openagentskill-0.2.1.tgz install k-dense-ai-arboreto","description":"Resolve policy, run the source installer safely, and report a verified install receipt.","copyLabel":"Copy command"},{"id":"codex","label":"Codex","title":"Codex install prompt","kind":"agent-prompt","value":"Install the \"arboreto\" agent skill from https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/arboreto. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"k-dense-ai-arboreto\",\"task\":\"Install arboreto\",\"agent\":\"codex\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.","description":"Give Codex a repo-aware install prompt when the skill is not available through a local CLI.","copyLabel":"Copy prompt"},{"id":"claude-code","label":"Claude Code","title":"Claude Code skill prompt","kind":"agent-prompt","value":"Add \"arboreto\" as a Claude Code skill from https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/arboreto. Inspect the skill instructions, place the reusable skill files in the appropriate local skills location for this project, and report the activation steps. Skill purpose: Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"k-dense-ai-arboreto\",\"task\":\"Install arboreto\",\"agent\":\"claude-code\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.","description":"Use this prompt to ask Claude Code to add the skill and explain the local activation steps.","copyLabel":"Copy prompt"},{"id":"cursor","label":"Cursor","title":"Cursor rule prompt","kind":"agent-prompt","value":"Turn \"arboreto\" from https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/arboreto into a reusable Cursor project rule or agent instruction. Preserve the core workflow, adapt paths to this repo, and keep the rule scoped to tasks where it is relevant. Skill purpose: Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"k-dense-ai-arboreto\",\"task\":\"Install arboreto\",\"agent\":\"cursor\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.","description":"Use this when installing as Cursor project rules or reusable agent instructions.","copyLabel":"Copy prompt"}],"repository":"https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/arboreto","github_repo":"K-Dense-AI/scientific-agent-skills","version":"1.0.0","license":"BSD-3-Clause license","urls":{"web":"https://www.openagentskill.com/skills/k-dense-ai-arboreto","repository":"https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/arboreto","api":"/api/agent/skills/k-dense-ai-arboreto","install_api":"/api/skills/k-dense-ai-arboreto/install"},"meta":{"created_at":"2026-08-20T13:23:14.06357+00:00","updated_at":"2026-08-20T13:23:14.06357+00:00","agent_friendly":true}}