equal-engineering-baselines

equal-engineering-baselines is a skill for Claude Code from AURORA-NEURO/aurora-agent. It costs 95 tokens per session (1,259 once invoked), scanned A, original, Apache-2.0.

A method for making comparisons between software systems fair by giving each baseline the same level of engineering and tuning. A baseline is the reference implementation used to judge another system.

In plain words
What is it for?
Use it when designing benchmarks, tuning comparison systems, reviewing result tables, or reporting cases where a system cannot or should not answer.
Why use it?
It prevents weakly configured reference systems from making a new system look better than it really is, and keeps refusals separate from failed results.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the aurora-science plugin — 6 skills shipped together

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/aurora-neuro/aurora-agent/equal-engineering-baselines
Any agent
npx skills add AURORA-NEURO/aurora-agent --skill equal-engineering-baselines
Clone the repo
git clone --depth 1 https://github.com/AURORA-NEURO/aurora-agent

Made for: Claude Code.

Or install aurora-science, the plugin that ships this one along with the rest of its 6 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for equal-engineering-baselines

README.md
[![agentmods](https://agentmods.dev/badge/skills/aurora-neuro/aurora-agent/equal-engineering-baselines.svg)](https://agentmods.dev/skills/aurora-neuro/aurora-agent/equal-engineering-baselines)
Your own site
<a href="https://agentmods.dev/skills/aurora-neuro/aurora-agent/equal-engineering-baselines"><img src="https://agentmods.dev/badge/skills/aurora-neuro/aurora-agent/equal-engineering-baselines.svg" alt="Measured on agentmods" height="20"></a>
Per session 95 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,259 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00095 $0.01259
Opus 5 $0.00048 $0.00629
Sonnet 5 $0.00019 $0.00252
Haiku 4.5 $0.00010 $0.00126

Measured 6d ago against content hash f3a5a33fc846, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

equal-engineering-baselines scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

plugins/aurora-science/skills/equal-engineering-baselines/SKILL.md · 94 lines

How it starts

The opening of the file, as written. The whole thing — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Note: the crate paths, documents, and measured numbers below are illustrations from the aurora-agent workspace where these methods were developed and tested. The methods themselves apply to any comparison or benchmarking effort.

Equal-engineering baselines

A comparison is only evidence if the baselines were engineered as seriously as the system being sold. The workspace learned this from its own upstream distribution, and encoded the lesson in a harness (crates/baseline) whose design decisions are worth copying anywhere.

Tune the baseline where it wins, not where it loses

The distribution's own comparison script measured its graph baseline at depth 7 and unbounded only — the two settings where the walk returns the entire 761-fact world. It never measured depths 5 or 6, where the identical code returns 11 facts, matching the compiler exactly. The published comparison therefore showed a 69x advantage that disappears entirely under equal tuning (docs/FINDINGS.md, "The distribution's own baseline script is a strawman").

The method:

  1. Sweep every tuning knob the baseline has — depth for walks, k for retrievers — and report the baseline at its best setting, not at the setting that flatters your system.
  2. Give each family its strongest member. The workspace's panel runs graph walks at four depths, a connected-component upper bound, two lexical budgets, two embedding budgets, and an unbounded directed walk (crates/baseline, default_panel()), alongside full-context as the recall ceiling.
  3. Label proxies as proxies. The panel's "embedding" retriever is a fixed-basis hashed-trigram model and every report line says so: "a lexical proxy for embedding retrieval, not a neural model." A baseline that impersonates a stronger method inflates your win over the real thing.
  4. If a baseline stays competitive under equal optimization, report that result. On the reference world, three differently-engineered baselines select the identical eleven facts the compiler selects — not the same count, the identical set — and the comparison document says the cheapest admissible strategy is the graph walk, not the compiler.

Read the full file on GitHub · 94 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 6d ago First seen · 94 lines · 95 tokens per session scan A f3a5a33fc846

Subscribe to this mod's changes

equal-engineering-baselines is a skill published in the GitHub repository AURORA-NEURO/aurora-agent (1 stars, last pushed 3d ago), licensed Apache-2.0. It adds 95 tokens to every session and 1,259 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Related

Other skills, from other repositories

blazemeter-performance-testing

Comprehensive guide for BlazeMeter Performance Testing, including load configuration, reporting, JMeter configuration, Taurus, scenarios, and advanced features. Use when working with Performance tests for (1) Configuring load settings and distribution, (2) Creating and running tests (JMeter, Browser, URL/API…

Blazemeter/bzm-mcp · 138 tokens

blazemeter-api-reference

Comprehensive reference for BlazeMeter REST APIs, including authentication, identifiers, and API endpoints. Use when working with BlazeMeter APIs for (1) Understanding BlazeMeter REST API structure, (2) Authenticating API requests, (3) Obtaining identifiers (Workspace ID, Project ID, Test ID, etc.), (4) Using Test…

Blazemeter/bzm-mcp · 109 tokens

blazemeter-private-locations

Comprehensive guide for BlazeMeter Private Locations, including Radar Agent, installation (Docker, Kubernetes, Helm), configuration, management, and troubleshooting. Use when working with Private Locations for (1) Installing agents (Docker, Kubernetes, Helm Chart), (2) Configuring Radar Agent for API Monitoring, (3)…

Blazemeter/bzm-mcp · 103 tokens

blazemeter-administration

Comprehensive guide for BlazeMeter Administration, including workspaces, projects, security, alerts, and team management. Use when working with administration for (1) Managing workspaces and projects, (2) Configuring security settings (SAML SSO, 2FA, API keys), (3) Creating workspace alerts, (4) Managing private…

Blazemeter/bzm-mcp · 111 tokens

blazemeter-functional-testing

Comprehensive guide for BlazeMeter Functional Testing, including GUI Functional Tests, API Tests (deprecated), Action Library, and debugging. Use when working with Functional Testing for (1) Creating GUI Functional Tests (YAML, Java IDE, Python IDE), (2) Managing Functional Tests (duplicate, delete, move, rename), (3)…

Blazemeter/bzm-mcp · 124 tokens

blazemeter-getting-started

Getting started guides for BlazeMeter, including onboarding, migration guides, continuous testing journey, glossary, and mobile testing. Use when getting started with BlazeMeter for (1) Navigating BlazeMeter University onboarding, (2) Migrating from Runscope or JMeter, (3) Understanding the continuous testing journey…

Blazemeter/bzm-mcp · 96 tokens