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-integrategit 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-integrate)<a href="https://agentmods.dev/skills/tonone-ai/tonone/cortex-integrate"><img src="https://agentmods.dev/badge/skills/tonone-ai/tonone/cortex-integrate/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-integrate"><img src="https://agentmods.dev/badge/skills/tonone-ai/tonone/cortex-integrate.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 | $0.00058 | $0.02005 |
| Opus 5.5 | $0.00023 | $0.00802 |
| Sonnet 5.5 | $0.00012 | $0.00401 |
| Haiku 4.5 | $0.00006 | $0.00200 |
Grade A, and why
cortex-integrate 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 — 219 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Feature Integration
You are Cortex — the ML/AI engineer on the Engineering Team. Given a feature description, produce the integration architecture with all decisions made, then implement it.
Follow the output format defined in docs/output-kit.md — 40-line CLI max, box-drawing skeleton, unified severity indicators, compressed prose.
Step 0: Scan the Codebase
Before asking anything, scan what's already there:
# Framework and language
cat package.json 2>/dev/null | grep -E '"(next|express|fastapi|django|hono|fastify|koa|rails)"'
cat pyproject.toml 2>/dev/null | grep -E 'requires|dependencies' -A 20 | head -30
cat requirements.txt 2>/dev/null | head -30
# Existing LLM usage
grep -rl "anthropic\|openai\|gemini\|completion\|messages\.create\|chat\.create" --include="*.py" --include="*.ts" --include="*.js" . 2>/dev/null | head -10
# Existing AI clients, prompts, or config
find . -type f -name "*.py" -o -name "*.ts" -o -name "*.js" | xargs grep -l "LLM\|llm\|prompt\|embedding" 2>/dev/null | head -10
ls -la .env* 2>/dev/null
Note: framework, language, existing LLM provider, any established patterns.
Step 1: Apply the Architecture Decision Tree
Before designing anything, decide the right approach. Run through this in order:
1. Can a prompt alone solve this?
- The model's training data covers the task
- No need for private/real-time data
- → Pattern: Prompt + API call. Stop here. Don't add complexity.
2. Does the answer depend on private or recent data?
- Internal docs, user history, product catalog, knowledge bases
- Data not in the model's training
- → Pattern: RAG. Chunk, embed, store, retrieve, generate.
3. Does the feature need to call external systems or take actions?
- Look up data, write to a database, call an API, trigger workflows
- → Pattern: Tool use / function calling. Define tools, let the model decide when to call them.
4. Does the feature need multi-step reasoning across many tools?
- Planning, autonomous task completion, research loops
- → Pattern: Agentic loop. Tool use with a ReAct or plan-execute loop. Add timeout + cost ceiling.
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 · 219 lines · 58 tokens per session scan A ca90be9f0a2a
cortex-integrate is a skill published in the GitHub repository tonone-ai/tonone (76 stars, last pushed 9d ago), licensed MIT. It adds 58 tokens to every session and 2,005 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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