Skill comparison
Use this as a shortlist, then open the skill detail page before adopting.
Decision summary
Strongest overall
Superset
Use this as a leading candidate, then validate the README and install path in your own agent stack.
Fastest prototype
Superset
Best first install candidate based on install readiness and adoption.
Freshest repo
Superset
Most recent maintenance signal among this shortlist.
| Signal | journal This skill should be used when the user wants to record bookkeeping entries (仕訳), import transaction data from CSV files, receipts, or invoices, or manage their general ledger. Trigger phrases include: "仕訳を入力", "仕訳登録", "CSVを取り込む", "レシートを読み込む", "請求書を取り込む", "帳簿を付ける", "経費を記録", "売上を記録", "仕訳を修正", "仕訳を検索", "仕訳を削除", "取引を登録", "帳簿の初期化". | Airflow Apache Airflow - A platform to programmatically author, schedule, and monitor workflows | Scientific Agent Skills Turn any AI agent into an AI Scientist. The #1 Agent Skills library for science, used by 160,000+ scientists worldwide. 140 ready-to-use skills plus 100+ scientific databases covering biology, chemistry, medicine, and drug discovery. Compatible with Cursor, Claude Code, Codex, Pi, Antigravity, and the open Agent Skills standard. | Superset Apache Superset is a Data Visualization and Data Exploration Platform |
|---|---|---|---|---|
| Quality | 67/100 Promising | 100/100 Excellent | 100/100 Excellent | 100/100 Excellent |
| Decision verdict | 66/100 Prototype first Prototype with this skill first; keep a fallback candidate ready. | 100/100 Production-ready Use this as a leading candidate, then validate the README and install path in your own agent stack. | 100/100 Production-ready Use this as a leading candidate, then validate the README and install path in your own agent stack. | 100/100 Production-ready Use this as a leading candidate, then validate the README and install path in your own agent stack. |
| Adoption | 359 stars Verified outcomes are shown on each skill page | 47K stars Verified outcomes are shown on each skill page | 33K stars Verified outcomes are shown on each skill page |
| 75K stars Verified outcomes are shown on each skill page |
| Freshness | Sep 9, 2026 | Aug 25, 2026 | Aug 13, 2026 | Sep 10, 2026 |
| Use-case fit |
| Workflow fit |
| Platform hints | Claude Code | Python, ETL, Claude Code | Python, Data Analysis, Claude Code, OpenAI Agents, Cursor | TypeScript, BI, Claude Code |
| Warnings | No OpenAgentSkill engagement data yet | No OpenAgentSkill engagement data yet | No OpenAgentSkill engagement data yet | No OpenAgentSkill engagement data yet |
| Best for | Research agents workflows · Claude Code teams · builders willing to evaluate younger projects | Workflow automation workflows · Claude Code teams · teams that value GitHub adoption signals | Data analysis workflows · Claude Code teams · teams that value GitHub adoption signals | Data analysis workflows · Claude Code teams · teams that value GitHub adoption signals |
| Not ideal for | teams that need a vendor-supported SLA · high-compliance environments without internal security review | teams that need a vendor-supported SLA · high-compliance environments without internal security review | teams that need a vendor-supported SLA · high-compliance environments without internal security review | teams that need a vendor-supported SLA · high-compliance environments without internal security review |
| OpenAgentSkill engagement | 0 views 0 install copies | 0 views 0 install copies | 0 views 0 install copies | 0 views 0 install copies |
| Install | $ npx skills add kazukinagata/shinkoku --skill journal | $ npx skills add apache/airflow | $ npx skills add K-Dense-AI/scientific-agent-skills | $ npx skills add apache/superset |