Cost Benchmark

CommunityPopular

ruvnet

cost-benchmark

Run the corpus benchmark — booster locally, optional Gemini/Sonnet/Opus baselines — and persist a verifiable measured-vs-claimed table

Overview

Publisher	ruvnet
Repository	`ruflo`
Skill name	`cost-benchmark`
Stars	72.7K
Forks	8.6K
Bundled files	Instructions only
License	MIT
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by ruvnet on GitHub. Read the source before you install it.

Installation

Install the Cost Benchmark AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/ruvnet/ruflo/tree/main/plugins/ruflo-cost-tracker/skills/cost-benchmark

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/ruvnet/ruflo.git /tmp/ruflo
mkdir -p .claude/skills
cp -r /tmp/ruflo/plugins/ruflo-cost-tracker/skills/cost-benchmark .claude/skills/cost-benchmark

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cost Benchmark in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Cost Benchmark on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Cost Benchmark is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Cost Benchmark

Runs scripts/bench.mjs against the structural+adversarial corpus and writes per-case + summary results to docs/benchmarks/runs/. This is the verification gate that backs every measurable claim in cost-booster-edit / cost-booster-route.

When to use

Before publishing a release — verify booster win rate didn't regress.
After expanding bench/booster-corpus.json — confirm new cases route correctly.
When auditing a "claimed upstream" tag — flip it to "verified" once the bench supports it.
On a cost question ("is Sonnet 4.6 cheaper than Opus 4.7 for these tasks?") — re-run with BENCH_ANTHROPIC=1.

Steps

Run the bench from v3/ (where agent-booster resolves):

bash
( cd v3 && node ../plugins/ruflo-cost-tracker/scripts/bench.mjs )                  # booster only — free, ~85 ms
( cd v3 && BENCH_LLM_BASELINE=1 node ../plugins/ruflo-cost-tracker/scripts/bench.mjs ) # + Gemini 2.0 Flash (cheap)
( cd v3 && BENCH_LLM_BASELINE=1 BENCH_ANTHROPIC=1 \
     node ../plugins/ruflo-cost-tracker/scripts/bench.mjs )                          # + Sonnet 4.6 + Opus 4.7

Inspect the markdown summary printed to stdout. The gate metric is winRate (Tier 1 cases). Adversarial cases are tracked separately as escalationRate.
Persisted output lands at:
- docs/benchmarks/runs/latest.json — pointer to the most recent run
- docs/benchmarks/runs/<ISO-timestamp>.json — historical record
Read it back in subsequent skills (e.g. cost-report step 2 reads latest.json for live tier-spend numbers).

Smoke gates

winRate ≥ 0.80 on Tier 1 cases (smoke step 23). Lower the threshold by editing scripts/smoke.sh.
escalationRate is reported but ungated — adversarial cases are diagnostic.

Env overrides

Env var	Default	Purpose
`BENCH_LLM_BASELINE`	unset	`=1` runs the OpenAI-compat baseline
`BENCH_LLM_MODEL`	`models/gemini-2.0-flash`	Override the OpenAI-compat model
`BENCH_LLM_BASE_URL`	Gemini OpenAI shim	Override endpoint
`BENCH_ANTHROPIC`	unset	`=1` runs Anthropic baseline (Sonnet 4.6 + Opus 4.7)
`BENCH_ANTHROPIC_MODELS`	`claude-sonnet-4-6,claude-opus-4-7`	Comma-separated Claude IDs
`BENCH_OUT`	timestamped file	Override output path
`BENCH_QUIET=1`	unset	Suppress markdown summary

API keys auto-pulled from gcloud secrets (GOOGLE_AI_API_KEY, ANTHROPIC_API_KEY); override with BENCH_LLM_API_KEY / BENCH_ANTHROPIC_API_KEY.

Cross-references

ADR-0002 §"Decision 1" / §"Riskiest assumption" · cost-booster-edit/SKILL.md (verification table consumes this skill's output) · cost-report/SKILL.md step 2 (reads runs/latest.json).

Frequently asked questions

What does the Cost Benchmark AI skill do?

Run the corpus benchmark — booster locally, optional Gemini/Sonnet/Opus baselines — and persist a verifiable measured-vs-claimed table

Why use Cost Benchmark on TypingMind?

Because you install it once and use it with any model. Cost Benchmark is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Cost Benchmark in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ruvnet/ruflo/tree/main/plugins/ruflo-cost-tracker/skills/cost-benchmark. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Cost Benchmark?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Cost Benchmark?

As many as you like. As long as a model supports skills, you can use Cost Benchmark with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Cost Benchmark AI skill free?

Yes. It is published on GitHub by ruvnet under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Cost Benchmark

When to use

Steps

Smoke gates

Env overrides

Cross-references

Frequently asked questions

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