Use agent-budget

Write the spend spec for an agentic workflow

Write the spend spec for an agentic workflow — per-stage model tiers, token caps with hard abort rules, a graceful-degradation order, and cost-per-outcome as the governing metric.

Reach for it when the user asks what an agent loop or multi-agent workflow should be allowed to spend, wants token/cost budgets and caps for AI automation, or got a surprise bill from an agent fleet.

Install it

npx skilldrop-cli install agent-budget                 # Claude Code, your user folder
npx skilldrop-cli install agent-budget --project       # Claude Code, this repo
npx skilldrop-cli install agent-budget --ide cursor    # or kiro, codex, antigravity, copilot

It is part of the AI engineering pack: --pack ai-engineering installs it with its neighbours.

Ask for it

Say it the way you would to a colleague. Requests like these route to it:

• "What should our agent workflow be allowed to spend in tokens?"
• "We got a surprise LLM bill from our automation — put budgets and caps on it"
• "Is this agent pipeline actually cheaper than doing the work manually?"
• "Add a budget line to this loop spec"

Requests like these belong to a different skill, so it shouldn't answer them:

• "Model our service's cloud infrastructure costs at 10x scale"
• "Which model tier should the adr-generator skill run on?"
• "Design the loop with exit criteria for this automation"

If another skill answers instead, ask for this one by name: "use agent-budget".

Check what you get

A good result:

• The outcome unit is named and every cost in the doc divides by it.
• Every stage has tier + rationale + three numbers (expected, cap, on-cap action). No stage rides free.
• Heavy tiers survive. No verification or judgment stage was downgraded to make the total look better.
• Caps are hard. Every on-cap action is abort or degrade — "warn and continue" appears nowhere.
• The run cap is less than the sum of stage caps, and the doc says why.
• Verification is last on the degradation ladder, explicitly.
• The comparison line exists — cost-per-outcome sits next to what the outcome costs without the agents.
• Estimates are tagged. [assumption] until measured; the calibration step is scheduled, not implied.

Use something else when

• Infra/cloud cost modeling (compute, storage, egress) — capacity-cost-model
• Choosing a model tier for a single skilldrop skill — that's model-routing.json's table, already decided
• Designing the loop or the fan-out itself — agent-loop-design / subagent-design; this skill prices what they draw
• Negotiating provider contracts or rate limits — procurement, not a spec

Next

• SKILL.md, the instructions the agent reads
• Its page on the site
• Related: agent-loop-design, capacity-cost-model, llm-eval-harness, subagent-design

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