Agent Skills › Houseofmvps/ultraship › cost

cost

GitHub

追踪AI构建成本，按功能/模型分析支出，提供优化建议与预算预测。辅助用户记录Token消耗，识别高耗任务，推荐更经济的模型或架构调整策略，实现AI使用降本增效。

skills/cost/SKILL.md Houseofmvps/ultraship

Trigger Scenarios

用户希望了解AI使用花费需要分析各功能模块的AI成本寻求降低AI调用费用的建议规划AI使用预算

Install

npx skills add Houseofmvps/ultraship --skill cost -g -y

More Options

Use without installing

npx skills use Houseofmvps/ultraship@cost

指定 Agent (Claude Code)

npx skills add Houseofmvps/ultraship --skill cost -a claude-code -g -y

安装 repo 全部 skill

npx skills add Houseofmvps/ultraship --all -g -y

预览 repo 内 skill

npx skills add Houseofmvps/ultraship --list

SKILL.md

Frontmatter

{
    "name": "cost",
    "description": "AI Build Cost Tracker — track how much AI is costing you per feature. Use when user wants to track AI spending, understand cost per feature, optimize AI usage, or budget their Claude\/GPT costs.",
    "allowed-tools": "Bash, Read, Write, Grep"
}

AI Build Cost Tracker

Know exactly what each feature costs to build with AI. Budget smarter, spend less.

Process

Phase 1: Show Current State

node ${CLAUDE_PLUGIN_ROOT}/tools/cost-tracker.mjs <project-directory> show

Parse the JSON output for cost history and summary.

Phase 2: Cost Dashboard

Present spending overview:

Total Spend:

All-time total cost
This week / this month breakdown
Average cost per task

By Task/Feature:

Ranked list of features by cost (most expensive first)
Flag any outliers (tasks costing 3x+ the average)

By Model:

Cost breakdown by AI model used
Potential savings from model switching

Daily Trend:

Last 30 days of daily spending
Identify high-spend days

Phase 3: Cost Insights

Generate actionable insights:

Where money goes — what types of tasks cost the most (debugging? features? refactoring?)
Optimization opportunities — tasks that could use a cheaper model
Budget projection — at current rate, monthly spend estimate
Cost per line of code — rough estimate based on git diff

Phase 4: Logging New Costs

To log a new cost entry:

node ${CLAUDE_PLUGIN_ROOT}/tools/cost-tracker.mjs <project-directory> log "<label>" <input_tokens> <output_tokens> --model=claude-opus-4-6

Labels should be descriptive kebab-case: auth-feature, bug-fix-login, refactor-api, seo-optimization

Phase 5: Recommendations

Based on spending patterns:

If debugging > 30% of spend:

"Consider investing in more tests upfront — debugging is your biggest cost driver"
Suggest running /test-driven-development skill

If a single task > 3x average:

"The [task] cost $X — consider breaking large tasks into smaller chunks"

Model optimization:

"Routine refactoring with Sonnet instead of Opus would save ~$X/month"
"Code review tasks can use Haiku — potential savings of ~$X/month"

Logging Guide

Help the user understand when and how to log costs:

Log at the END of each task/feature (not during)
Use consistent labels across sessions
Include ALL tokens (input + output) from the conversation
Estimate if exact numbers aren't available (Claude Code shows token usage in the UI)

Phase 6: Build vs. Buy Framework

When reviewing cost data, help the user think about the economics of building with AI:

Worth building with AI (high ROI):

Features that would take 2+ days manually but 2 hours with AI
Boilerplate-heavy work (CRUD, migrations, test suites, API endpoints)
Exploration and prototyping (try 3 approaches, keep the best one)

Consider alternatives:

If a feature costs >$100 in AI tokens, check if a library or SaaS already does it
If debugging the same issue costs >$20 in AI tokens, the root cause is architectural — fix the architecture, not the symptom
If AI-generated code needs significant manual correction, the prompt needs work, not more tokens

Cost benchmarks for solo founders:

Task Type	Expected Cost	If Higher, Investigate
New API endpoint	$1-5	Complex business logic or unclear spec
Bug fix	$0.50-3	Missing tests or hard-to-reproduce issue
Full feature (frontend + backend)	$5-20	Large scope or frequent rework
Refactoring	$2-10	Unclear boundaries or missing tests
Content/copy generation	$0.25-1	Too many revision cycles

Key Principles

AI is an investment, not a cost. The goal isn't to minimize spend — it's to maximize ROI. A $50 feature that generates $5K MRR is a great investment. But a $50 bug fix that should have cost $5 with better tests is waste.
Track to learn, not to punish. Cost data reveals where your process is inefficient. High debugging costs mean insufficient tests. High rework costs mean unclear specs.
Optimize the expensive tasks, not the cheap ones. Switching from Opus to Haiku for a $0.10 task saves nothing. Reducing a $50 debugging session by improving test coverage saves real money.