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Recommends the optimal Claude model based on task requirements and cost analysis
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Connect the user's Akashi second brain (local, private, verifiable) over MCP and answer from cited vault passages with signed provenance. Query it BEFORE answering anything about the user's knowledge, projects, decisions, or conventions. Triggers - akashi, vault, my notes, second brain, remember this, what did I decide.
Use CodexBar CLI local cost usage to summarize per-model usage for Codex or Claude, including the current (most recent) model or a full model breakdown. Trigger when asked for model-level usage/cost data from codexbar, or when you need a scriptable per-model summary from codexbar cost JSON.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Monitor API health and assert endpoint status with ZK proofs
Semantic code review with provable assertions about code quality
Audit project dependencies with verifiable security assertions
| name | model-advisor |
| description | Recommends the optimal Claude model based on task requirements and cost analysis |
| type | semantic |
| user-invocable | true |
| semantic | {"skillVersion":1,"permissions":{"graph":["read"],"memory":["recall"]},"queries":[{"predicate":"advisor:recommendation","scope":"agent"}]} |
Recommends the optimal Claude model based on task requirements, complexity analysis, and cost efficiency.
Use when choosing between Claude models for a specific task, when optimizing API costs, or when you need guidance on which model best fits a workload profile.
| Model | Input | Output | Best For |
|---|---|---|---|
| Opus 4.6 | $15/MTok | $75/MTok | Complex reasoning, research, multi-step analysis, code architecture, creative writing |
| Sonnet 4.6 | $3/MTok | $15/MTok | Coding, general tasks, data analysis, moderate complexity |
| Haiku 4.5 | $0.80/MTok | $4/MTok | Classification, extraction, simple Q&A, high volume, low latency |
skill_memory_context