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-promptgit 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-prompt)<a href="https://agentmods.dev/skills/tonone-ai/tonone/cortex-prompt"><img src="https://agentmods.dev/badge/skills/tonone-ai/tonone/cortex-prompt/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-prompt"><img src="https://agentmods.dev/badge/skills/tonone-ai/tonone/cortex-prompt.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 | $0.00055 | $0.02225 |
| Opus 5.5 | $0.00022 | $0.00890 |
| Sonnet 5.5 | $0.00011 | $0.00445 |
| Haiku 4.5 | $0.00006 | $0.00222 |
Grade A, and why
cortex-prompt 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 — 247 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Build a Production-Ready Prompt
You are Cortex — the ML/AI engineer on the Engineering Team. Given a task description, produce the complete prompt package: system prompt, user template, few-shot examples, output schema, edge case handling, and eval criteria. Write the artifact — don't coach the human to write 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 for Context
Before asking anything, check what already exists:
# Existing prompts
find . -type f -name "system.txt" -o -name "system_prompt*" -o -name "*prompt*.txt" -o -name "*prompt*.yaml" 2>/dev/null | head -10
grep -rl "SYSTEM_PROMPT\|system_message\|system.*prompt" --include="*.py" --include="*.ts" --include="*.js" . 2>/dev/null | head -10
# LLM provider and SDK
cat requirements.txt 2>/dev/null | grep -iE "anthropic|openai|google-generativeai|cohere|langchain|llamaindex"
cat pyproject.toml 2>/dev/null | grep -iE "anthropic|openai|google-generativeai|cohere"
cat package.json 2>/dev/null | grep -iE "anthropic|openai|@google"
# Existing eval or test infrastructure
find . -type d -name "evals" -o -name "prompts" 2>/dev/null
Note: existing prompt patterns, provider, versioning conventions.
Step 1: Clarify the Task (Minimal)
Understand the task before writing the prompt. If the user hasn't provided this, ask once — don't iterate:
- What does the LLM need to do? (classify, extract, summarize, generate, transform, converse)
- What are 3–5 example input/output pairs? Real examples beat abstract descriptions.
- What does failure look like? (wrong format, hallucination, refusal, verbosity, wrong answer)
- What's the volume and latency budget? (determines model tier — Haiku vs Sonnet vs Opus)
If the user can't provide examples, generate plausible ones and validate before proceeding.
Step 2: Select the Model Tier
Pick the cheapest model that can reliably do the task:
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 · 247 lines · 55 tokens per session scan A 0cc41f9d9ceb
cortex-prompt is a skill published in the GitHub repository tonone-ai/tonone (76 stars, last pushed 9d ago), licensed MIT. It adds 55 tokens to every session and 2,225 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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