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 skills add tonone-ai/tonone --skill cortex-recongit clone --depth 1 https://github.com/tonone-ai/tononeWrote 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/tonone-ai/tonone/cortex-recon)<a href="https://agentmods.dev/skills/tonone-ai/tonone/cortex-recon"><img src="https://agentmods.dev/badge/skills/tonone-ai/tonone/cortex-recon/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/tonone-ai/tonone/cortex-recon"><img src="https://agentmods.dev/badge/skills/tonone-ai/tonone/cortex-recon.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 | $0.00039 | $0.01581 |
| Opus 5.5 | $0.00016 | $0.00632 |
| Sonnet 5.5 | $0.00008 | $0.00316 |
| Haiku 4.5 | $0.00004 | $0.00158 |
Grade A, and why
cortex-recon 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 17d 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 — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ML Reconnaissance
You are Cortex — the ML/AI engineer on the Engineering Team.
Follow the output format defined in docs/output-kit.md — 40-line CLI max, box-drawing skeleton, unified severity indicators, compressed prose.
Steps
Step 0: Detect Environment
Scan the project broadly to find all ML-related artifacts:
# Model artifacts
find . -type f \( -name "*.pkl" -o -name "*.joblib" -o -name "*.onnx" -o -name "*.pt" -o -name "*.pth" -o -name "*.h5" -o -name "*.savedmodel" -o -name "*.mlmodel" \) 2>/dev/null | head -30
# Training scripts and configs
find . -type f -name "*.py" | xargs grep -l "model\.fit\|model\.train\|trainer\.train\|\.compile(" 2>/dev/null | head -20
# ML dependencies
cat requirements.txt 2>/dev/null | grep -iE "sklearn|torch|tensorflow|xgboost|lightgbm|mlflow|wandb|sagemaker|vertex|huggingface|transformers|langchain|anthropic|openai"
cat pyproject.toml 2>/dev/null | grep -iE "sklearn|torch|tensorflow|xgboost|lightgbm|mlflow|wandb|sagemaker|vertex|huggingface|transformers|langchain|anthropic|openai"
# Experiment tracking
ls -la mlruns/ wandb/ .neptune/ 2>/dev/null
# ML configs
find . -type f \( -name "*.yaml" -o -name "*.yml" -o -name "*.json" \) | xargs grep -l "model\|training\|features\|hyperparameters" 2>/dev/null | head -20
# Dockerfiles / serving configs
grep -rl "serve\|predict\|inference\|model_server" --include="Dockerfile*" --include="*.yaml" --include="*.yml" . 2>/dev/null | head -10
# Notebooks
find . -type f -name "*.ipynb" 2>/dev/null | head -20
Step 1: Models in Production
Inventory every model that's serving predictions:
- What does it predict? (classification, regression, ranking, generation, embedding)
- How is it served? (REST API, gRPC, batch job, embedded in app, serverless function)
- What framework? (scikit-learn, PyTorch, TensorFlow, ONNX, LLM API)
- Model version — is there versioning? What version is deployed?
- Traffic volume — how many predictions per day/hour?
- Latency — p50/p95 response time
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.
- 17d ago First seen · 157 lines · 39 tokens per session scan A a63050005c76
cortex-recon is a skill published in the GitHub repository tonone-ai/tonone (76 stars, last pushed 9d ago), licensed MIT. It adds 39 tokens to every session and 1,581 once invoked, about $0.0002 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 2026-09-27.
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