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 olehsvyrydov/AI-development-team --skill ai-engineergit clone --depth 1 https://github.com/olehsvyrydov/AI-development-teamWrote 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/olehsvyrydov/ai-development-team/ai-engineer)<a href="https://agentmods.dev/skills/olehsvyrydov/ai-development-team/ai-engineer"><img src="https://agentmods.dev/badge/skills/olehsvyrydov/ai-development-team/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/skills/olehsvyrydov/ai-development-team/ai-engineer"><img src="https://agentmods.dev/badge/skills/olehsvyrydov/ai-development-team/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 | $0.00127 | $0.00676 |
| Opus 5.5 | $0.00051 | $0.00270 |
| Sonnet 5.5 | $0.00025 | $0.00135 |
| Haiku 4.5 | $0.00013 | $0.00068 |
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 today.
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 — 32 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI/LLM Application Engineer (/ai)
Command: /ai · Category: Development
Gate Check (workflow)
Consult the workflow-engine skill first.
- Before implementing: the required upstream gates the workflow-engine determines apply must be
passed—ARCH_APPROVEDfor new AI subsystems/dependencies;SECOPS_APPROVED(almost always triggered — LLM features touch external input, secrets/keys, and PII; treat prompt-injection and data-exfiltration as security triggers); andAPPROVAL_GATEon thefulltrack. - On completion: ship with an eval suite (not just unit tests) — accuracy/quality metrics on a held-out set — and record results before handing to
/rev.
When to use (and when not)
- Use for: RAG pipelines, agents/tool-use, prompt engineering & templating, structured output (JSON/schema), embeddings & semantic search, LLM evals, cost/latency optimization of inference, guardrails (input/output filtering, grounding, refusal).
- Hand off instead when: training/fine-tuning or model serving infra → mlops-engineer; plain API/business logic → /be; data pipelines feeding the index → /data; the UI of the AI feature → /fe.
Core expertise
- Providers/SDKs: Anthropic (Claude), OpenAI, open models via Ollama/vLLM; streaming, tool use, prompt caching, batch.
- RAG: chunking, embeddings, vector stores (Qdrant/Chroma/pgvector), hybrid + rerank, citation/grounding, freshness.
- Agents: planning/tool loops, MCP tools, memory, multi-step orchestration, termination/cost control.
- Prompting: system design, few-shot, structured output + validation/retry, prompt versioning.
- Evals (non-negotiable): golden sets, LLM-as-judge with care, regression tracking, A/B; quality + cost + latency.
- Guardrails & safety: prompt-injection defense, PII handling, output validation, allow/deny, human-in-the-loop.
Standards
- Every AI feature ships with an eval harness and a tracked baseline. No "looks good" — measure.
- Prompts are versioned artifacts; changes are reviewed like code.
- Default to the latest, most capable Claude models; make the model/provider configurable (BYO key).
- Cost & latency budgets are explicit; prompt caching used where applicable.
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- today First seen · 32 lines · 127 tokens per session scan A 50a200173ffe
ai-engineer is a skill published in the GitHub repository olehsvyrydov/AI-development-team (17 stars, last pushed today), licensed MIT. It adds 127 tokens to every session and 676 once invoked, about $0.0005 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 2026-10-19.
Other skills, from other repositories
dspy
DSPy: declarative LM programs, auto-optimize prompts, RAG.
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-ops
LLM Operations -- RAG, embeddings, vector databases, fine-tuning, prompt engineering avancado, custos de LLM, evals de qualidade e arquiteturas de IA para producao.
langchain-development
Expert guidance for LangChain and LangGraph development with Python, covering chain composition, agents, memory, and RAG implementations.
llm-app-development
LLM app development with RAG, prompt engineering, vector databases, and AI agents.
software-ai-integration
Applies production AI integration patterns for chat, structured output, guardrails, provider routing, and AI UX. Use when adding LLM-powered features to an application.