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Cost report

Skill telerik/observability-skills/skills/cost-report

Report and explain LLM spend and usage on the Progress Observability Platform. Use when the user asks "what's driving my LLM costs", "how much am I spending", "cost by model/app", "am I close to my quota", "why did spend spike", or wants a cost/usage summary or a cheaper-model recommendation.From its SKILL.md

Install
npx -y skills add telerik/observability-skills --skill cost-report

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

2.2 KB, as published. Nobody here has run it

Cost & usage report

Read references/mcp.md first for the tool contract and limits. This skill uses only metadata tools — no trace content, so no injection surface. If that file isn't present (this skill was lifted out on its own), ask the user for the plugin's references/mcp.md, or fall back to the hard rules: every tool is read-only, and get_cost_breakdown / get_usage_summary are the only tools this skill needs.

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Turn the platform's cost and usage data into a short, decision-ready report.

Workflow

  1. Scope. Confirm the date range (get_cost_breakdown takes start_date / end_date) and what the user cares about — total spend, a suspected spike, model mix, or quota headroom. Default to the last 7 days if unspecified.

  2. Pull cost. get_cost_breakdown. Run it more than once when useful: group_by: "model" for model mix, "application" for which app spends, "day" for the trend. Use "all" for a first pass.

  3. Pull usage/quota. get_usage_summary for current billing-period usage and remaining quota.

  4. Report.

    • Headline — total spend for the range and where the billing period stands vs. quota.
    • Top drivers — the models/apps carrying most cost, with shares.
    • Trend — day-over-day movement; call out any spike and which model/app it came from.
    • Quota — projected burn vs. remaining (extrapolate linearly from period-to-date usage — state that assumption), and whether the current rate lands over quota before the period ends.
    • Recommendation (only if the data supports it) — e.g. a high-volume, low- stakes call path that could move to a cheaper model; frame it as a hypothesis to validate, not a certainty.

Keep it tight — a few bullets and one small table, not a wall of numbers.

Never write back to the platform — the server is read-only.

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Keep looking

Skills are one crate of 628,853. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.