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 itallstartedwithaidea/agent-skills --skill prompt-architecturegit clone --depth 1 https://github.com/itallstartedwithaidea/agent-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/skills/itallstartedwithaidea/agent-skills/prompt-architecture)<a href="https://agentmods.dev/skills/itallstartedwithaidea/agent-skills/prompt-architecture"><img src="https://agentmods.dev/badge/skills/itallstartedwithaidea/agent-skills/prompt-architecture/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/itallstartedwithaidea/agent-skills/prompt-architecture"><img src="https://agentmods.dev/badge/skills/itallstartedwithaidea/agent-skills/prompt-architecture.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 | $0.00014 | $0.02038 |
| Opus 5.5 | $0.00006 | $0.00815 |
| Sonnet 5.5 | $0.00003 | $0.00408 |
| Haiku 4.5 | $0.00001 | $0.00204 |
Grade A, and why
prompt-architecture 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.
How it starts
The opening of the file, as written. The whole thing — 190 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Architecture
Part of Agent Skills™ by googleadsagent.ai™
Description
Prompt Architecture is the structural engineering of agent instructions. Where casual prompt writing produces fragile, inconsistent results, architectural prompt design creates deterministic, high-performance agent behaviors that hold up under adversarial conditions and scale across thousands of invocations. This skill distills the prompt engineering methodology developed within the googleadsagent.ai™ platform, where Buddy™ handles complex Google Ads analysis through meticulously layered prompt structures.
The fundamental principle is that prompts are not strings — they are programs. A well-architected prompt has a clear execution model: system-level invariants establish the agent's identity and constraints, user-level instructions define the current task, and assistant-level priming shapes the output format and reasoning trajectory. Each layer serves a distinct purpose and must be engineered independently before composition.
Advanced prompt architecture incorporates constraint propagation, output schema enforcement, chain-of-thought scaffolding, and dynamic few-shot example selection. These techniques eliminate the "prompt lottery" problem where identical inputs produce wildly varying output quality across runs.
Use When
- Agent outputs are inconsistent across invocations with the same input
- You need deterministic formatting (JSON, structured reports, specific schemas)
- Complex multi-step reasoning requires explicit chain-of-thought scaffolding
- The agent must adhere to strict behavioral constraints (safety, tone, scope)
- Few-shot examples are needed to establish domain-specific patterns
- You are designing system prompts for production deployment at scale
How It Works
graph TD
A[System Layer] --> B[Identity & Constraints]
A --> C[Output Schema Definition]
A --> D[Tool Definitions]
B --> E[Prompt Assembly]
C --> E
D --> E
F[User Layer] --> G[Task Specification]
F --> H[Dynamic Few-Shot Examples]
G --> E
H --> E
I[Assistant Layer] --> J[Reasoning Primer]
I --> K[Format Enforcement]
J --> E
K --> E
E --> L[Validation Gate]
L -->|Pass| M[Agent Execution]
L -->|Fail| N[Prompt Revision]
N --> E
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 · 190 lines · 14 tokens per session scan A a63f6519af05
prompt-architecture is a skill published in the GitHub repository itallstartedwithaidea/agent-skills (44 stars, last pushed 6mo ago), licensed MIT. It adds 14 tokens to every session and 2,038 once invoked, about $0.0001 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 2026-08-30.
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