Laporan audit skill
memory-management Laporan audit.
Design and operate memory systems for long-running AI agents. Covers context window optimization, summarization strategies, vector-based retrieval, episodic memory, memory consolidation, and garbage collection for production agent systems.
Trust Score OpenAgentSkill
Trust Score OpenAgentSkill
The Trust Score helps an agent decide whether a skill is safe enough to shortlist before installation.
Adopsi GitHub
Info62
471 star GitHub
Aktivitas star/fork
Info62
471 star dan 62 fork; aktivitas issue tidak tersedia dalam metadata saat ini
Pemeliharaan terbaru
Lulus88
2 bulan sejak push
Kejelasan lisensi
Lulus86
MIT
Kelengkapan README/SKILL.md
Info76
Metadata publik memerlukan konteks README/SKILL.md yang lebih kuat
Risiko dependensi/runtime
Info64
credential or environment access, database surface
Ketersediaan pemasangan
Lulus92
npx skills add cosmicstack-labs/mercury-agent-skills --skill memory-management
Keamanan perintah pemasangan
Lulus92
Jalur pemasangan paket atau runtime standar
Cakupan izin
Info62
secrets or environment access, database access
Bukti repositori
Lulus86
https://github.com/cosmicstack-labs/mercury-agent-skills/tree/main/categories/ai-ml/memory-management
Status peninjauan
Lulus88
Data tinjauan AI tersedia
Hasil terbukti Agent
Info54
Belum ada data hasil Agent
Pemeriksaan
Tinjauan pemasangan dan adopsi
Jalur pemasangan
92
npx skills add cosmicstack-labs/mercury-agent-skills --skill memory-management
Repositori
88
https://github.com/cosmicstack-labs/mercury-agent-skills/tree/main/categories/ai-ml/memory-management
Lisensi
86
MIT
Pemeliharaan
88
2 bulan sejak push
Tinjauan AI
88
Approved with no listed issues
Kelengkapan README/SKILL.md
84
Usable description available
Risiko dependensi
64
credential or environment access, database surface
Keamanan perintah pemasangan
92
Jalur pemasangan paket atau runtime standar
Cakupan izin
62
secrets or environment access, database access
Aktivitas star/fork
62
471 star dan 62 fork; aktivitas issue tidak tersedia dalam metadata saat ini
Adopsi
68
471 star GitHub
Peringatan
- Quality score needs review
Metode
This report combines public metadata, AI review output, repository freshness, install readiness, OpenAgentSkill events, quality scoring, trust checks, and the agent safety gate. It is not a full source-code security review.
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