telerik/observability-skills · Archived

cost-report

Report and explain LLM spend and usage on the Progress Observability Platform.

First seen Aug 3, 2026

Installation

$ npx skills add telerik/observability-skills --skill cost-report

Summary

  • 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.

Stronger alternatives

This repository is archived — consider an actively maintained alternative.

Similar popular skills

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Also in this package

Other skills from telerik/observability-skills.

npx skills add telerik/observability-skills

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Repository health

Stars 1
License LICENSE
Default branch main
Open issues 1
Status Archived

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 2,284 B
  • docs SUMMARY.md 312 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 1 installs

SKILL.md

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 getcostbreakdown / getusagesummary are the only tools this skill needs.

<!-- copilot:start --> Turn the platform's cost and usage data into a short, decision-ready report.

Workflow

  1. Scope. Confirm the date range (getcostbreakdown 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.

  1. Pull cost. getcostbreakdown. 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.

  1. Pull usage/quota. getusagesummary for current billing-period usage and

remaining quota.

  1. 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. <!-- copilot:end -->