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 agentmods add skills/arbazkhan971/godmode/mlopsnpx skills add arbazkhan971/godmode --skill mlopsgit clone --depth 1 https://github.com/arbazkhan971/godmodeWrote 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/arbazkhan971/godmode/mlops)<a href="https://agentmods.dev/skills/arbazkhan971/godmode/mlops"><img src="https://agentmods.dev/badge/skills/arbazkhan971/godmode/mlops.svg" alt="Measured on agentmods" 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 | $0.00010 | $0.01136 |
| Opus 5 | $0.00005 | $0.00568 |
| Sonnet 5 | $0.00002 | $0.00227 |
| Haiku 4.5 | $0.00001 | $0.00114 |
Grade A, and why
mlops 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 yesterday.
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 — 150 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Activate When
/godmode:mlops, "deploy model", "model serving"- "model drift", "retrain", "A/B test models"
- Trained model ready for production deployment
Workflow
1. Model Readiness
Model: <name and version>
Source: EXP-<ID>
Checklist:
[ ] Evaluation complete (test metrics documented)
[ ] Bias/fairness check passed
[ ] Artifacts saved (weights, config, preprocessor)
[ ] Input/output schema documented
[ ] Latency benchmarked (< target p99 ms)
[ ] Size acceptable (< N MB)
IF latency p99 > 100ms: apply optimization. IF model size > 500MB: consider distillation/pruning.
2. Serving Infrastructure
Options:
TF Serving: TensorFlow models, gRPC/REST
Triton: multi-framework, ONNX/TensorRT
SageMaker: managed AWS, auto-scaling
FastAPI/Ray Serve: custom, flexible
# Check for serving frameworks
pip list | grep -iE "fastapi|ray|triton|sagemaker"
ls model_repository/ serve/ 2>/dev/null
3. Inference Optimization
| Optimization | Latency | Size | Accuracy |
| Baseline FP32 | <ms> | <MB> | <val> |
| FP16 quant | <ms> | <MB> | <val> |
| INT8 quant | <ms> | <MB> | <val> |
| ONNX | <ms> | <MB> | <val> |
| Distillation | <ms> | <MB> | <val> |
IF accuracy drop > 1% from quantization: use FP16 only. IF latency target not met: try TensorRT or distillation.
Batching: static (fixed workload), dynamic (variable traffic, max_queue_delay_ms), adaptive (auto-tune).
4. Model Versioning
| Version | Metric | Status | Traffic |
| v3.1 | F1=0.891 | CHAMPION | 90% |
| v3.2 | F1=0.903 | CANARY | 10% |
| v3.0 | F1=0.879 | ARCHIVED | 0% |
Lifecycle: STAGED->CANARY->CHAMPION->ARCHIVED
5. A/B Testing
Champion: v<N> Challenger: v<N>
Split: <champion%>/<challenger%>
Routing: random|user-hash|feature-flag
Duration: <minimum days>
Sample size: <minimum per variant>
Success: primary metric >= <threshold> improvement
Guardrails: latency p99, error rate, business KPIs
IF p-value > 0.05 after min samples: no winner. IF guardrail regresses > 2%: stop test, revert.
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.
- yesterday First seen · 150 lines · 10 tokens per session scan A d46bdea5e69f
mlops is a skill published in the GitHub repository arbazkhan971/godmode (26 stars, last pushed 6d ago), licensed MIT. It adds 10 tokens to every session and 1,136 once invoked, about $0.0001 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 2026-09-03.
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