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 hajekim/agentic-design-patterns-extension --skill a2agit clone --depth 1 https://github.com/hajekim/agentic-design-patterns-extensionWrote 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/hajekim/agentic-design-patterns-extension/a2a)<a href="https://agentmods.dev/skills/hajekim/agentic-design-patterns-extension/a2a"><img src="https://agentmods.dev/badge/skills/hajekim/agentic-design-patterns-extension/a2a.svg" alt="Measured on agentmods" 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 | $0.00435 | $0.03972 |
| Opus 5 | $0.00217 | $0.01986 |
| Sonnet 5 | $0.00087 | $0.00794 |
| Haiku 4.5 | $0.00044 | $0.00397 |
Grade A, and why
a2a scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
response = requests.get(f"{self.agent_url}/.well-known/agent.json", timeout=10) This is a copy
100% identical to a2a — 3 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 440 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent-to-Agent (A2A) Communication Pattern
Overview
The Agent-to-Agent (A2A) Communication Pattern enables autonomous agents to discover, communicate with, and delegate tasks to other agents using standardized protocols. Just as MCP standardizes tool integration, A2A standardizes how agents interact with each other — enabling federated, interoperable multi-agent systems across organizational and technical boundaries.
Core Principle: Agents should be first-class collaborators, not just tool callers — standardize how agents discover and communicate with each other.
When This Skill Applies
Activate this pattern when:
- Multiple specialized agents need to collaborate across service boundaries
- Agents from different organizations or teams must interoperate
- Dynamic agent discovery is needed (don't hardcode agent endpoints)
- Complex tasks require delegating sub-tasks to expert agents
- You want to compose existing agents into new workflows without rewriting them
- Agent capabilities need to be advertised and discoverable programmatically
Rule of thumb: Use A2A when your multi-agent system spans organizational boundaries, requires dynamic discovery, or must be interoperable with third-party agents.
A2A Architecture
Key Concepts
- Agent Card: Machine-readable capability advertisement (what the agent can do)
- Task: The unit of work delegated between agents
- Artifact: Output produced by an agent for the requesting agent
- Discovery: Finding agents by capability, not by hardcoded URL
- Trust: Authentication and authorization between agents
A2A Communication Flow
Agent A (Client) Agent B (Server)
│ │
│──── Discover Agent Cards ──────────────►│
│◄─── Agent Card (capabilities) ──────────│
│ │
│──── Send Task (with context) ──────────►│
│◄─── Task Acknowledgment ────────────────│
│ │
│──── Poll for status ───────────────────►│
│◄─── Status Update ──────────────────────│
│ │
│──── Get Result ────────────────────────►│
│◄─── Artifact (result) ──────────────────│
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 · 440 lines · 435 tokens per session scan A 98a9ff9dc5db
a2a is a skill published in the GitHub repository hajekim/agentic-design-patterns-extension (1 stars, last pushed 5mo ago), licensed MIT. It adds 435 tokens to every session and 3,972 once invoked, about $0.0022 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 100% identical to a2a, differing in 3 lines, and is treated as a copy.
Other skills, from other repositories
prompt engineering
Use this skill when asked to create, refine, analyze, or optimize prompts for Large Language Models (LLMs). This skill ensures adherence to prompt engineering best practices and enforces a rigorous design workflow.
redteam
Expertise in offensive security research, vulnerability analysis, CMS-focused application testing, and red team operations.
redteam-source-audit
Focused source-code security review workflow for web applications, CMS extensions, APIs, and supporting services.
redteam-cms
Focused methodology for authorized CMS fingerprinting, component inventory, misconfiguration review, and vulnerability validation.
redteam-exploit-validation
Focused workflow for validating exploitability safely and turning candidate issues into reproducible, bounded proof.
redteam-recon
Focused reconnaissance workflow for authorized security assessments, bug bounty triage, lab targets, and CTF infrastructure.