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.
npx skills add MLOps-Courses/agentops-open-course --skill agent-token-budgetgit clone --depth 1 https://github.com/MLOps-Courses/agentops-open-courseWrote 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.
[](https://agentmods.dev/skills/mlops-courses/agentops-open-course/agent-token-budget)<a href="https://agentmods.dev/skills/mlops-courses/agentops-open-course/agent-token-budget"><img src="https://agentmods.dev/badge/skills/mlops-courses/agentops-open-course/agent-token-budget/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/mlops-courses/agentops-open-course/agent-token-budget"><img src="https://agentmods.dev/badge/skills/mlops-courses/agentops-open-course/agent-token-budget.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00065 | $0.00554 |
| Opus 5.5 | $0.00026 | $0.00222 |
| Sonnet 5.5 | $0.00013 | $0.00111 |
| Haiku 4.5 | $0.00006 | $0.00055 |
Grade A, and why
agent-token-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 29d 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.
How it starts
The opening of the file, as written. The whole thing — 37 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Token Budget
Every agent loop step is another model call, so cost and latency compound. Give each conversation a hard token ceiling and attribute usage to the session — bounding reasoning work, not just dollars.
When to use
- An agentic loop or delegation chain can call the model an unbounded number of times.
- You need to answer "how many tokens did this session use?" and "what happens at the limit?".
- You want token/cost signals in traces and metrics without hardcoding a vendor's prices.
Steps
- Accumulate usage into session state. After each model response, add its input/output tokens to a running per-session total that persists across turns (no
temp:prefix if your session store honors one). - Enforce a hard ceiling before the model call. In a before-model hook, if the session total has reached the limit, short-circuit with a clear message ("start a new session, or raise the limit") instead of a silent failure or an open-ended bill.
- Attribute, don't just count. Emit tokens as an OTel counter (graph throughput) and as span attributes (per-turn detail); compute cost from configurable per-1k prices that default to 0 for local models — never hardcode a provider's pricing.
- Make the budget cover the whole conversation. In multi-agent/delegation flows, keep the totals in shared session state so hops between sub-agents accumulate against one ceiling rather than each starting fresh.
Reference implementation
From the AgentOps Open Course (agent modules live under agents/python/src/agent/):
budget.py—record_token_usage,enforce_token_budget,estimate_cost; OTel counter + span attributes.- Course chapters
7.3. Costsand3.7. Multi-Agent.
Verify
Set the ceiling to 1 token and send a turn: assert the model call is refused with the actionable message and the token counter reflects the usage; confirm a delegation chain hits one shared ceiling, not one per sub-agent.
Honest limits
A per-session budget bounds one conversation, not one client — a caller who rotates sessions bypasses it, so pair it with a per-client/tenant limit at the gateway. It bounds tokens, not dollars, unless you supply real prices.
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.
- 29d ago Changed 33485821b16d
- 1mo ago First seen · 37 lines · 65 tokens per session scan A 16ae49bf3c81
agent-token-budget is a skill published in the GitHub repository MLOps-Courses/agentops-open-course (2 stars, last pushed yesterday), licensed MIT. It adds 65 tokens to every session and 554 once invoked, about $0.0003 per session on Opus 5.5. 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.
Other skills, from other repositories
skill-release-gate
Evaluate an Agent Skill bundle for structural integrity, trigger quality, artifact improvement, script correctness, safety, installed-tree integrity, and target-host portability before release.
migration-review
Review database migration files when a change adds or modifies paths under migrations/. Use it before merge to collect forward, rollback, locking, and data-safety evidence.
orchard-release
Check deployment evidence and recovery conditions.
mirrord-prev-env
Help users create and manage mirrord preview environments — running a modified service as an isolated pod in a shared Kubernetes cluster, scoped by an environment key and HTTP/queue traffic filtering, so teams can validate and review changes against real traffic without affecting live services. Use when a developer…
mirrord-operator
Help users install and configure the mirrord Operator for team/enterprise environments. Use when users ask about operator setup, Helm installation, cloud API key or license configuration, air-gapped/offline licensing, enabling features (queue splitting, DB branching, preview environments, multi-cluster), internal…
orchard-review
Review a release proposal.