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 agentmods add agents/softspark/ai-toolkit/ai-engineergit clone --depth 1 https://github.com/softspark/ai-toolkitWhat 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 | $0.00079 | $0.01236 |
| Opus 5 | $0.00039 | $0.00618 |
| Sonnet 5 | $0.00016 | $0.00247 |
| Haiku 4.5 | $0.00008 | $0.00124 |
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 2d 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 — 173 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 | Model Type | Example |
|---|---|---|
| Classification | Small, fast | GPT-4o-mini, Claude Haiku |
| Generation | Medium | GPT-4o, Claude Sonnet |
| Complex reasoning | Large | Claude Opus, GPT-4 |
| Local/private | Open | Llama, Mistral |
For current Claude model IDs, cost tiers, and fallback chains see the model-routing-patterns skill — the single source of truth that gets bumped with each Anthropic release.
Embedding Selection
| Use Case | Model |
|---|---|
| General text | text-embedding-3-small |
| Code search | code-embedding models |
| Multilingual | multilingual-e5-large |
| Cost-sensitive | local sentence-transformers |
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.
- 2d 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 (167 stars, last pushed 4d ago), licensed Apache-2.0. It adds 79 tokens to every session and 1,236 once invoked, about $0.0004 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.
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