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 jeet129/praxis --skill agentic-architecturegit clone --depth 1 https://github.com/jeet129/praxisWrote 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/jeet129/praxis/agentic-architecture)<a href="https://agentmods.dev/skills/jeet129/praxis/agentic-architecture"><img src="https://agentmods.dev/badge/skills/jeet129/praxis/agentic-architecture.svg" alt="Measured on agentmods" height="20"></a>- 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 Prompt Injection · line 8 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
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 | $0.00162 | $0.03420 |
| Opus 5 | $0.00081 | $0.01710 |
| Sonnet 5 | $0.00032 | $0.00684 |
| Haiku 4.5 | $0.00016 | $0.00342 |
Grade A, and why
agentic-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 8d 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 — 274 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agentic Architecture
capability: agentic-ai
domain: ml
state: active
dependencies:
- architecture-pattern-selection
- resilience-patterns
- distributed-systems-patterns
- nfr-definition
- ml-problem-framing
triggers:
- "designing a new LLM-powered feature"
- "choosing agent topology (single / multi / planner-executor / supervisor)"
- "designing tool-use surfaces and tool schemas"
- "deciding memory architecture (short-term / episodic / semantic)"
- "placing LLM calls relative to deterministic logic"
- "choosing structured-output / function-calling / streaming patterns"
outputs:
- agent topology decision + ADR
- tool catalog (per tool: schema, side effects, idempotency, authn/authz)
- memory architecture (which memory types; storage; retrieval)
- LLM-vs-deterministic boundary diagram (call graph)
- structured-output / function-calling design
- failure-mode catalog (what does the system do when the LLM is wrong / slow / unavailable)
consumers:
- ml-ai-engineer (primary author)
- solution-architect (co-designs system architecture)
- rag-design (consumes when retrieval-augmented)
- llm-safety (consumes for guardrail placement)
- llm-cost-optimization (consumes for cost-routing decisions)
- evaluation-engineering (consumes for what to evaluate)
references: []
The macro-design layer for LLM-powered features. Done right, it sets up rag-design, evaluation-engineering, llm-safety, and llm-cost-optimization to do their jobs. Done poorly, the system is a chatbot wrapped around a single API call with no story for failure, no story for evaluation, and no story for cost.
The principle: LLMs are non-deterministic components within a deterministic system. Architect the system; the LLM is one (important) part.
When this skill fires
- A new LLM-powered feature is being designed.
- Agent topology is being chosen (single-agent, multi-agent, planner-executor, supervisor).
- Tool-use surfaces are being designed (what the agent can call).
- Memory architecture is being chosen.
- The boundary between LLM calls and deterministic logic is being placed.
- Structured-output / function-calling patterns are being decided.
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.
- 8d ago First seen · 274 lines · 162 tokens per session scan A 0aec2ad6ced3
agentic-architecture is a skill published in the GitHub repository jeet129/praxis (7 stars, last pushed 4d ago), licensed MIT. It adds 162 tokens to every session and 3,420 once invoked, about $0.0008 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 2026-08-31.
Other skills, from other repositories
dspy
DSPy: declarative LM programs, auto-optimize prompts, RAG.
ai-engineering-toolkit
6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.
llm-app-patterns
Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.
llm-security
Use for authorized security assessment of LLM applications and AI agents, including prompt injection, tool abuse, RAG exposure, memory poisoning, and model supply-chain risks.
ai-product
Every product will be AI-powered. The question is whether you'll build it right or ship a demo that falls apart in production.
llm-ops
LLM Operations -- RAG, embeddings, vector databases, fine-tuning, prompt engineering avancado, custos de LLM, evals de qualidade e arquiteturas de IA para producao.