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 | $0.00007 | $0.01019 |
| Sonnet 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 10d 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.
- 10d 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 (37 stars, last pushed 5mo ago), licensed MIT. It adds 14 tokens to every session and 2,038 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 2026-08-30.
Other skills, from other repositories
implementing-llm-guardrails-for-security
Implements input and output validation guardrails for LLM-powered applications to prevent prompt injection, data leakage, toxic content generation, and hallucinated outputs. Builds a security validation pipeline using NVIDIA NeMo Guardrails Colang definitions, custom Python validators for PII detection and content…
prompting
Guide for writing effective system prompts for LLM agents. Use when creating or editing system prompts for applications, agent configurations, or development tools.
few-shot-examples
Curated few-shot examples for construction AI tasks: classification, extraction, analysis. Domain-specific examples for improved LLM performance.
prompt-templates
Reusable prompt templates for construction AI tasks: cost estimation, schedule analysis, document processing, BIM queries. Structured prompts for consistent results.
opik-optimizer
Optimize LLM prompts, tools, and agents in Opik using standardized optimizer workflows (prompt optimization, tool optimization, and parameter tuning), dataset/metric wiring, and result interpretation.
prism
Consultant for NotebookLM steering prompt design. Optimizes Audio/Video/Slide/Infographic output quality through source preparation, prompt engineering, and Custom Goals persona design.