Nasde Toolkit

Noesis Agentic Software Development Evals Toolkit

Measure how your whole AI coding setup performs — and what it costs in tokens and dollars across models and providers — so you can choose where to invest and when to switch.

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Why NASDE?

Your team runs AI coding agents — but **which setup is actually best for your codebase, and what is it costing you?** Switch from Claude to Codex, swap a model, add a skill or an MCP server — and you're guessing whether quality went up, down, or just got more expensive.

The decisions that matter are getting expensive: which provider, which model, which configuration — each with a different quality-per-dollar trade-off. NASDE measures your whole harness — the agent, its skills, its MCP servers, against your tasks — and reports not just how good the output is, but how many tokens and how many dollars it took, per model and per provider. That's the data behind a real decision: where to invest, which model to standardize on, and when a migration actually pays off.

It runs on your own machine with a subscription you already have. Today it drives Claude Code, the Codex CLI, and the Gemini CLI; planned: Pi, Cursor, and router-based setups.

What NASDE does — in four steps

One nasde run command executes the whole chain.

1. You describe a task you already understand. An instruction, a repo snapshot, and the assessment criteria describing what a good solution looks like. The output can be anything the agent writes into its workspace — code, a migration plan, an ADR, a SQL script, updated docs.
2. The agent solves it in a sandbox. The agent works in a safe, isolated environment — it can't touch your machine or your real code. Every run starts from the same clean state, so different configurations get a fair comparison. When it's done, a quick test.sh check gives a rough pass/fail signal. Powered by Harbor, runs locally on Docker or in the cloud.
3. A reviewer agent assesses the result against your criteria. After initial rough tests pass or fail, a second coding agent (claude or codex) navigates the workspace and scores your chosen dimensions (e.g. domain modeling, test quality) on whatever scale you picked. The review stays token-efficient even on large codebases.
4. Results land in a dashboard (optional). Browse scores, compare variants, and track how your agent setup evolves over time — optionally via Opik.

You're the one defining "what good looks like." NASDE just automates running the experiment and assessing it the same way every time.

📖 Documentation

Full documentation lives at → noesisvision.github.io/nasde-toolkit

Concepts (how the scoring works, the evaluation pipeline, token & cost, rubric calibration), the complete CLI reference, every configuration-file format, authentication, and step-by-step guides — all there, searchable.

• Quick Start — zero to a working benchmark from your own git history
• How it works — the two independent kinds of scoring, end to end
• A real task, end to end — instruction, criteria, and scores
• CLI reference — the full command reference
• Use Cases · Benchmark Results — worked examples with numbers

What do I use it for?

The core use is a cost-and-quality decision about your AI coding stack: which agent, which model, which provider, which configuration — for our codebase and our budget? NASDE answers it with numbers instead of vibes. Typical things you'd do with it:

• **Compare providers and models on quality and cost** — Claude Code vs. Codex vs. Gemini, Sonnet vs. Opus, against your tasks; see the score and the tokens and dollars each one spends, and pick the best quality-per-dollar for your budget.
• Decide whether a migration pays off — before standardizing on a new agent or model, measure what actually changes in output quality and in spend.
• Measure your whole harness, not just one skill — run your real CLAUDE.md + skills + MCP servers as a unit and see how the full configuration performs.
• Tune a single skill or config — baseline vs. "with my new skill"; see whether it moves the score up or down, and on which dimensions.
• Build a regression suite for your AI setup — re-run the task set whenever someone tweaks the prompt/skills/MCP/model and catch quality or cost regressions before they ship.

Quick start

The fastest path from zero to a working benchmark built from your own git history:

# 1. Install the CLI
uv tool install nasde-toolkit --python 3.13
nasde --version

# 2. Install the authoring skills for Claude Code
nasde install-skills

> Python version: we recommend --python 3.13 (3.12 is also supported). Python 3.14 is not yet supported — a transitive dependency hasn't released cp314 wheels.

Then, from inside your own repo, ask Claude Code:

> "Create a NASDE benchmark with a single task, based on a recent piece of work from this repo — a commit, a range of commits, or a merged PR."

The nasde-benchmark-from-history skill proposes a good candidate and scaffolds the task files for you to review. Then run it:

nasde run --all-variants -C path/to/generated-benchmark

Start small — one task is enough to validate the loop end to end. Your existing claude / codex / gemini CLI auth covers it (a Claude Max or ChatGPT Plus subscription is enough). API keys work too.

→ Full walkthrough: Quick Start · Authentication & Opik

Authoring helpers (Claude Code skills)

Writing assessment_criteria.md, picking tasks from git history, and scaffolding Dockerfiles is the tedious part of building a benchmark. NASDE ships Claude Code skills that take care of most of it — install them with nasde install-skills:

| Skill | What it does | |-------|-------------| | nasde-benchmark-creator | Interactive end-to-end scaffolding: project layout, tasks, Dockerfiles, test scripts, assessment criteria. | | nasde-benchmark-from-history | Point it at a commit range, a merged PR, or a closed issue from your own repo — it proposes tasks based on work your team already finished, and writes the task files for you to review. | | nasde-benchmark-from-public-repos | Describe a skill you want to test broadly; it builds a diversity matrix of public repos (languages, sizes, styles) and scaffolds one task per cell. | | nasde-benchmark-runner | Guides running benchmarks, re-running the reviewer on existing results, verifying the experiment tracker, and troubleshooting failed runs. | | nasde-benchmark-calibration | Publishes trial diffs + scores as PRs/MRs, pulls your review comments back, and proposes concrete rubric edits — the human-in-the-loop calibration loop. |

You don't have to use these — everything they do is just writing files you could write by hand — but they save a lot of typing.

Architecture

See [ARCHITECTURE.md](ARCHITECTURE.md) for the full system architecture with diagrams, and [docs/adr/](docs/adr/) for architectural decision records. Release notes live in [CHANGELOG.md](CHANGELOG.md).

Key design: nasde is a thin integration layer over Harbor and Opik, not a replacement. Core flow uses their Python APIs directly; utility commands pass through to their CLIs unchanged.

Community

Have questions, want to share your benchmarks, or discuss AI agent evaluation strategies? Join our Discord community — we'd love to hear from you!

[](https://discord.gg/QF5PMX4Dqg)

Security

Found a security issue? Please report it privately — see [SECURITY.md](SECURITY.md) for the reporting channels, response timeline, and what's in scope.

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: NoesisVision
• Source: NoesisVision/nasde-toolkit
• License: MIT
• Homepage: https://noesis.vision/nasde/

Install and usage instructions live in the source repository linked above.