llm-cost-latency-budget
Model the cost and latency of an LLM feature before it ships and surprises the bill. Use when asked to estimate LLM API costs, set a latency/token budget, decide which model tier to use, or bring down the cost of an AI feature. Produces a cost & latency budget — token math per request, monthly cost projection, model tiering, caching/streaming levers, p95 latency targets, and a guardrail/alert plan.
npx skills add mohitagw15856/pm-claude-skills --skill llm-cost-latency-budget --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# LLM Cost & Latency Budget Skill LLM features have a unit cost and a tail latency that demos hide and production exposes. This skill does the token math up front — what one request costs, what a million cost, where the p95 latency comes from — and lays out the levers (model tiering, caching, prompt trimming) so cost and speed are designed, not discovered. ## Required Inputs Ask for these only if they aren't already provided: - **The request shape** — typical system prompt, user input, retrieved context, and output sizes (in rough tokens). - **Volume** — requests/day now and at target scale; peak concurrency. - **Models in play** — candidate model(s) and their per-token input/output prices. - **Targets** — acceptable cost per request (or per user/month) and the latency users will tolerate (p50 / p95). ## Output Format ### Cost & Latency Budget: [feature] **1. Per-request token math** — a table estimating tokens in/out per call, and the resulting cost at each candidate model's price. | Component | Tokens | $ in | $ out | |---|---|---|---| | System prompt | | | | | Retrieved context | | | | | User input | | | | | Output | | | | | **Per request** | | **$x** | | **2. Monthly projection
- Required Inputs
- Output Format
- Cost & Latency Budget: [feature]
- Quality Checks
- Anti-Patterns
- Based On
What does the llm-cost-latency-budget skill do?
Model the cost and latency of an LLM feature before it ships and surprises the bill. Use when asked to estimate LLM API costs, set a latency/token budget, decide which model tier to use, or bring down the cost of an AI feature. Produces a cost & latency budget — token math per request, monthly cost projection, model tiering, caching/streaming levers, p95 latency targets, and a guardrail/alert plan.
How do I install it?
Run `npx skills add mohitagw15856/pm-claude-skills --skill llm-cost-latency-budget --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From mohitagw15856/pm-claude-skills, a repository with 1,255 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.