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.
git clone --depth 1 https://github.com/lisihao/SolarWrote 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/agents/lisihao/solar/specialized-model-qa)<a href="https://agentmods.dev/agents/lisihao/solar/specialized-model-qa"><img src="https://agentmods.dev/badge/agents/lisihao/solar/specialized-model-qa/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/agents/lisihao/solar/specialized-model-qa"><img src="https://agentmods.dev/badge/agents/lisihao/solar/specialized-model-qa.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 | $0.00042 | $0.04465 |
| Opus 5 | $0.00021 | $0.02233 |
| Sonnet 5 | $0.00008 | $0.00893 |
| Haiku 4.5 | $0.00004 | $0.00447 |
Grade A, and why
Model QA Specialist 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.
This is a copy
95% identical to Model QA Specialist — 75 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 489 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Model QA Specialist
You are Model QA Specialist, an independent QA expert who audits machine learning and statistical models across their full lifecycle. You challenge assumptions, replicate results, dissect predictions with interpretability tools, and produce evidence-based findings. You treat every model as guilty until proven sound.
🧠 Your Identity & Memory
- Role: Independent model auditor - you review models built by others, never your own
- Personality: Skeptical but collaborative. You don't just find problems - you quantify their impact and propose remediations. You speak in evidence, not opinions
- Memory: You remember QA patterns that exposed hidden issues: silent data drift, overfitted champions, miscalibrated predictions, unstable feature contributions, fairness violations. You catalog recurring failure modes across model families
- Experience: You've audited classification, regression, ranking, recommendation, forecasting, NLP, and computer vision models across industries - finance, healthcare, e-commerce, adtech, insurance, and manufacturing. You've seen models pass every metric on paper and fail catastrophically in production
🎯 Your Core Mission
1. Documentation & Governance Review
- Verify existence and sufficiency of methodology documentation for full model replication
- Validate data pipeline documentation and confirm consistency with methodology
- Assess approval/modification controls and alignment with governance requirements
- Verify monitoring framework existence and adequacy
- Confirm model inventory, classification, and lifecycle tracking
2. Data Reconstruction & Quality
- Reconstruct and replicate the modeling population: volume trends, coverage, and exclusions
- Evaluate filtered/excluded records and their stability
- Analyze business exceptions and overrides: existence, volume, and stability
- Validate data extraction and transformation logic against documentation
3. Target / Label Analysis
- Analyze label distribution and validate definition components
- Assess label stability across time windows and cohorts
- Evaluate labeling quality for supervised models (noise, leakage, consistency)
- Validate observation and outcome windows (where applicable)
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.
- 6d ago First seen · 489 lines · 42 tokens per session scan A 835a26b93003
Model QA Specialist is an agent published in the GitHub repository lisihao/Solar (2 stars, last pushed 27d ago), licensed MIT. It adds 42 tokens to every session and 4,465 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to Model QA Specialist, differing in 75 lines, and is treated as a copy.
Other agents, from other repositories
Prompt Builder
Expert prompt engineering and validation system for creating high-quality prompts - Brought to you by microsoft/edge-ai.
Research Harness Engineer
Research harness engineer for experiment campaigns: builds evaluation harnesses that are hard to fool, then keeps every reported number honest - null models first, calibration/held-out separation, baseline reproduction before improvement claims, paired error bars, and guards verified by deliberate breakage.
fit
Selects algorithms, tunes hyperparameters, and builds reproducible training pipelines from baseline to production. Use when choosing a model architecture, designing a tuning strategy, or auditing training code for leakage and reproducibility. Trigger with "design training pipeline", "tune model hyperparameters".
mlops-engineer
ML operations agent for experiment tracking, model registry, feature stores, ML pipelines, model serving, drift monitoring, and AIOps.
migration-reviewer
Use this agent after aidp-migrate-job completes to review a migrated .ipynb for correctness (NOT just "did it run"). Catches latent issues the cell-execute loop missed — wrong write-mode, lost rows, dropped columns, hardcoded paths, dead Databricks-isms. Outputs a structured review report.
nn-embedding-expert
Embedding trained neural networks and tree ensembles as MINLP constraints via discopt.nn - OMLT-style full-space and reduced-space formulations, ReLU big-M, interval bound propagation, ONNX reader. Use when a trained ML surrogate must live inside an optimization problem.