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.
npx skills add yeaight7/agent-powerups --skill baseline-comparison-reviewgit clone --depth 1 https://github.com/yeaight7/agent-powerupsWrote 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.
[](https://agentmods.dev/skills/yeaight7/agent-powerups/baseline-comparison-review)<a href="https://agentmods.dev/skills/yeaight7/agent-powerups/baseline-comparison-review"><img src="https://agentmods.dev/badge/skills/yeaight7/agent-powerups/baseline-comparison-review/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/yeaight7/agent-powerups/baseline-comparison-review"><img src="https://agentmods.dev/badge/skills/yeaight7/agent-powerups/baseline-comparison-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00039 | $0.00490 |
| Opus 5.5 | $0.00016 | $0.00196 |
| Sonnet 5.5 | $0.00008 | $0.00098 |
| Haiku 4.5 | $0.00004 | $0.00049 |
Grade A, and why
baseline-comparison-review 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 49 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Machine learning models add technical debt. A complex model earns its place only by clearly outperforming a "dumb" baseline; this review makes that comparison explicit before a new model is approved.
When to Use
- A new model architecture is proposed for approval
- A complex model would replace an existing heuristic or rule
- Reported gains have not been compared against any baseline
Inputs
- The candidate model's evaluation results (metric + data split)
- The evaluation code, or enough dataset/task detail to define fair baselines
Workflow
- Define the naive baseline:
- Classification: predict the majority class.
- Regression: predict the mean or median of the training target.
- Time series: predict the last known value (naive persistence).
- Define the heuristic baseline: what simple if/else rule would a domain expert write?
- Evaluate both baselines on the same split and metric as the candidate model.
- Evaluate the delta: if the complex model only beats the heuristic baseline marginally (e.g., ~1%), recommend keeping the heuristic — the complexity is not worth the maintenance cost.
- Demand a baseline evaluation script before approving the new architecture, so the comparison is rerunnable.
Output
- A baseline-vs-model comparison on identical data and metric, with an explicit keep/replace recommendation that weighs maintenance cost
Verification
- Naive baseline defined and evaluated
- Heuristic baseline defined and evaluated (or explicitly ruled out with a reason)
- Candidate compared on the same split and metric as the baselines
- Delta judged against maintenance cost, not just statistical improvement
- A rerunnable baseline evaluation script exists
Failure Modes
- No heuristic baseline — comparing only against the naive baseline makes weak models look strong. Ask what rule a domain expert would write.
- Unequal comparison — baseline evaluated on a different split or metric than the model. Re-run both on identical data.
- Complexity bias — approving a model for a marginal gain without stating the maintenance cost in the recommendation.
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.
- 1mo ago First seen · 49 lines · 39 tokens per session scan A eea84ee570e4
baseline-comparison-review is a skill published in the GitHub repository yeaight7/agent-powerups (6 stars, last pushed 19d ago), licensed Apache-2.0. It adds 39 tokens to every session and 490 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-09-14.
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