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 jnPiyush/AgentX --skill ai-agent-developmentgit clone --depth 1 https://github.com/jnPiyush/AgentXWrote 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/jnpiyush/agentx/ai-agent-development)<a href="https://agentmods.dev/skills/jnpiyush/agentx/ai-agent-development"><img src="https://agentmods.dev/badge/skills/jnpiyush/agentx/ai-agent-development/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/jnpiyush/agentx/ai-agent-development"><img src="https://agentmods.dev/badge/skills/jnpiyush/agentx/ai-agent-development.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 | $0.00059 | $0.03033 |
| Opus 5.5 | $0.00024 | $0.01213 |
| Sonnet 5.5 | $0.00012 | $0.00607 |
| Haiku 4.5 | $0.00006 | $0.00303 |
Grade A, and why
ai-agent-development 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 15d 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 — 351 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Agent Development
Purpose: Build production-ready AI agents with Microsoft Foundry and Agent Framework. Scope: Agent architecture, model selection, orchestration, observability, evaluation.
When to Use This Skill
- Building AI agents with Microsoft Foundry or Agent Framework
- Selecting LLM models for agent scenarios
- Implementing multi-agent orchestration workflows
- Adding tracing and observability to AI agents
- Evaluating agent quality and response accuracy
Decision Tree
Need an AI agent?
+-- Simple request-response? -> Single agent with tools
+-- Multi-step reasoning? -> Reasoning model (tuned effort) + planner
+-- Multiple specialized domains? -> Multi-agent orchestration
+-- Human approval needed? -> Human-in-the-loop workflow
+-- High reliability required? -> Reflection + self-correction loop
+-- Real-time streaming? -> Async event-driven agent
Prerequisites
- A runtime version supported by the target repository
- A current stable Agent Framework SDK version verified against official docs
- Microsoft Foundry workspace with deployed model
Quick Start
Installation
Resolve current SDK package names and stable versions from the official Agent Framework documentation at implementation time. Pin the selected package version in the target repository lock file. Do not copy preview flags or version numbers from this skill into production setup.
Model Selection
Select a Capability Class before selecting a concrete provider model:
| Capability Class | Use When | Required Evidence |
|---|---|---|
| Fast | Classification, extraction, or short tool turns | Meets latency and minimum quality thresholds |
| Balanced | General agent work with moderate reasoning | Best quality/cost result on the representative eval set |
| Deep reasoning | Architecture, hard debugging, or complex planning | Material measured gain over Balanced justifies latency and cost |
| Coding agent | Long-running repository edits and test loops | Tool accuracy, patch quality, and completion rate meet thresholds |
| Multimodal | Screenshots, diagrams, audio, or video are required inputs | Target modalities and formats are verified in the active host |
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.
- references/evaluation-guide.md 5.8 KB
- references/model-change-test-automation.md 20 KB
- references/model-drift-judge-patterns.md 13 KB
- references/multi-model-patterns.md 2.4 KB
- references/orchestration-patterns.md 8.1 KB
- references/tracing-and-evaluation.md 3.9 KB
- scripts/check-model-drift.ps1 14 KB runs code
- scripts/run-model-comparison.py 20 KB runs code
- scripts/scaffold-agent.py 17 KB runs code
- scripts/validate-agent-checklist.ps1 10 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.
- 15d ago Changed 82e327383339
- 1mo ago Changed · -1 lines 4dc0d402f6e3
- 1mo ago First seen · 352 lines · 59 tokens per session scan A 16b825b0484e
ai-agent-development is a skill published in the GitHub repository jnPiyush/AgentX (17 stars, last pushed 2d ago), licensed Apache-2.0. It adds 59 tokens to every session and 3,033 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 2026-08-30.
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