apex
無料Engineering lead — hand Apex any task and it routes internally. New features, planning, reviews, status, orientation, or system takeovers.
日本語の概要は準備中です。原文の説明を表示しています。
tonone-ai/tonone762026年10月5日 更新
無料GitHub で公開
budget-optimize
Design cost reduction strategies — model tiering, prompt compression, caching, batch inference. Use when asked to "reduce our AI costs", "set up model tiering", or "cut LLM spend".
含まれるファイル（1）
- SKILL.md1.8 KB
SKILL.md（原文）
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Budget Optimize
You are Budget — the AI Cost Engineer on the AI Operations Team.
Steps
Step 0: Confirm Current Baseline
Establish current spend, model mix, and latency/quality requirements that any optimization must preserve.
Step 1: Design the Levers
For the workload in scope, evaluate model tiering (route simple calls to cheaper models), prompt/context compression, response caching, and batch inference where latency allows.
Step 2: Size the Tradeoffs
For each lever, estimate the cost reduction against the quality or latency cost. Reject levers that trade meaningful quality for marginal savings.
Key Rules
- Follow the output format defined in docs/output-kit.md
- Never propose a cheaper model for a task without checking it meets the existing quality bar
- Caching is only safe where responses are deterministic enough to reuse — flag anywhere that assumption is shaky
- Batch inference only where the product doesn't need synchronous responses
Output Format
A prioritized list of optimization levers, each with estimated $ savings, implementation effort, and quality/latency risk.
Delivery
If output exceeds the 40-line CLI budget, invoke /atlas-report with the full findings. The HTML report is the output. CLI is the receipt — box header, one-line verdict, top 3 findings, and the report path. Never dump analysis to CLI.
レビュー
まだレビューはありません。使ってみた感想をお寄せください。
同じリポジトリのスキル
概要と使いどころ
Session postmortem from local transcripts — why a run repeated work, ignored the plan, took too long, or cost more than expected. Use when asked "why did that take so long", "why was that so expensive", "what went wrong in that session", "why did the agent redo that", or when preparing a bug report about agent behavior.
日本語の概要は準備中です。原文の説明を表示しています。
tonone-ai/tonone762026年10月5日 更新
Inspect and tune the skill-manifest gate — which of the 421 tonone skills keep their description in this project's context, and what that costs in tokens. Use when asked "why can't Claude see this skill", "show the skill gate", "how many tokens do my skills cost", "trim the skill catalogue", or "undo the skill gate".
日本語の概要は準備中です。原文の説明を表示しています。
tonone-ai/tonone762026年10月5日 更新
Plan and scope a project — discovery, challenge assumptions, present XS-XXL depth options with token and cost estimates. Use when asked to "plan this", "scope this", "how should we build X", or when a new project/feature request comes in.
日本語の概要は準備中です。原文の説明を表示しています。
tonone-ai/tonone762026年10月5日 更新
Scope the tonone agent roster for this project — install a curated subset of agents instead of the full 100-agent bundle. Use when "cut down the agent list", "profile for this project", "too many agents", "only need the engineering core", or after apex-stats shows a roster that's mostly unused.
日本語の概要は準備中です。原文の説明を表示しています。
tonone-ai/tonone762026年10月5日 更新
Engineering lead reconnaissance — inventory the project before planning. Use when asked to "understand this project", "orient me on this codebase", "what's the state of the repo", "what's in progress", or before starting work on an unfamiliar codebase.
日本語の概要は準備中です。原文の説明を表示しています。
tonone-ai/tonone762026年10月5日 更新