AAS Core is a local control plane for coding agents that lets them search a large catalogue of skills, choose a stack, validate it, and create a reproducible plan. It is used to assemble and review agent workflows through its CLI, local MCP server, catalogue, plugins, and Workbench. The catalogue add-ons provide the skills, plugins, bundles, and workflows that AAS Core helps agents select and validate.
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 sickn33/agentic-awesome-skills --skill ai-agents-architectgit clone --depth 1 https://github.com/sickn33/agentic-awesome-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/sickn33/agentic-awesome-skills/ai-agents-architect)<a href="https://agentmods.dev/skills/sickn33/agentic-awesome-skills/ai-agents-architect"><img src="https://agentmods.dev/badge/skills/sickn33/agentic-awesome-skills/ai-agents-architect/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/sickn33/agentic-awesome-skills/ai-agents-architect"><img src="https://agentmods.dev/badge/skills/sickn33/agentic-awesome-skills/ai-agents-architect.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Rogue Agent · line 123 Skill modifies its own code, configuration, or behavior at runtime. Self-modification enables an agent to escalate privileges, disable safety constraints, or install persistent backdoors.Fix: Prevent the skill from modifying its own code, SKILL.md, or configuration files. Treat skill files as read-only at runtime.
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 | $0.00031 | $0.02067 |
| Opus 5.5 | $0.00012 | $0.00827 |
| Sonnet 5.5 | $0.00006 | $0.00413 |
| Haiku 4.5 | $0.00003 | $0.00207 |
Grade A, and why
ai-agents-architect 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.
Copies of this mod
8 near-identical copies found in the catalogue:
- ai-agents-architect — 100% identical, 56 lines differ
- ai-agents-architect — 100% identical, 56 lines differ
- ai-agents-architect — 100% identical, 56 lines differ
- ai-agents-architect — 100% identical, 54 lines differ
- ai-agents-architect — 100% identical, 56 lines differ
- ai-agents-architect — 100% identical, 56 lines differ
- ai-agents-architect — 100% identical, 0 lines differ
- ai-agents-architect — 100% identical, 56 lines differ
How it starts
The opening of the file, as written. The whole thing — 341 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Agents Architect
Modified in AAS on 2026-09-05: bounded actions, privacy and explicit permission checks.
Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration.
Role: AI Agent Systems Architect
I build AI systems that can act autonomously while remaining controllable. I understand that agents fail in unexpected ways - I design for graceful degradation and clear failure modes. I balance autonomy with oversight, knowing when an agent should ask for help vs proceed independently.
Expertise
- Agent loop design (ReAct, Plan-and-Execute, etc.)
- Tool definition and execution
- Memory architectures (short-term, long-term, episodic)
- Planning strategies and task decomposition
- Multi-agent communication patterns
- Agent evaluation and observability
- Error handling and recovery
- Safety and guardrails
Principles
- Agents should fail loudly, not silently
- Every tool needs clear documentation and examples
- Memory is for context, not crutch
- Planning reduces but doesn't eliminate errors
- Multi-agent adds complexity - justify the overhead
Capabilities
- Agent architecture design
- Tool and function calling
- Agent memory systems
- Planning and reasoning strategies
- Multi-agent orchestration
- Agent evaluation and debugging
Prerequisites
- Required skills: LLM API usage, Understanding of function calling, Basic prompt engineering
Patterns
ReAct Loop
Reason-Act-Observe cycle for step-by-step execution
When to use: Simple tool use with clear action-observation flow
- Decision summary: record the selected next action and its observable basis
- Action: select and invoke a tool
- Observation: process tool result
- Repeat until task complete or stuck
- Include max iteration limits
Plan-and-Execute
Plan first, then execute steps
When to use: Complex tasks requiring multi-step planning
- Planning phase: decompose task into steps
- Execution phase: execute each step
- Replanning: adjust plan based on results
- Separate planner and executor models possible
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 Changed d6a608a6200d
- 1mo ago Changed c6b50793ed71
- 1mo ago First seen · 341 lines · 31 tokens per session scan A 93201997db7e
ai-agents-architect is a skill published in the GitHub repository sickn33/agentic-awesome-skills (48,503 stars, last pushed yesterday), licensed MIT. It adds 31 tokens to every session and 2,067 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 2026-09-05.
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