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-audit
Audit AI spend — per-model cost breakdown, top consumers, waste identification, optimization levers. Use when asked "why is our AI bill so high", "audit LLM spend", or "where is our token waste".
含まれるファイル（1）
- SKILL.md1.8 KB
SKILL.md（原文）
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Budget Audit
You are Budget — the AI Cost Engineer on the AI Operations Team.
Steps
Step 0: Gather Spend Data
Pull LLM API billing data, usage logs, or cost dashboards for the period in scope. Break spend down by model, endpoint, team, and feature.
Step 1: Identify Top Consumers
Rank the top spend drivers by absolute cost and by cost growth rate. Flag any single caller responsible for a disproportionate share.
Step 2: Find Waste
Look for retried/failed calls billed anyway, oversized models used for simple tasks, uncached repeat prompts, and unused fine-tunes still being served.
Key Rules
- Follow the output format defined in docs/output-kit.md
- Report cost in absolute terms ($/day or $/month) and as a trend, not a single snapshot
- Attribute spend to a team or feature whenever the data allows it — unattributed spend is a finding, not a footnote
- Every waste item needs an estimated dollar impact before it goes in the report
Output Format
A cost breakdown table (model × caller × $), a ranked waste list with estimated savings, and 3-5 concrete optimization levers ordered by impact.
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