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/AGENTS.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/instructions/cpuguy96/stepcovnet/agents-md)<a href="https://agentmods.dev/instructions/cpuguy96/stepcovnet/agents-md"><img src="https://agentmods.dev/badge/instructions/cpuguy96/stepcovnet/agents-md/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/instructions/cpuguy96/stepcovnet/agents-md"><img src="https://agentmods.dev/badge/instructions/cpuguy96/stepcovnet/agents-md.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 | $0.01611 | $0.01611 |
| Opus 5.5 | $0.00644 | $0.00644 |
| Sonnet 5.5 | $0.00322 | $0.00322 |
| Haiku 4.5 | $0.00161 | $0.00161 |
Grade A, and why
StepCOVNet AGENTS.md 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 — 63 lines — stays where its author put it; the contents beside it link to each section on GitHub.
StepCOVNet — agent entry
Open one index below. Do not load multiple. Specialized rows (e.g. dataset prep, what's next) are alternatives to the general research/skills index — not extra docs to load together.
| If the task is… | Index |
|---|---|
| A procedure (train, overfit, WSL, log EXP/NOTE, debug playbooks) | .cursor/skills/README.md |
| Research (experiments, design, pipeline; log runs in EXPERIMENT_LOG) | docs/research/README.md |
| Paper draft / citations (related work, BibTeX keys, claim ledger) | docs/research/PAPER_LEDGER.md · paper.bib · PAPER_OUTLINE.md |
DDC placement recreation (Dataset A, M-ddc-20ms) |
configs/ddc/placement_fraxtil.json · scripts/train_ddc_placement.py |
| DDCL / ITGPT recreation (then incremental claims) | PAPER_LEDGER.md · configs/ddc/ddcl_placement_fraxtil.json · configs/ddc/itgpt_placement_fraxtil_exp.json · configs/ddc/itgpt_placement_fraxtil_exp_128ep.json · configs/ddc/itgpt_placement_fraxtil_orig_128ep.json · scripts/train_ddcl_placement.py · scripts/train_itgpt_placement.py |
Onset metrics (timing_match, F1, gates) |
docs/research/ONSET_METRICS.md |
| AR onset design (locked stack, gates, debug notes) | docs/research/AR_ONSET_DESIGN.md |
| Scaling AR training (how to add songs and keep numbers comparable) | docs/research/AR_SCALING_LADDER.md |
| What's next / what to do now / orientation | .cursor/skills/whats-next/SKILL.md → EXPERIMENT_LOG.md § Current phase |
| Research autoresearch (one-prompt goal + budget loop) | .cursor/skills/autoresearch/SKILL.md |
| AR tide overfit (gate PASS — champion v8) | EXPERIMENT_LOG.md § Current phase · champion: configs/ar/tide_overfit.json |
| GPU training, WSL, overfit smoke tests | .cursor/skills/wsl-gpu-stepcovnet/SKILL.md |
Repo layout (src/, scripts/, configs/, data) |
docs/agents/project-layout.md |
Dataset prep / final_data pipeline |
docs/research/DATASET_PREP_PIPELINE.md |
| Recreate training data (SMO downloads → prep) | docs/research/TRAINING_DATA_SETUP.md |
| Pre-push validation (same as CI) | pre_submit.py · .github/workflows/pre-submit.yml |
| Steering correction — how the agent decides / promotes lessons | .cursor/skills/steering-correction-promotion/SKILL.md |
| Refresh agent brain / refresh all rules — audit rules, skills, catalogs | .cursor/skills/agent-brain-refresh/SKILL.md |
North star: PIPELINE_ARCHITECTURE.md.
Rules catalog (always vs scoped — Cursor loads alwaysApply automatically): docs/agents/agent-brain.md.
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 · 63 lines · 1,611 tokens per session scan A 66ead12b4919
StepCOVNet AGENTS.md is an instructions file published in the GitHub repository cpuguy96/StepCOVNet (22 stars, last pushed 27d ago), licensed Apache-2.0. It adds 1,611 tokens to every session, about $0.0064 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.
StepCOVNet AGENTS.md compared with instructions from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| StepCOVNet AGENTS.mdcpuguy96/StepCOVNet | Instructions file | 22 | 27d ago | Apache-2.0 | A | 1,611 | Codex, OpenCode |
| vscode buildNext.instructions.mdmicrosoft/vscode | Instructions file | 195k | today | MIT | A | 9,719 | GitHub Copilot |
| next.js AGENTS.mdvercel/next.js | Instructions file | 143k | today | MIT | A | 7,461 | Codex, OpenCode |
| vscode oss-third-party-notices.instructions.mdmicrosoft/vscode | Instructions file | 195k | today | MIT | A | 5,516 | GitHub Copilot |
| langchain AGENTS.mdlangchain-ai/langchain | Instructions file | 149k | today | MIT | A | 4,680 | Codex, OpenCode |
| vscode copilot-instructions.mdmicrosoft/vscode | Instructions file | 195k | today | MIT | A | 3,093 | GitHub Copilot |
| deepseek-harness AGENTS.mddeepseek-ai/deepseek-harness | Instructions file | 275k | 3d ago | MIT | A | 4,313 | Codex, OpenCode |