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 vasilyu1983/AI-Agents-public --skill software-ai-integrationgit clone --depth 1 https://github.com/vasilyu1983/AI-Agents-publicWrote 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/vasilyu1983/ai-agents-public/software-ai-integration)<a href="https://agentmods.dev/skills/vasilyu1983/ai-agents-public/software-ai-integration"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/software-ai-integration/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/vasilyu1983/ai-agents-public/software-ai-integration"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/software-ai-integration.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 256 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Excessive Agency · line 340 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00038 | $0.06502 |
| Opus 5 | $0.00019 | $0.03251 |
| Sonnet 5 | $0.00008 | $0.01300 |
| Haiku 4.5 | $0.00004 | $0.00650 |
Grade A, and why
software-ai-integration 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 — 362 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI-Augmented Product Engineering
Integrate LLMs and AI capabilities into production applications with clean architecture, cost discipline, and reliable user experience.
Quick Reference
| Concern | Defaults |
|---|---|
| LLM API integration | Anthropic SDK, OpenAI SDK, Vercel AI SDK |
| Streaming responses | SSE / ReadableStream + AI SDK streamText/streamObject |
| Structured output | JSON mode, tool_use/function calling, Zod schemas |
| Chat interface | AI SDK useChat hook, custom streaming UI |
| AI-assisted forms | Inline suggestions, auto-complete, content generation |
| Guardrails | Input/output filtering, content moderation, PII detection |
| Cost management | Token counting, caching (semantic + exact), model routing |
| Multi-provider | AI SDK provider abstraction, Portkey, LiteLLM, or a thin internal router |
| Evaluation | Human feedback, LLM-as-judge, A/B testing AI variants |
| RAG in products | Vector search + context injection (see also ai-rag for deeper patterns) |
When to Use This Skill
- Adding AI-powered features to an existing product (chat, generation, suggestions)
- Building streaming UI for LLM responses in web or mobile applications
- Implementing structured output with schema validation from LLM calls
- Designing cost control and caching strategies for AI features
- Building multi-provider fallback and model routing logic
- Implementing guardrails, content moderation, and safety layers
- Choosing AI UX patterns (loading states, regenerate, feedback, attribution)
When NOT to Use This Skill
- LLM lifecycle management (fine-tuning, deployment, monitoring) → ai-llm
- Agent system architecture and orchestration → ai-agents
- Prompt engineering techniques and patterns → ai-prompt-engineering
- RAG system architecture (indexing, retrieval, chunking) → ai-rag
- ML model training and data science → ai-ml-data-science
- MLOps and model serving infrastructure → ai-mlops
- Building MCP servers and tool protocols → agents-mcp
What ships with it
10 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.
- agents/openai.yaml 360 B
- data/sources.json 5.0 KB
- evals/evals.json 9.1 KB
- learnings.consolidated.md 599 B
- learnings.md 399 B
- references/framework-and-gateway-patterns.md 1.2 KB
- references/product-integration-patterns.md 1.4 KB
- references/prompt-injection-and-ai-act.md 27 KB
- references/rollout-and-observability.md 2.0 KB
- scripts/check_injection_defenses.py 13 KB runs code
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 · 362 lines · 38 tokens per session scan A 5e3c14f1563f
software-ai-integration is a skill published in the GitHub repository vasilyu1983/AI-Agents-public (87 stars, last pushed 8d ago), licensed MIT. It adds 38 tokens to every session and 6,502 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 2026-09-03.
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