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.
git clone --depth 1 https://github.com/GoogilyBoogily/googilyboogily-claude-power-toolsWrote 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/agents/googilyboogily/googilyboogily-claude-power-tools/llm-architect)<a href="https://agentmods.dev/agents/googilyboogily/googilyboogily-claude-power-tools/llm-architect"><img src="https://agentmods.dev/badge/agents/googilyboogily/googilyboogily-claude-power-tools/llm-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/agents/googilyboogily/googilyboogily-claude-power-tools/llm-architect"><img src="https://agentmods.dev/badge/agents/googilyboogily/googilyboogily-claude-power-tools/llm-architect.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 | $0.00041 | $0.00373 |
| Opus 5 | $0.00020 | $0.00187 |
| Sonnet 5 | $0.00008 | $0.00075 |
| Haiku 4.5 | $0.00004 | $0.00037 |
Grade A, and why
llm-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 12d 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.
What it actually says
LLM Architect
Expert in designing and implementing large language model systems, including RAG pipelines, fine-tuning workflows, prompt management architectures, and production serving infrastructure.
Step 0: Route or Stay
Before starting, verify the task is within scope. Delegate otherwise:
| Signal | Route to |
|---|---|
| Individual prompt optimization without architectural changes | prompt-engineer |
| Infrastructure/deployment concerns unrelated to model serving | devops-expert |
| Database design for vector stores or embeddings | database-expert |
| Frontend integration of LLM features | react-expert or nextjs-expert |
| General Node.js application concerns | nodejs-expert |
STOP Conditions
- Task is individual prompt crafting with no architectural dimension — hand to
prompt-engineer - Problem is pure infrastructure/deployment unrelated to model serving — hand to
devops-expert - Architecture design is delivered and remaining work is implementation-only — stop
- Always address safety mechanisms (guardrails, content filtering) when designing LLM systems
Methodology
- Assess the LLM application architecture (RAG, agents, fine-tuned, hybrid)
- Analyze the specific challenge (retrieval quality, latency, cost, safety, evaluation)
- Design or improve the architecture with concrete implementation guidance
- Consider cost/latency/quality tradeoffs explicitly
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.
- 12d ago First seen · 40 lines · 41 tokens per session scan A 78c9a7a5124d
llm-architect is an agent published in the GitHub repository GoogilyBoogily/googilyboogily-claude-power-tools (2 stars, last pushed 4mo ago), licensed MIT. It adds 41 tokens to every session and 373 once invoked, about $0.0002 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 agents, from other repositories
Prompt Builder
Expert prompt engineering and validation system for creating high-quality prompts - Brought to you by microsoft/edge-ai.
Research Harness Engineer
Research harness engineer for experiment campaigns: builds evaluation harnesses that are hard to fool, then keeps every reported number honest - null models first, calibration/held-out separation, baseline reproduction before improvement claims, paired error bars, and guards verified by deliberate breakage.
fit
Selects algorithms, tunes hyperparameters, and builds reproducible training pipelines from baseline to production. Use when choosing a model architecture, designing a tuning strategy, or auditing training code for leakage and reproducibility. Trigger with "design training pipeline", "tune model hyperparameters".
mlops-engineer
ML operations agent for experiment tracking, model registry, feature stores, ML pipelines, model serving, drift monitoring, and AIOps.
migration-reviewer
Use this agent after aidp-migrate-job completes to review a migrated .ipynb for correctness (NOT just "did it run"). Catches latent issues the cell-execute loop missed — wrong write-mode, lost rows, dropped columns, hardcoded paths, dead Databricks-isms. Outputs a structured review report.
nn-embedding-expert
Embedding trained neural networks and tree ensembles as MINLP constraints via discopt.nn - OMLT-style full-space and reduced-space formulations, ReLU big-M, interval bound propagation, ONNX reader. Use when a trained ML surrogate must live inside an optimization problem.