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 ai-agentsgit 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/ai-agents)<a href="https://agentmods.dev/skills/vasilyu1983/ai-agents-public/ai-agents"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/ai-agents/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/ai-agents"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/ai-agents.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00032 | $0.05209 |
| Opus 5 | $0.00016 | $0.02604 |
| Sonnet 5 | $0.00006 | $0.01042 |
| Haiku 4.5 | $0.00003 | $0.00521 |
Grade A, and why
ai-agents 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 11d 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 — 310 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Agents Development — Architecture Hub
Use this skill to decide whether a workflow should be an agent, which agent shape fits, which protocol boundary to use, and what production controls must exist before rollout.
Default posture: explicit control flow, bounded tools, typed contracts, auditable state, human approval for high-risk actions, and telemetry from day one.
Keep this file lean. Load detail from references/index.md, assets/, and sibling skills only when needed.
When to Use This Skill
Use this skill when the user asks for:
- agent architecture or operating-model decisions
- build-vs-not-agent assessment
- MCP vs A2A protocol choice
- production readiness review for an existing agent system
- evaluation, observability, rollout, or safety planning
- framework selection after requirements are already clear
- a starting template for a new agent spec
- graph engineering, agent/workflow graphs, state-machine orchestration, cyclic graphs, or DAG-versus-loop design
- loop engineering, run-until-done coding agents, self-improving workflows, evaluator feedback loops, or bounded autonomous iteration
- uncertainty over whether a "graph" means execution control flow, an improvement network, or a knowledge/context graph
Use Other Skills for Depth
- Prompt contracts and structured outputs →
../ai-prompt-engineering/SKILL.md - Retrieval, chunking, reranking, search quality →
../ai-rag/SKILL.md - Vector-brain implementation, schemas, ingest scripts, manifests, and retrieval tool contracts →
../ai-vector-brain/SKILL.md - Bot building (support, sales, conversation design, LangGraph) →
ai-bot-builder - Voice bots (STT/TTS pipeline, telephony, latency) →
../ai-voice-bots/SKILL.md - MCP server setup, transports, server builds →
../agents-mcp/SKILL.md - Subagents, delegation contracts, least-privilege tools →
agents-subagents - CLI-based tools (non-interactive, idempotent, agent-friendly patterns) →
../software-devtools/SKILL.md - Evaluation harnesses, attack suites, regression gates →
../qa-agent-testing/SKILL.md - Deployment guardrails and model operations →
../ai-mlops/SKILL.md - Application security and high-risk controls →
../software-security-appsec/SKILL.md - Model and inference cost tuning →
../ai-llm/SKILL.md,../ai-llm-inference/SKILL.md - Knowledge/context graphs, retrieval architecture, and graph-backed memory →
ai-context-layer,../ai-rag/SKILL.md,../ai-vector-brain/SKILL.md
What ships with it
60 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.
- _lib/resolve_versions.py 12 KB runs code
- agents/openai.yaml 372 B
- assets/agent-template-ainative-sdlc.md 2.3 KB
- assets/checklists/agent-safety-checklist.md 5.2 KB
- assets/core/agent-template-quick.md 3.5 KB
- assets/core/agent-template-specialized.md 8.9 KB
- assets/core/agent-template-standard.md 9.9 KB
- assets/knowledge-base/kb-architecture.md 19 KB
- assets/multi-agent/evaluator-router-template.md 6.7 KB
- assets/multi-agent/manager-worker-template.md 6.0 KB
- assets/rag/hybrid-retrieval.md 3.9 KB
- assets/rag/rag-advanced.md 5.7 KB
- assets/rag/rag-basic.md 3.0 KB
- assets/tools/tool-definition.md 3.7 KB
- assets/tools/tool-validation-checklist.md 3.6 KB
- data/model-pricing.json 6.7 KB
- data/sources.json 24 KB
- learnings.consolidated.md 585 B
- learnings.md 863 B
- references/24-7-operating-model.md 15 KB
- references/a2a-handoff-patterns.md 23 KB
- references/a2a-mcp-integration-patterns.md 4.5 KB
- references/agent-debugging-patterns.md 15 KB
- references/agent-delivery-methods.md 15 KB
- references/agent-economics.md 12 KB
- references/agent-maturity-governance.md 9.3 KB
- references/agent-operations-best-practices.md 8.6 KB
- references/ai-engine-layers.md 18 KB
- references/api-contracts-for-agents.md 2.0 KB
- references/autonomous-loop-patterns.md 20 KB
- references/build-vs-not-decision.md 12 KB
- references/claude-agent-sdk-patterns.md 11 KB
- references/code-swe-agents.md 14 KB
- references/coding-agent-usage-tracking.md 22 KB
- references/context-engineering.md 9.5 KB
- references/context-graph-patterns.md 14 KB
- references/context-rotation-and-state.md 4.0 KB
- references/deployment-ci-cd-and-safety.md 13 KB
- references/escalation-patterns.md 7.8 KB
- references/evaluation-and-observability.md 13 KB
- references/framework-landscape.md 15 KB
- references/game-theory-multi-agent-systems.md 15 KB
- references/graph-and-loop-engineering.md 6.3 KB
- references/guardrails-implementation.md 16 KB
- references/inbox-engine-patterns.md 18 KB
- references/index.md 8.5 KB
- references/mcp-practical-guide.md 18 KB
- references/mcp-server-builder.md 1.4 KB
- references/memory-systems.md 5.6 KB
- references/modern-best-practices.md 17 KB
- references/multi-agent-patterns.md 14 KB
- references/ooda-loop-agent-architecture.md 12 KB
- references/operational-patterns.md 11 KB
- references/os-agent-capabilities.md 8.9 KB
- references/principal-agent-theory.md 12 KB
- references/protocol-decision-tree.md 18 KB
- references/pydantic-ai-patterns.md 14 KB
- references/rag-patterns.md 11 KB
- references/skill-lifecycle.md 938 B
- references/tool-design-specs.md 14 KB
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.
- 11d ago First seen · 310 lines · 32 tokens per session scan A eec89614ffc9
ai-agents is a skill published in the GitHub repository vasilyu1983/AI-Agents-public (87 stars, last pushed 9d ago), licensed MIT. It adds 32 tokens to every session and 5,209 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 2026-08-30.
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