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.
git clone --depth 1 https://github.com/promptingbox/mcpWrote 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/promptingbox/mcp/save)<a href="https://agentmods.dev/commands/promptingbox/mcp/save"><img src="https://agentmods.dev/badge/commands/promptingbox/mcp/save/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/commands/promptingbox/mcp/save"><img src="https://agentmods.dev/badge/commands/promptingbox/mcp/save.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 | $0.00009 | $0.00232 |
| Opus 5.5 | $0.00004 | $0.00093 |
| Sonnet 5.5 | $0.00002 | $0.00046 |
| Haiku 4.5 | $0.00001 | $0.00023 |
Grade A, and why
save 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 1mo 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
Save a prompt to the user's PromptingBox account.
Instructions
-
Look at the current conversation context. Identify what the user most likely wants to save:
- If the user just wrote or refined a prompt, save that prompt
- If the conversation contains a system prompt, instruction set, or reusable template, save that
- If unclear, ask the user what they'd like to save
-
Before saving, propose:
- Title: A clear, descriptive title
- Folder: Suggest an appropriate folder based on the prompt's domain (optional)
- Tags: Suggest 1-3 relevant tags (optional)
-
Ask the user to confirm or adjust, then call
save_promptwith the final values. -
After saving, show the user the prompt URL so they can view it in PromptingBox.
If the user provided arguments after the command (e.g., /pbox:save my code review prompt), use that as a hint for what to save.
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.
- 1mo ago First seen · 26 lines · 9 tokens per session scan A 8c50c8aff961
save is a command published in the GitHub repository promptingbox/mcp (0 stars, last pushed 7mo ago), licensed MIT. It adds 9 tokens to every session and 232 once invoked, about $0.0000 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 2026-08-31.
Other commands, from other repositories
prompt
System instructions for writing effective prompts. Apply when generating commands, skills, agents, or any LLM instructions.
boot
Start a new PromptKit prompt assembly session (alias for /bootstrap).
prompt-show
Display full details of a saved prompt by ID.
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.
develop-image-prompt.eval
Generates a detailed image generation prompt from a document or content description. Good output: a prompt that is specific, visual, non-abstract, includes style/composition/lighting guidance, and is calibrated to the specified dimensions and style options.