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AI Agents Development — Production Skill Hub · majiayu000 | DecimalAI

agent toolingby majiayu000

AI Agents Development — Production Skill Hub

majiayu000/ai-agents

Source majiayu000/claude-skill-registry ↗License MIT

Production-grade AI agent patterns with MCP integration, agentic RAG, handoff orchestration, multi-layer guardrails, and observability (modern best practices)

SkillScore86Maintained

Automatic

AI Engineer pack

Last verified 8/18/2026

What it changes

Passed 19 of 22 test cases with the skill, and 13 of 22 without it.

Without skill59%

With skill86%

Checks it passed that the model alone missed

Requires explicit tool allowlists in safety controls.
Specifies Model Context Protocol (MCP) for tool access.
Recommends Anthropic Computer Use with MCP for desktop access.

and 4 more in the full report

6 fixed13 passed either way3 still failing0 broke

Pass-rate lift: +27 pp
Tokens: +144%
Agent turns: 0%
Response time: +1%

Model gemini-3.6-flashMeasured 8/18/2026v119 of 22 cases passed with skill

Tokens, turns and time describe resource use, not a dollar price.

Use this skill

Save it into your project's skills folder. Claude Code picks it up in its next session.

curl -fsSL --create-dirs -o .claude/skills/majiayu000-ai-agents/SKILL.md https://app.decimal.ai/s/majiayu000-ai-agents/SKILL.md

Want it pinned and updatable? pip install "decimalai>=0.15", then decimalai skills add majiayu000-ai-agents, and start Claude Code with claude --plugin-dir "$(git rev-parse --show-toplevel)/.agents"

1 · One-time — install the CLI (docs ↗)

pip install "decimalai>=0.15"

2 · Get the skill — writes .agents/skills/majiayu000-ai-agents/ and pins it in .decimal/skills.lock

decimalai skills add majiayu000-ai-agents

No CLI? Get the same file — curl · raw file

curl -fsSL --create-dirs -o .claude/skills/majiayu000-ai-agents/SKILL.md https://app.decimal.ai/s/majiayu000-ai-agents/SKILL.md

Raw file: /s/majiayu000-ai-agents/SKILL.md ↗ · machine-readable summary at /s/majiayu000-ai-agents.json ↗

Safety checks: 3 of 3 passed

Code scan CleanIntent PassedContent Clean

See it in action

One of 6 test cases the skill fixed. Same model, same prompt, with and without the skill.

1 of 6

Prompt

We are building a multi-agent system where a coordinator agent delegates sub-tasks to domain-specific specialist agents and aggregates their final outputs. Please provide a formal architecture recommendation specifying the recommended control flow mechanism, the appropriate communication protocol for inter-agent handoffs versus external tool calls, and a step-by-step workflow plan.

What was checked, without and with the skill
Checked	Without skill	With skill
Recommends Agent-to-Agent Protocol (A2A) for inter-agent handoffs and delegation.	Missed	Passed
Recommends Model Context Protocol (MCP) for external tool calls.	Passed	Passed

case-01 · gemini-3.6-flash · 8/18/2026 · answers are capped and redacted

What's inside

The instructions your model reads when the skill loads.

SKILL.md366 linessingle file

AI Agents Development — Production Skill Hub

Modern Best Practices (December 2025): deterministic control flow, bounded tools, auditable state, MCP-based tool integration, handoff-first orchestration, multi-layer guardrails, OpenTelemetry tracing, and human-in-the-loop controls (OWASP LLM Top 10: https://owasp.org/www-project-top-10-for-large-language-model-applications/).

This skill provides production-ready operational patterns for designing, building, evaluating, and deploying AI agents. It centralizes procedures, checklists, decision rules, and templates used across RAG agents, tool-using agents, OS agents, and multi-agent systems.

No theory. No narrative. Only what Claude can execute.

When to Use This Skill

Claude should activate this skill whenever the user asks for:

Designing an agent (LLM-based, tool-based, OS-based, or multi-agent).
Scoping capability maturity and rollout risk for new agent behaviors.
Creating action loops, plans, workflows, or delegation logic.
Writing tool definitions, MCP tools, schemas, or validation logic.
Generating RAG pipelines, retrieval modules, or context injection.
Building memory systems (session, long-term, episodic, task).
Creating evaluation harnesses, observability plans, or safety gates.
Preparing CI/CD, rollout, deployment, or production operational specs.
Producing any template in /resources/ or /templates/.
Implementing MCP servers or integrating Model Context Protocol.
Setting up agent handoffs and orchestration patterns.
Configuring multi-layer guardrails and safety controls.
For prompt scaffolds, retrieval tuning, or security depth, see Scope Boundaries below.

Scope Boundaries (Use These Skills for Depth)

Prompt scaffolds & structured outputs → ai-prompt-engineering
RAG retrieval & chunking → ai-rag
Search tuning (BM25/HNSW/hybrid) → ai-rag
Security/guardrails → ai-mlops
Inference optimization → ai-llm-inference

Quick Reference

Track record

Benchmark

19 / 22

test cases passed, vs 13 / 22 without

Adoption

Users: 0
Times used: 0

Safety and source details

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/3/2026

View 15 scanner checks

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

majiayu000 / claude-skill-registry

skills/agent/ai-agents/SKILL.md

View on GitHub ↗

License MITImported 8/3/2026Version v1

More skills from the same source and nearby in the registry, with their headline benchmark lift.

Ready to supercharge your AI agents?

See it in action

What's inside

AI Agents Development — Production Skill Hub

When to Use This Skill

Scope Boundaries (Use These Skills for Depth)

Quick Reference

Track record

Benchmark

Adoption

SkillSafety

Source

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