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 agentmods add skills/entityprocess/agentv/agent-architecture-designnpx skills add EntityProcess/agentv --skill agent-architecture-designgit clone --depth 1 https://github.com/EntityProcess/agentvWhat 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 | $0.00070 | $0.01123 |
| Opus 5 | $0.00035 | $0.00562 |
| Sonnet 5 | $0.00014 | $0.00225 |
| Haiku 4.5 | $0.00007 | $0.00112 |
Grade A, and why
agent-architecture-design 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 2d 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 — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Architecture Design
Overview
Guide the selection and design of the correct agentic architecture by diagnosing the problem type, mapping it to a proven design pattern, and defining the workflow structure, tooling, and management model.
Process
Phase 1: Problem Diagnosis
Categorize the request on two axes:
| Task-Level (single job) | Project-Level (coordination needed) | |
|---|---|---|
| Software-Shaped (working code/system) | Single-Agent Iterative Loop | Autonomous Pipeline or Multi-Agent System |
| Metric-Shaped (optimize a number) | Optimization Loop | Optimization Loop + Multi-Agent System |
Diagnosis questions:
- Is the goal working software or optimizing a metric?
- Is this a single discrete task or multiple coordinated parts?
- How much human involvement is acceptable during execution?
- What scale justifies the architecture complexity?
Phase 2: Pattern Selection
Load references/agentic-design-patterns.md for full details on each pattern. Summary:
Single-Agent Iterative Loop (Agentic IDE)
- Human = manager, Agent = worker
- Decompose the problem into small chunks (UI, API, tests)
- Agent gets a workspace (terminal, files, search)
- Best for: individual developer productivity on discrete tasks
Autonomous Pipeline (Zero-Human Loop)
- Spec In → Autonomous Zone → Eval Out
- Heavy human involvement at start (specs) and end (review), zero in the middle
- Requires robust evals — iterations happen automatically until eval passes
- Best for: zero-human-intervention software delivery
Optimization Loop (Self-Improving Agent)
- Hill climbing against a specific metric
- Agent tries paths, fails, backtracks
- Needs a clear optimization target
- Best for: reaching peak of an optimization metric through experimentation
Multi-Agent System (Hierarchical/Supervisor Pattern)
- Specialized roles with defined handoffs (Researcher → Writer → Editor → Publisher)
- Complexity lies in context management between agents
- Only justified at scale (10,000 tickets, not 10)
- Best for: seamless coordination across specialized AI workers
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 2d ago First seen · 109 lines · 70 tokens per session scan A 565807b5f63a
agent-architecture-design is a skill published in the GitHub repository EntityProcess/agentv (15 stars, last pushed 1mo ago), licensed MIT. It adds 70 tokens to every session and 1,123 once invoked, about $0.0003 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.
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