القائمة
AI agent architecture, tool use, memory systems, multi-agent orchestration, and safety patterns
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
استنادا إلى تصنيف SOC المهني
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/InugamiDev/ultrathink-oss --skill ai-agentsيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
عرض SKILL.md
Unified design foundations — design system architecture, tokens, component specs, visual principles, creative vision, figma integration, plus brand design system loader (66 real brands via DESIGN.md). Absorbs design, design-system, design-systems, design-principles, design-router, creative-vision, figma, design-md.
Render, summarize, and present markdown documents and structured content in multiple output modes
Ultra UI skill - combines Google's DESIGN.md spec (machine-readable design tokens) with the ui-ux-pro-max knowledge base (91 styles, 161 palettes, 73 font pairings, 161 products, 104 UX guidelines, 25 chart types). Generates lint-clean DESIGN.md files, validates token references and WCAG contrast, exports Tailwind/DTCG tokens, and diffs design systems version-over-version.
| name | ai-agents |
| description | AI agent architecture, tool use, memory systems, multi-agent orchestration, and safety patterns |
| layer | domain |
| category | ai-ml |
| triggers | ["ai agent","agent architecture","tool use","agent memory","multi-agent","agent orchestration","agentic workflow","ReAct pattern"] |
| inputs | [{"objective":"What the agent should accomplish"},{"tools":"Available tools and APIs the agent can use"},{"constraints":"Safety boundaries, cost limits, human-in-the-loop requirements"},{"architecture":"Single agent | multi-agent | hierarchical"}] |
| outputs | [{"agent_design":"Agent architecture with tool definitions and memory strategy"},{"tool_definitions":"Tool schemas with input/output specifications"},{"orchestration_logic":"How multiple agents coordinate"},{"safety_guardrails":"Input/output validation and boundary enforcement"}] |
| linksTo | ["prompt-engineering","rag","error-handling","logging"] |
| linkedFrom | ["cook","plan","research"] |
| preferredNextSkills | ["prompt-engineering","rag"] |
| fallbackSkills | ["sequential-thinking"] |
| riskLevel | medium |
| memoryReadPolicy | selective |
| memoryWritePolicy | selective |
| sideEffects | ["Agents may execute tools with real-world effects","Agents may make API calls","Agents consume LLM tokens (cost)"] |
Design and implement AI agent systems that can reason, use tools, maintain memory, and coordinate with other agents. This skill covers the ReAct pattern, tool definition, memory architectures (short-term, long-term, episodic), multi-agent orchestration, and safety guardrails. Agents are not just chatbots -- they are autonomous systems that take actions in the world.
PERCEPTION:
Input parsing, context extraction, intent classification
REASONING:
Chain-of-thought, planning, self-reflection, error correction
ACTION:
Tool selection, parameter construction, execution
MEMORY:
Working memory (current conversation)
Short-term memory (recent interactions, scratch pad)
Long-term memory (persistent knowledge, embeddings)
Episodic memory (past task execution records)
LOOP:
Observe -> Think -> Act -> Observe -> Think -> Act -> ... -> Done
ReAct (Reason + Act):
Thought: I need to find the user's order status.
Action: query_database(order_id="ord_123")
Observation: Order status is "shipped", tracking: "1Z999AA..."
Thought: I have the information. I'll respond to the user.
Answer: Your order has been shipped! Tracking: 1Z999AA...
Plan-and-Execute:
Plan: [Step 1: Search products, Step 2: Compare prices, Step 3: Recommend]
Execute each step, revise plan if needed
Reflection:
After completing a task, evaluate quality and retry if insufficient
Multi-Agent:
Researcher agent -> Analyst agent -> Writer agent -> Reviewer agent
Each agent specialized for one part of the workflow
// Tools are typed schemas that the LLM can invoke
interface ToolDefinition {
name: string;
description: string;
parameters: {
type: 'object';
properties: Record<string, { type: string; description: string; enum?: string[] }>;
required: string[];
};
}
: [] = [
{
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: { : , : },
: { : , : , : [, , ] },
: { : , : },
},
: [],
},
},
{
: ,
: ,
: {
: ,
: {
: { : , : },
},
: [],
},
},
];
import Anthropic from '@anthropic-ai/sdk';
const client = new Anthropic();
async function runAgent(userMessage: string, tools: ToolDefinition[]) {
const messages: Anthropic.MessageParam[] = [
{ role: 'user', content: userMessage },
];
const MAX_ITERATIONS = 10;
for (let i = 0; i < MAX_ITERATIONS; i++) {
const response = await client.messages.create({
model: 'claude-sonnet-4-20250514',
max_tokens: 4096,
system: 'You are a helpful assistant. Use tools when needed to answer questions.',
tools,
messages,
});
// Check if the model wants to use a tool
if (response.stop_reason === 'tool_use') {
const toolUseBlocks = response.content.filter(
(block) => block.type === 'tool_use'
);
// Add assistant response to conversation
messages.push({ role: 'assistant', content: response.content });
// Execute each tool call
const toolResults = [];
for (const toolUse of toolUseBlocks) {
const result = await executeTool(toolUse.name, toolUse.input);
toolResults.push({
type: 'tool_result' as const,
tool_use_id: toolUse.id,
content: JSON.stringify(result),
});
}
messages.push({ role: 'user', content: toolResults });
} else {
// Model is done, return the final response
return response.content;
}
}
throw new Error('Agent exceeded maximum iterations');
}
async function executeTool(name: string, input: Record<string, unknown>) {
switch (name) {
case 'search_products':
return await searchProducts(input.query as string, input);
case 'get_order_status':
return await getOrderStatus(input.orderId as string);
default:
throw new Error(`Unknown tool: ${name}`);
}
}
interface Memory {
// Working memory: current conversation context
conversation: Message[];
// Short-term memory: recent scratchpad
scratchpad: Map<string, unknown>;
// Long-term memory: persistent knowledge (vector store)
retrieve(query: string, k?: number): Promise<MemoryEntry[]>;
store(content: string, metadata?: Record<string, unknown>): Promise<void>;
// Episodic memory: past task records
getEpisode(taskId: string): Promise<Episode | null>;
recordEpisode(episode: Episode): Promise<void>;
}
interface Episode {
taskId: string;
objective: string;
steps: Array<{ thought: string; action: string; result: string }>;
outcome: 'success' | 'failure';
reflection: string;
timestamp: Date;
}
// Before starting a new task, check if we have done something similar
async function planWithMemory(objective: string, memory: Memory) {
const similarEpisodes = await memory.retrieve(objective, 3);
const relevantKnowledge = await memory.retrieve(objective, 5);
const context = `
Similar past tasks:
${similarEpisodes.map(e => `- ${e.content} (${e.metadata?.outcome})`).join('\n')}
Relevant knowledge:
${relevantKnowledge.map(k => `- ${k.content}`).join('\n')}
`;
return context;
}
interface Agent {
name: string;
systemPrompt: string;
tools: ToolDefinition[];
run(input: string): Promise<string>;
}
// Sequential pipeline
async function researchPipeline(question: string) {
const researcher = createAgent('researcher', researcherPrompt, [webSearch, readDoc]);
const analyst = createAgent('analyst', analystPrompt, [calculateMetrics]);
const writer = createAgent('writer', writerPrompt, [formatReport]);
const rawFindings = await researcher.run(question);
const analysis = await analyst.run(`Analyze these findings:\n${rawFindings}`);
const report = await writer.run(`Write a report from:\n${analysis}`);
return report;
}
// Supervisor pattern
async function supervisorLoop(objective: string, agents: Agent[]) {
const supervisor = createAgent('supervisor', supervisorPrompt, [
{ name: 'delegate', description: 'Assign a task to an agent', parameters: { ... } },
{ name: 'complete', description: 'Mark the objective as done', parameters: { ... } },
]);
let result = '';
for (let i = 0; i < 10; i++) {
const action = await supervisor.run(`Objective: ${objective}\nProgress: ${result}`);
if (action.tool === 'complete') return action.output;
const agent = agents.find(a => a.name === action.delegateTo);
result += await agent!.run(action.task);
}
}
INPUT VALIDATION:
- Sanitize user input before passing to LLM
- Validate tool parameters before execution
- Reject prompt injection attempts
OUTPUT VALIDATION:
- Verify tool outputs are within expected bounds
- Filter sensitive information from responses
- Check for hallucinated tool calls
EXECUTION BOUNDARIES:
- Maximum iterations per agent run
- Token budget per task
- Allowlist of permitted tools
- Human-in-the-loop for destructive actions
- Rate limiting on expensive operations
MONITORING:
- Log all tool invocations with inputs and outputs
- Track token usage and cost per agent run
- Alert on unusual patterns (loops, excessive tool calls)
| Pitfall | Impact | Fix |
|---|---|---|
| No iteration limit | Infinite loop, cost explosion | Cap at 10-20 iterations |
| Vague tool descriptions | LLM calls wrong tool or wrong params | Write precise, example-rich descriptions |
| No human-in-the-loop | Agent takes irreversible destructive actions | Require confirmation for high-risk tools |
| Stuffing all context in prompt | Token limit exceeded, degraded quality | Use RAG for dynamic context |
| Not logging tool calls | Cannot debug agent failures | Log every tool invocation |
| Trusting LLM output as code | Injection, errors, hallucinations | Validate and sandbox all LLM-generated code |