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/onset-event-eval-matching/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/onset-event-eval-matching)<a href="https://agentmods.dev/skills/cpuguy96/stepcovnet/onset-event-eval-matching"><img src="https://agentmods.dev/badge/skills/cpuguy96/stepcovnet/onset-event-eval-matching/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/onset-event-eval-matching"><img src="https://agentmods.dev/badge/skills/cpuguy96/stepcovnet/onset-event-eval-matching.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 | $0.00066 | $0.00593 |
| Opus 5.5 | $0.00026 | $0.00237 |
| Sonnet 5.5 | $0.00013 | $0.00119 |
| Haiku 4.5 | $0.00007 | $0.00059 |
Grade A, and why
onset-event-eval-matching 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 2mo 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 — 47 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Onset event eval and matching
Core rule
Training and eval must use the same assignment philosophy. Eval always uses Hungarian matching with tolerance; training uses assign_onset_pairs_l1 (Hungarian L1) in src/stepcovnet/onset_events/losses.py.
Ordered slot→GT pairing on tide (634 GT, 1024 uniform slots) can yield zero in-tolerance pairs → loss drives all confidences toward 0 → 0% F1 despite reasonable predicted times (see NOTE-20260606-13, EXP-07/08).
Diagnostics-first (F1=0 or suspicious)
Before changing epochs, architecture, or loss weights, run diagnostics via the WSL command template with:
scripts/debug_onset_overfit.py --checkpoint=models_wsl/overfit_tide/<frontend>/onset_event_model.keras
Check in output / diagnostics.py:
- Max confidence and count above 0.5
- Hungarian pairs within tolerance vs thresholded detections
- TP / FP / FN breakdown (expect ~233/634 TP at ~30% F1 plateau)
Interpretation guide
| Symptom | Likely cause | Next step |
|---|---|---|
| Max conf ~0.0002, 0% F1 | Train/eval assignment mismatch or no learnable pairs | Verify Hungarian train loss; read NOTE-13 |
| Many pairs in tolerance, low F1 | Threshold / post-hoc filtering | tide-ablations threshold phase |
| ~28–30% F1, high FP | Query-slot formulation cap, not metric bug | Formulation change; see EXP-10 |
Key code paths
| Module | Role |
|---|---|
losses.py |
assign_onset_pairs_l1 — training assignment |
metrics.py |
match_onsets_numpy — eval Hungarian matching |
diagnostics.py |
Confidence sweeps, pair stats |
scripts/debug_onset_overfit.py |
Checkpoint inspection CLI |
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.
- 2mo ago First seen · 47 lines · 66 tokens per session scan A 5887d1866e69
onset-event-eval-matching is a skill published in the GitHub repository cpuguy96/StepCOVNet (22 stars, last pushed 27d ago), licensed Apache-2.0. It adds 66 tokens to every session and 593 once invoked, about $0.0003 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 2026-08-30.
onset-event-eval-matching compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| onset-event-eval-matchingcpuguy96/StepCOVNet | Skill | 22 | 27d ago | Apache-2.0 | A | 66 | Claude Code, Cursor |
| developing-with-streamlitstreamlit/streamlit | Skill | 46k | today | Apache-2.0 | A | 146 | Claude Code |
| physicsnemo-discoverNVIDIA/physicsnemo | Skill | 3.4k | today | Apache-2.0 | A | 124 | Claude Code, Codex |
| depth-estimationSharpAI/DeepCamera | Skill | 3.1k | 1mo ago | MIT | A | 22 | Claude Code, Codex |
| HomeSafe-BenchSharpAI/DeepCamera | Skill | 3.1k | 1mo ago | MIT | A | 28 | Claude Code, Codex |
| perforatedai-plotPerforatedAI/PerforatedAI | Skill | 240 | 2d ago | Apache-2.0 | A | 90 | Claude Code, Codex |
| perforatedai-complex-methodsPerforatedAI/PerforatedAI | Skill | 240 | 2d ago | Apache-2.0 | A | 58 | Claude Code, Codex |