agent-budget

agent-budget is a skill for Claude Code from sananthanarayan/skilldrop. It costs 81 tokens per session (1,750 once invoked), scanned A, original, MIT.

A budgeting method for an AI workflow in which several stages or agents may use models and tokens. It defines spending limits, what happens when a limit is reached, and the cost of one completed result.

In plain words
What is it for?
Use it to set model tiers, token caps, hard stop rules, fallback behaviour, and a cost-per-result target for an agent workflow.
Why use it?
It prevents an agent loop from consuming more model usage than intended. It also makes the workflow’s cost measurable by tying spending to a defined outcome.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: mentions subagents; positional $N argument.

Part of the skilldrop plugin — 51 skills, 4 agents shipped together

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/sananthanarayan/skilldrop/agent-budget
Any agent
npx skills add sananthanarayan/skilldrop --skill agent-budget
Clone the repo
git clone --depth 1 https://github.com/sananthanarayan/skilldrop

Made for: Claude Code.

Or install skilldrop, the plugin that ships this one along with the rest of its 51 skills, 4 agents.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for agent-budget

README.md
[![agentmods](https://agentmods.dev/badge/skills/sananthanarayan/skilldrop/agent-budget.svg)](https://agentmods.dev/skills/sananthanarayan/skilldrop/agent-budget)
Your own site
<a href="https://agentmods.dev/skills/sananthanarayan/skilldrop/agent-budget"><img src="https://agentmods.dev/badge/skills/sananthanarayan/skilldrop/agent-budget.svg" alt="Measured on agentmods" height="20"></a>
Per session 81 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,750 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00081 $0.01750
Opus 5 $0.00041 $0.00875
Sonnet 5 $0.00016 $0.00350
Haiku 4.5 $0.00008 $0.00175

Measured 5d ago against content hash d2bb90c4f5a0, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

agent-budget scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/agent-budget/SKILL.md · 62 lines

How it starts

The opening of the file, as written. The whole thing — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.

agent-budget

An unbudgeted agent loop is a runaway-cost incident with an architecture diagram. This skill produces the budget spec that governs an agentic workflow: what each stage may spend, on which tier of model, what happens at the cap, and — the number that actually matters — what one outcome costs. Pairs with agent-loop-design (the caps land in its loop spec) and subagent-design (the fleet line lands in its role cards); the tier vocabulary is this repo's own light/standard/heavy routing abstraction, so the spec ports across providers. Infra spend (compute, storage, egress) is capacity-cost-model's domain — this skill budgets the tokens.

How to respond

  1. Pin the outcome unit and the workflow's stages. Ask at most 2 questions, spent on: "what is one successful outcome?" (a merged PR, a triaged ticket, a verified report — the denominator every cost divides by) and "what does a typical run look like today?" (stages, rounds, models — or "not built yet", which makes this a design-time budget, the cheap time to write one). Non-interactive run (no user to ask): derive both from the input and tag [assumption]; no outcome unit derivable → emit BLOCKED: need the workflow and its outcome unit.

  2. Assign each stage the cheapest adequate tier — light / standard / heavy, per the routing rule of thumb: light for mechanical extraction and formatting, standard for most generation, heavy only where the hard thinking is the value (adversarial verification, weighted judgment) — and heavy stages are never downgraded to save money; they're where the money buys correctness. Every tier assignment carries a one-line rationale. The classic misallocation runs both directions: frontier models formatting JSON, and — worse — the cheap model doing the verify pass that exists to catch the cheap mistakes.

  3. Set three numbers per stage in templates/budget-spec.md: expected spend per run (estimate honestly, tag [assumption] until measured), cap (the hard stop — 3–5× expected, tighter for unattended loops), and on-cap action (abort-and-escalate, or degrade — never "continue and warn"; a warning nobody is watching is a continue). State each as tokens and approximate money — tokens are what the harness enforces, but money is the only unit that sums across tiers, so all cap comparisons happen in currency. A stage that fans out per item carries two caps: a per-item cap (that item fails to the report's needs-human list; the rest continue) and a stage-wide cap. Then the run-level cap for the whole workflow — in currency, less than the sum of stage caps: every stage simultaneously hitting its cap is not a run to finish, it's an anomaly to stop.

Read the full file on GitHub · 62 lines

Files

What ships with it

4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 5d ago First seen · 62 lines · 81 tokens per session scan A d2bb90c4f5a0

Subscribe to this mod's changes

agent-budget is a skill published in the GitHub repository sananthanarayan/skilldrop (2 stars, last pushed 22d ago), licensed MIT. It adds 81 tokens to every session and 1,750 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Related

Other skills, from other repositories

openlore-brainstorm

Transform a feature idea into an annotated story using a Domain Sketch or Constrained Option Tree. Use when asked to brainstorm, explore, or shape a feature before implementation.

clay-good/OpenLore · 40 tokens

openlore-execute-refactor

Apply a confirmed .openlore/refactor-plan.md with a test gate after each change. Use when asked to execute or continue an OpenLore refactoring plan.

clay-good/OpenLore · 41 tokens

openlore-plan-refactor

Identify a high-priority refactoring target, assess its blast radius, and write .openlore/refactor-plan.md without changing code. Use when asked to plan or prioritize a refactor.

clay-good/OpenLore · 46 tokens

openlore-debug

Debug with OpenLore structural context, an explicit root-cause hypothesis, and RED/GREEN verification. Use when a bug, failure, or regression needs diagnosis and repair.

clay-good/OpenLore · 39 tokens

openlore-analyze-codebase

Run a full static OpenLore analysis and summarize architecture, call graph, refactoring issues, and duplicate code. Use when asked to analyze, map, or assess a codebase without LLM inference.

clay-good/OpenLore · 48 tokens

mission-brief

Mission-driven SDD orchestrator: take a feature description, structure it into a Mission Brief (goal, constraints, success criteria), generate an ordered step list with prompts that trigger installed SDD skills via model invocation or command-file discovery, and walk those steps to converged implementation. Use when…

tikalk/adlc-team-skills · 99 tokens