Borrowing it
Nothing to install: this file belongs to AtharvaMaik/PromptQueue. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/AtharvaMaik/PromptQueue/main/AGENTS.mdgit clone --depth 1 https://github.com/AtharvaMaik/PromptQueueWrote 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/instructions/atharvamaik/promptqueue/agents-md)<a href="https://agentmods.dev/instructions/atharvamaik/promptqueue/agents-md"><img src="https://agentmods.dev/badge/instructions/atharvamaik/promptqueue/agents-md/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/instructions/atharvamaik/promptqueue/agents-md"><img src="https://agentmods.dev/badge/instructions/atharvamaik/promptqueue/agents-md.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 | $0.00434 | $0.00434 |
| Opus 5.5 | $0.00174 | $0.00174 |
| Sonnet 5.5 | $0.00087 | $0.00087 |
| Haiku 4.5 | $0.00043 | $0.00043 |
Grade A, and why
PromptQueue AGENTS.md 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 2mo 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
PromptQueue Agent Instructions
Use this repo to schedule prompts for later delivery through promptqueue.py.
When To Use
When the user says their AI limits reset at a time, asks to send/paste a prompt later, or asks to queue a prompt for an AI tool, schedule it here.
Example user request:
my limits reset at 7:30 pm, schedule prompt "ABC"
Schedule
Run commands from the repo root.
Prefer --stdin so quoted and multiline prompts survive shell parsing:
@'
ABC
'@ | python promptqueue.py add --stdin 19:30 TARGET
Use local machine time. Convert natural times like 7:30 pm to 19:30. HH:MM schedules today if still future, otherwise tomorrow. Use a dated time when the user gives a date:
@'
ABC
'@ | python promptqueue.py add --stdin "2026-06-24 19:30" TARGET
After queueing, make sure a runner will be alive when the job is due:
python promptqueue.py run
Use run --once only for already-due jobs or smoke tests.
Targets
Infer the target from the user's wording. If no target is named, use the AI tool the user is currently talking to when it has a matching target.
claude Claude web UI
claude-code Claude CLI
codex Codex desktop UI
codex-exec Codex CLI
cursor Cursor UI
antigravity Google Antigravity UI
chatgpt ChatGPT web UI
gemini Gemini web UI
copilot Copilot web UI
copy clipboard only; use only if asked
Run this if target availability is unclear:
python promptqueue.py targets
Use --no-submit only when the user asks to paste but not send.
After scheduling, tell the user the printed job id, target, and scheduled 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.
- 2mo ago First seen · 69 lines · 434 tokens per session scan A 5a645d83a976
PromptQueue AGENTS.md is an instructions file published in the GitHub repository AtharvaMaik/PromptQueue (9 stars, last pushed 3mo ago), licensed MIT. It adds 434 tokens to every session, about $0.0017 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.
PromptQueue AGENTS.md compared with instructions from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| PromptQueue AGENTS.mdAtharvaMaik/PromptQueue | Instructions file | 9 | 3mo ago | MIT | A | 434 | Codex, OpenCode |
| vscode buildNext.instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 9,719 | GitHub Copilot |
| next.js AGENTS.mdvercel/next.js | Instructions file | 143k | today | MIT | A | 7,461 | Codex, OpenCode |
| langchain AGENTS.mdlangchain-ai/langchain | Instructions file | 149k | yesterday | MIT | A | 4,680 | Codex, OpenCode |
| vscode oss-third-party-notices.instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 5,516 | GitHub Copilot |
| vscode copilot-instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 3,093 | GitHub Copilot |
| deepseek-harness AGENTS.mddeepseek-ai/deepseek-harness | Instructions file | 274k | 2d ago | MIT | A | 4,313 | Codex, OpenCode |