Borrowing it
Nothing to install: this file belongs to cpuguy96/StepCOVNet. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/cpuguy96/StepCOVNet/master/.cursor/skills/tide-overfit-protocol/SKILL.mdgit clone --depth 1 https://github.com/cpuguy96/StepCOVNetWrote 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/cpuguy96/stepcovnet/tide-overfit-protocol)<a href="https://agentmods.dev/skills/cpuguy96/stepcovnet/tide-overfit-protocol"><img src="https://agentmods.dev/badge/skills/cpuguy96/stepcovnet/tide-overfit-protocol/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/cpuguy96/stepcovnet/tide-overfit-protocol"><img src="https://agentmods.dev/badge/skills/cpuguy96/stepcovnet/tide-overfit-protocol.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 | $0.00060 | $0.00560 |
| Opus 5 | $0.00030 | $0.00280 |
| Sonnet 5 | $0.00012 | $0.00112 |
| Haiku 4.5 | $0.00006 | $0.00056 |
Grade A, and why
tide-overfit-protocol 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 11d 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.
What it actually says
Tide overfit protocol
When to use
Single-song memorization check on tide before scaling to multi-song val. Default 50 epochs; 100 ep only when explicitly testing epoch sensitivity.
Preconditions
- Tide audio/chart paths in
configs/overfit_tide/*.json - MERT:
data/v2/test/tide.mert.npy(or path in configmert_features_dir) - Do not set
pipeline_check_shortcuts=trueunless explicitly debugging pipeline wiring
Run (WSL GPU)
Use the WSL command template with:
scripts/run_overfit_tide_suite.py --epochs=50
Subset frontends: --frontends=conv1d,mel
Scripts auto-dispatch from Windows when WSL is available.
Configs and artifacts
| Item | Path |
|---|---|
| Configs | configs/overfit_tide/{conv1d,mel,mert}.json |
| Models | models_wsl/overfit_tide/<frontend>/ |
| Summary | models_wsl/overfit_tide/suite_summary.json |
| Callbacks | callbacks/overfit_tide/<frontend>/ |
After the run
- Read
suite_summary.jsonfor per-frontend F1 / precision / recall - Log
EXP-YYYYMMDD-NNin EXPERIMENT_LOG.md with full timestamp (index + entry) - If F1=0 or conv1d collapse → follow onset-event-eval-matching before retraining
- If plateau persists → tide-ablations or formulation change per EXPERIMENT_LOG.md § Recommended next step
Related
- wsl-gpu-stepcovnet
- PIPELINE_ARCHITECTURE.md — tag stage
prefor frontend comparison
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.
- 11d ago First seen · 48 lines · 60 tokens per session scan A f552d97e0cf9
tide-overfit-protocol is a skill published in the GitHub repository cpuguy96/StepCOVNet (22 stars, last pushed 17d ago), licensed Apache-2.0. It adds 60 tokens to every session and 560 once invoked, about $0.0003 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 2026-08-30.
Other skills, from other repositories
HomeSafe-Bench
VLM indoor safety hazard detection benchmark inspired by HomeSafeBench (arXiv 2509.23690).
physicsnemo-discover
Official NVIDIA-authored guidance for navigating PhysicsNeMo — pick the model, datapipe, or example for a SciML/AI4Science task (surrogates, forecasting, downscaling, physics-informed, inverse, generative). Points at existing files via live repo search; never writes code. Do NOT use for installation or environment…
SmartHome Video Anomaly Benchmark
VLM evaluation suite for video anomaly detection in smart home camera footage.
Home Security AI Benchmark
LLM & VLM evaluation suite for home security AI applications.
depth-estimation
Real-time depth map privacy transforms using Depth Anything v2 (CoreML + PyTorch).
Cloud Provider Regression Test
Connectivity, chat, JSON & streaming regression tests for all enabled cloud LLM providers.