Laporan audit skill
agentsop-cost-tiered-models Laporan audit.
Split a multi-call LM workflow by cognitive load, not by accuracy: let one strong model make the few reasoning decisions and a cheap model do the many mechanical executions (Aider architect+editor, DSPy optimizer-LM vs task-LM, vLLM speculative draft+target, LangGraph supervisor+worker are the same shape). Use when designing or cost-optimizing a pipeline that calls an LM many times, when deciding which steps need a strong reasoner vs a cheap executor, or when adding an escalation valve for when the cheap tier degrades. Search keywords: reduce LLM cost, cheaper model, lower token cost, model cascade, route to cheap model, strong model plus cheap model, LLM cost optimization.
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
357 star GitHub
Aktivitas star/fork
Peringatan57
357 star dan 19 fork; aktivitas issue tidak tersedia dalam metadata saat ini
Pemeliharaan terbaru
Lulus88
1 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, network or browser surface
Ketersediaan pemasangan
Lulus92
npx skills add agentsope/SkillAlchemy --skill agentsop-cost-tiered-models
Keamanan perintah pemasangan
Lulus92
Jalur pemasangan paket atau runtime standar
Cakupan izin
Peringatan48
secrets or environment access, network or browser access
Bukti repositori
Lulus86
https://github.com/agentsope/SkillAlchemy/tree/master/skills/agentsop-cost-tiered-models
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 agentsope/SkillAlchemy --skill agentsop-cost-tiered-models
Repositori
88
https://github.com/agentsope/SkillAlchemy/tree/master/skills/agentsop-cost-tiered-models
Lisensi
86
MIT
Pemeliharaan
88
1 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, network or browser surface
Keamanan perintah pemasangan
92
Jalur pemasangan paket atau runtime standar
Cakupan izin
48
secrets or environment access, network or browser access
Aktivitas star/fork
57
357 star dan 19 fork; aktivitas issue tidak tersedia dalam metadata saat ini
Adopsi
68
357 star GitHub
Peringatan
- Permission surface may require sandboxing
- Quality score needs review
- Permission surface needs review: secrets or environment access, network or browser access
- Stars/forks activity: 357 stars, 19 forks; issue activity unavailable in current metadata
- Permission surface: secrets or environment access, network or browser access
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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