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 agentmods add skills/sananthanarayan/skilldrop/agent-budgetnpx skills add sananthanarayan/skilldrop --skill agent-budgetgit clone --depth 1 https://github.com/sananthanarayan/skilldropWrote 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/sananthanarayan/skilldrop/agent-budget)<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>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 | $0.00081 | $0.01750 |
| Opus 5 | $0.00041 | $0.00875 |
| Sonnet 5 | $0.00016 | $0.00350 |
| Haiku 4.5 | $0.00008 | $0.00175 |
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.
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
-
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 → emitBLOCKED: need the workflow and its outcome unit. -
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.
-
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.
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.
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.
- 5d ago First seen · 62 lines · 81 tokens per session scan A d2bb90c4f5a0
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.
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.
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.
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.
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.
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.
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…