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/Amey-Thakur/AI-SKILLSWrote 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/amey-thakur/ai-skills/agent-system-prompt)<a href="https://agentmods.dev/commands/amey-thakur/ai-skills/agent-system-prompt"><img src="https://agentmods.dev/badge/commands/amey-thakur/ai-skills/agent-system-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/commands/amey-thakur/ai-skills/agent-system-prompt"><img src="https://agentmods.dev/badge/commands/amey-thakur/ai-skills/agent-system-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 | $0.00025 | $0.00396 |
| Opus 5 | $0.00013 | $0.00198 |
| Sonnet 5 | $0.00005 | $0.00079 |
| Haiku 4.5 | $0.00003 | $0.00040 |
Grade A, and why
agent-system-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 11d 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
You were invoked as a slash command. The user's input:
$ARGUMENTS
Use that input to fill this prompt's variables (take the main content, topic, or task from it; ask only if a required value is missing and not supplied), then follow the prompt exactly.
Write a system prompt for an AI agent.
Purpose: {purpose} Tools and constraints: {tools}
Cover, in order:
- Role and objective: who the agent is and what success looks like, in one or two sentences. Concrete, not "you are a helpful assistant".
- Capabilities and tools: what it can do, when to use each tool, and how to handle tool errors (retry, fall back, report). Reference tools by what they are for, not just their names.
- Boundaries: what it must NOT do, what requires confirmation, and how to handle requests outside its scope or that it cannot safely fulfill. Be explicit: agents follow the boundaries you write, not the ones you assumed.
- Method: how to approach the task (plan first, verify before acting on irreversible steps, when to ask versus proceed).
- Output format: exactly how to respond, and how to report completion or failure.
Rules: precise and unambiguous (an agent does exactly what the prompt says, including the gaps: vagueness becomes unpredictable behavior). Front-load the most important instructions (models weight the start heavily). State boundaries and safety as hard rules, not suggestions. Keep it lean: every instruction competes for attention, so cut what does not change behavior. If the purpose implies risks (irreversible actions, sensitive data), build in the guardrails explicitly.
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.
- 11d ago First seen · 42 lines · 25 tokens per session scan A 2f14855ad7b9
agent-system-prompt is a command published in the GitHub repository Amey-Thakur/AI-SKILLS (7 stars, last pushed 5d ago), licensed MIT. It adds 25 tokens to every session and 396 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.
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