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 | $0.04958 |
| Opus 5.5 | $0.00015 | $0.01983 |
| Sonnet 5.5 | $0.00008 | $0.00992 |
| Haiku 4.5 | $0.00004 | $0.00496 |
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 20d 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 — 247 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI-Augmented Product Engineering
Quick Reference
| Concern | Defaults |
|---|---|
| LLM API integration | Vercel AI SDK by default; use a direct provider SDK when its capabilities or smaller abstraction fit better (see Integration and Gateway Choice) |
| Streaming responses | SSE / ReadableStream from the SDK's server streaming call; streaming mode matched to guardrail risk (see Streaming Architecture) |
| Structured output | Schema-constrained output or tool calling, one schema definition (e.g. Zod) validated in code; contract rules in ai-prompt-engineering |
| Chat interface | The SDK's chat UI hook, or a 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 | Thin internal router for one product; consider a Portkey-class gateway for shared routing or policy (see Integration and Gateway Choice) |
| 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 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
When NOT to Add AI At All
Not every "AI feature" request should become one. Apply this filter before the decision tree below:
What ships with it
11 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 359 B
- data/sources.json 5.5 KB
- evals/evals.json 10 KB
- learnings.consolidated.md 599 B
- learnings.md 814 B
- references/integration-scenarios.md 4.7 KB
- references/product-integration-patterns.md 5.1 KB
- references/prompt-injection-and-ai-act.md 17 KB
- references/rollout-and-observability.md 2.2 KB
- scripts/check_injection_defenses.py 14 KB runs code
- scripts/test_check_injection_defenses.py 1.4 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.
- 20d ago Changed · -115 lines eb9c21c791f6
- 1mo ago Changed d4354d448909
- 1mo 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 (101 stars, last pushed 10d ago), licensed MIT. It adds 38 tokens to every session and 4,958 once invoked, about $0.0002 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 2026-09-03.
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.
cortex-prompt
Build a production-ready prompt package — system prompt, few-shot examples, output format, edge case handling, eval criteria. Use when asked to "prompt engineering", "build a prompt", "write a system prompt", or "improve this prompt".