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 agentmods add commands/hypd-ai/ads-mcp-plugin/promptsgit clone --depth 1 https://github.com/HYPD-AI/ads-mcp-pluginWrote 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/commands/hypd-ai/ads-mcp-plugin/prompts)<a href="https://agentmods.dev/commands/hypd-ai/ads-mcp-plugin/prompts"><img src="https://agentmods.dev/badge/commands/hypd-ai/ads-mcp-plugin/prompts.svg" alt="Measured on agentmods" 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 | $0.00017 | $0.00182 |
| Opus 5 | $0.00009 | $0.00091 |
| Sonnet 5 | $0.00003 | $0.00036 |
| Haiku 4.5 | $0.00002 | $0.00018 |
Grade A, and why
prompts 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 5d 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.
What it actually says
Help the user find and run a HYPD prompt template.
- Ensure
inithas been run once this session — call it first if not. - Call
prompt_templates_list, filtered by $ARGUMENTS when given (category, platform, or free-text query). Skip entries whoserunsInis for a different assistant. - Present the matches as a short grouped list of titles — never dump the raw output — and let the user pick.
- Call
prompt_templates_runwith the chosen key and follow its [input] directives.
If HYPD is unavailable or not authenticated, do not fail — follow the hypd-getting-started skill to guide the user through connecting.
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.
- 5d ago First seen · 12 lines · 17 tokens per session scan A 8c662255a567
prompts is a command published in the GitHub repository HYPD-AI/ads-mcp-plugin (2 stars, last pushed 12d ago), licensed MIT. It adds 17 tokens to every session and 182 once invoked, about $0.0001 per session on Opus 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 2026-08-31.
Other commands, from other repositories
prompt
System instructions for writing effective prompts. Apply when generating commands, skills, agents, or any LLM instructions.
music-suno-prompt
Grounded Suno prompt synthesis from local knowledge corpus + persona canon + label canon. No vibes-prompting.
ai
Load the Kaizen skill for production-ready AI agent implementation with signature-based programming and multi-agent coordination.
audit-prompt
Evaluate an existing prompt for clarity, effectiveness, and edge cases.
dare-llm-integration
Integração segura e eficiente com LLMs (Gemini, Claude, OpenAI, Ollama) em projetos DARE.
prompt-optimize
You are an expert prompt engineer specializing in crafting effective prompts for LLMs through advanced techniques including constitutional AI, chain-of-thought reasoning, and model-specific optimization.