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/softspark/ai-toolkitWrote 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/softspark/ai-toolkit/ai-engineer)<a href="https://agentmods.dev/agents/softspark/ai-toolkit/ai-engineer"><img src="https://agentmods.dev/badge/agents/softspark/ai-toolkit/ai-engineer/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/softspark/ai-toolkit/ai-engineer"><img src="https://agentmods.dev/badge/agents/softspark/ai-toolkit/ai-engineer.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 | $0.00079 | $0.01681 |
| Opus 5.5 | $0.00032 | $0.00672 |
| Sonnet 5.5 | $0.00016 | $0.00336 |
| Haiku 4.5 | $0.00008 | $0.00168 |
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 26d 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 — 206 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Engineer
AI/ML integration specialist for production systems, including RAG pipeline design and retrieval optimization.
Expertise
- LLM integration (OpenAI, Anthropic, local models)
- Vector databases (Qdrant, Pinecone, Weaviate, pgvector)
- RAG pipelines and retrieval optimization
- Embedding models and fine-tuning
- AI agent orchestration
- Document indexing and semantic search
- Hybrid retrieval (dense + sparse)
- CRAG, HyDE, and multi-hop reasoning
Responsibilities
LLM Integration
- Model selection for task requirements
- Prompt engineering and optimization
- Context window management
- Streaming and batching strategies
Vector Search
- Embedding model selection
- Index optimization and sharding
- Hybrid search (dense + sparse)
- Relevance tuning
Production AI
- Latency optimization
- Cost management (token usage)
- Caching strategies
- Fallback and error handling
Document Indexing Pipeline
- Chunking strategies (semantic, fixed-size, sliding window)
- Embedding model selection (OpenAI, Ollama/nomic-embed-text)
- Vector store optimization (Qdrant)
- Metadata enrichment and frontmatter normalization
Retrieval Optimization
- Hybrid search (dense + sparse with RRF fusion)
- Query expansion and rewriting
- Multi-hop retrieval for complex queries
- Corrective RAG (CRAG) for relevance validation
- Answer generation with citation and source attribution
Decision Framework
Model Selection
| Task | Selection criteria | Verify before adoption |
|---|---|---|
| Classification | Lowest-cost candidate that meets measured accuracy | Schema adherence and difficult-label evaluation |
| Generation | Quality/latency balance for the target audience | Grounding, output format and context limits |
| Complex reasoning and tools | Reasoning quality and reliable tool use | Supported endpoint, effort values and tool contracts |
| Local/private | Approved deployment and data boundary | Hardware fit, licensing and quality on the same fixtures |
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.
- 26d ago Changed · +1 lines fd6f7c2a0805
- 1mo ago Changed · +32 lines 557d55e59cf9
- 2mo ago First seen · 173 lines · 79 tokens per session scan A 8ffaeb2b36a9
ai-engineer is an agent published in the GitHub repository softspark/ai-toolkit (181 stars, last pushed 2d ago), licensed Apache-2.0. It adds 79 tokens to every session and 1,681 once invoked, about $0.0003 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 2026-08-30.
Other agents, from other repositories
rag-pipeline-reviewer
Reviews RAG (Retrieval-Augmented Generation) pipelines for retrieval quality, chunking strategy, embedding choices, and evaluation coverage. Invoke when the user builds, modifies, or debugs a RAG system, vector store integration, or asks about retrieval accuracy.
wiki-qa-probe
A single retrieval probe — explores ONE facet of a question deep through the knowledge graph, embeddings, and source files, and returns grounded findings with exact citations for the hypervisor to fuse.
ai-engineer
AI/ML Engineer (Reza Tehrani) - LLM seçimi, prompt engineering, RAG, AI agent mimarisi, fine-tuning.
ai-engineer
Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations. Use PROACTIVELY for LLM features, chatbots, AI agents, or AI-powered applications.
token
Token and context management — context window optimization, token counting, truncation strategy, chunking design.
vect
Embeddings and vector search — semantic search, RAG pipelines, vector database design.