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 j4flmao/agent-skills --skill ai-agentsgit clone --depth 1 https://github.com/j4flmao/agent-skillsWrote 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/j4flmao/agent-skills/ai-agents)<a href="https://agentmods.dev/skills/j4flmao/agent-skills/ai-agents"><img src="https://agentmods.dev/badge/skills/j4flmao/agent-skills/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/j4flmao/agent-skills/ai-agents"><img src="https://agentmods.dev/badge/skills/j4flmao/agent-skills/ai-agents.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 | $0.00000 | $0.00354 |
| Opus 5 | $0.00000 | $0.00177 |
| Sonnet 5 | $0.00000 | $0.00071 |
| Haiku 4.5 | $0.00000 | $0.00035 |
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 10d 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.
What it actually says
AI Agent Architectures
1. Skill Context
Focus: Designing, evaluating, and implementing Autonomous AI Agents, ReAct loops, planning, and tool use. Triggers: agent architecture, multi-agent systems, react loop, tool calling, autonomous agent
2. Advanced Technical Patterns
The agent acts as an AI Architect, specializing in agentic workflows beyond simple RAG or zero-shot generation.
ReAct (Reason + Act) Loop
- Mechanics: The model is prompted to output a "Thought" (reasoning) followed by an "Action" (tool call). It then receives an "Observation" (tool result) and continues.
- Optimization: Forcing strict JSON output schemas for tool calls to prevent parsing errors. Pre-filling the assistant message to guide the thought process.
Multi-Agent Orchestration
- Hierarchical: A Router/Manager agent analyzes the task and delegates sub-tasks to specialized worker agents (e.g., Code Writer, Code Reviewer).
- Sequential (Chain): Agent A's output becomes Agent B's input.
- Debate/Consensus: Two agents generate different solutions and a third agent acts as a judge to combine the best parts.
Memory Structures
- Short-term Memory: The immediate context window (chat history). Often requires summarization when approaching token limits.
- Long-term Memory: Semantic search over past interactions (Vector DBs) or updating a structured user profile (Entity-based memory).
3. Output Format
- Provide the system prompt architecture.
- Explain the tool-calling schema (OpenAI format or Anthropic format).
- Use Mermaid sequence diagrams to map out the agent workflow.
What ships with it
5 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.
- 10d ago First seen · 27 lines · 0 tokens per session scan A 9a1184e9cccb
ai-agents is a skill published in the GitHub repository j4flmao/agent-skills (22 stars, last pushed 3d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 354 tokens. 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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…