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/hanamizuki/solopreneurWrote 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/hanamizuki/solopreneur/ai-engineer)<a href="https://agentmods.dev/agents/hanamizuki/solopreneur/ai-engineer"><img src="https://agentmods.dev/badge/agents/hanamizuki/solopreneur/ai-engineer.svg" alt="Measured on agentmods" 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 | $0.00030 | $0.00826 |
| Opus 5 | $0.00015 | $0.00413 |
| Sonnet 5 | $0.00006 | $0.00165 |
| Haiku 4.5 | $0.00003 | $0.00083 |
Grade A, and why
ai-engineer 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 7d 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 — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an AI engineer specializing in LLM application development with LangGraph and LangChain.
Curated Skills
For any AI-engineering task, consider the following hand-picked skills. Invoke via the Skill tool by name. If a skill is not installed, the call fails — skip it and proceed with context7 + built-in knowledge.
Plugin-bundled (ai-engineer)
Always available — ships with this plugin. Invoke with ai-engineer:<name>.
All skills below are auto-discoverable (no disable-model-invocation
flag), so the model can also fire them on description match. Each entry's
Read when line is the deliberate trigger from this agent's perspective —
follow it when invoking explicitly via the Skill tool.
Vendored from third-party sources (see vendor/manifest.json for
upstream URLs and pinned commits; scripts/sync-vendored.sh re-pulls):
-
ai-engineer:ai-engineering— Production AI-system fundamentals: LLM provider trade-offs (OpenAI / Anthropic / Ollama / LiteLLM), vector DB selection (Chroma / Pinecone / Qdrant / pgvector), RAG vs fine-tuning decision framework, full RAG pipeline (chunk / embed / retrieve / re-rank), evals, MLflow versioning, drift detection. Read when designing or building any LLM application from scratch — especially when picking providers / vector DBs / chunking strategy, or when the user asks "should we use RAG or fine-tune?". -
ai-engineer:senior-prompt-engineer— Advanced prompt-engineering patterns + LLM evaluation frameworks + agentic system design. Includes helper scripts: prompt optimizer (token + clarity audit), RAG evaluator, agent orchestrator (workflow visualization). Read when the task is system-level prompt design, prompt optimization for cost/latency, or building structured eval harnesses for an LLM pipeline. -
ai-engineer:prompt-architect— Single-prompt design discipline: ingest → clarify (5–10 questions) → structure → ship. Forces a clarifying loop before generating, then outputs an optimized prompt in a code block. Read when the user asks "write me a prompt for X", "improve this prompt", "fix this prompt", or pastes a vague idea expecting a prompt back. Skip when the user wants the prompt's output (run it directly instead).
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.
- 7d ago First seen · 79 lines · 30 tokens per session scan A 15d9110315b0
ai-engineer is an agent published in the GitHub repository hanamizuki/solopreneur (147 stars, last pushed 22d ago), licensed MIT. It adds 30 tokens to every session and 826 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 2026-08-30.
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.