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 hajekim/agentic-design-patterns-extension --skill appendix-agentspacegit clone --depth 1 https://github.com/hajekim/agentic-design-patterns-extensionWrote 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/hajekim/agentic-design-patterns-extension/appendix-agentspace)<a href="https://agentmods.dev/skills/hajekim/agentic-design-patterns-extension/appendix-agentspace"><img src="https://agentmods.dev/badge/skills/hajekim/agentic-design-patterns-extension/appendix-agentspace/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/hajekim/agentic-design-patterns-extension/appendix-agentspace"><img src="https://agentmods.dev/badge/skills/hajekim/agentic-design-patterns-extension/appendix-agentspace.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 | $0.00421 | $0.03227 |
| Opus 5.5 | $0.00168 | $0.01291 |
| Sonnet 5.5 | $0.00084 | $0.00645 |
| Haiku 4.5 | $0.00042 | $0.00323 |
Grade A, and why
appendix-agentspace 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.
This is a copy
100% identical to appendix-agentspace — 3 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 300 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Appendix D - Google AgentSpace
Overview
Google AgentSpace is a no-code enterprise agent platform that enables organizations to build and deploy AI agents without deep programming expertise. It provides a graphical user interface for agent construction, integrating Google Cloud's AI capabilities, enterprise datastores, and Knowledge Graph into a unified, configurable system.
AgentSpace abstracts the underlying technical complexity — autonomous reasoning, knowledge graph mapping, data source integration — into a visual interface where users define agent behavior through prompts, configure knowledge sources, and deploy agents accessible via web interface.
Core Principle: Not all agents need code. AgentSpace enables business teams to deploy context-aware AI agents by configuring prompts and data sources — democratizing agent development beyond engineering teams.
When This Skill Applies
Activate this pattern when:
- Business teams (not just developers) need to deploy AI agents
- Agents need deep integration with enterprise Google Workspace data
- The use case is well-defined and can be specified through prompts
- Rapid prototyping is needed before committing to code-based agents
- Organization uses Google Cloud and wants native integration
- The agent needs Knowledge Graph enrichment for factual accuracy
- Analytics and monitoring of agent usage is required without custom instrumentation
Rule of thumb: If the agent's behavior can be described in a system prompt and the data sources are Google Cloud datastores, AgentSpace is faster than code. If you need custom logic, loops, or non-Google integrations, use ADK or LangGraph.
AgentSpace Architecture
┌─────────────────────────────────────────────────────┐
│ Google AgentSpace │
│ │
│ ┌──────────────┐ ┌─────────────┐ ┌────────────┐ │
│ │ Prompt Gallery│ │ Agent │ │ Analytics │ │
│ │ (Google-made +│ │ Designer │ │ Dashboard │ │
│ │ Custom) │ │ (No-code) │ │ │ │
│ └──────┬───────┘ └──────┬──────┘ └────────────┘ │
│ │ │ │
│ ┌──────▼─────────────────▼──────────────────────┐ │
│ │ Agent Runtime │ │
│ │ (Gemini model + Prompt + Knowledge + Data) │ │
│ └──────────────────────┬─────────────────────────┘ │
│ │ │
│ ┌──────────┐ ┌────────▼────────┐ ┌────────────┐ │
│ │ Google │ │ Connected Data │ │ Web UI │ │
│ │ Knowledge│ │ Stores │ │ (Chat) │ │
│ │ Graph │ │ (BigQuery, GCS) │ │ │ │
│ └──────────┘ └─────────────────┘ └────────────┘ │
└─────────────────────────────────────────────────────┘
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 · 300 lines · 421 tokens per session scan A 3f993ef7f633
appendix-agentspace is a skill published in the GitHub repository hajekim/agentic-design-patterns-extension (1 stars, last pushed 6mo ago), licensed MIT. It adds 421 tokens to every session and 3,227 once invoked, about $0.0017 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to appendix-agentspace, differing in 3 lines, and is treated as a copy.
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