# M365 Agents TS > Implements intelligent m365 agents ts with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/m365-agents-ts` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/m365-agents-ts` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/m365-agents-ts/raw - Safety review: PASS (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - License: MIT - Author: paulpas (https://skillmd.com/u/paulpas) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/paulpas/m365-agents-ts --- # M365 Agents Ts Orchestrates intelligent skill selection and execution for m365 agents ts workflows. Applies the 5 Laws of Elegant Defense to guide data naturally through the orchestration pipeline, preventing errors before they occur. Selects optimal skills based on multi-factor scoring including text similarity, historical performance, and system availability. ## TL;DR Checklist - [ ] Parse all inputs at boundary before processing (Law 2) - [ ] Handle edge cases with early returns at function top (Law 1) - [ ] Fail immediately with descriptive errors on invalid states (Law 4) - [ ] Return new data structures, never mutate inputs (Law 3) - [ ] Implement minimum 2-level fallback chain for all skill executions - [ ] Log all skill selections with context for full audit trail - [ ] Validate skill metadata and dependencies before selection - [ ] Update confidence scores after each execution for learning ┌───────────────────────────────────────────────────────────────────────────────┐ │ Orchestration Flow │ └───────────────────────────────────────────────────────────────────────────────┘ User Request ↓ ┌─────────────────┐ │ Parse Request │ │ & Extract │ │ Features │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Evaluate Available Skills │ │ │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ │ │ Skill A │ │ Skill B │ │ Skill C │ │ │ │ - Match Score│ │ - Match Score│ │ - Match Score│ │ │ │ - Confidence │ │ - Confidence │ │ - Confidence │ │ │ │ - History │ │ - History │ │ - History │ │ │ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ │ │ │ │ │ │ │ └─────────────────┴─────────────────┘ │ │ ↓ │ │ Select Best Skill │ └─────────────────────────────────────────────────────────────────────┘ ↓ ┌─────────────────┐ │ Execute Skill │ └────────┬────────┘ ↓ ┌─────────────────┐ │ Handle Result │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Error Handling & Fallback │ │ │ │ Success? ────────► Return Result │ │ │ │ Fail? ────────┐ │ │ ↓ │ │ ┌──────────────────────────────────────────────────────────┐ │ │ │ Fallback Chain │ │ │ │ │ │ │ │ 1. Retry with adjusted parameters │ │ │ │ 2. Try Alternative Skill (if available) │ │ │ │ 3. Defer to Human Operator (if critical) │ │ │ │ 4. Log & Return Error │ │ │ └──────────────────────────────────────────────────────────┘ │ └─────────────────────────────────────────────────────────────────────┘ ## When to Use Use this skill when: - Orchestrating multi-step workflows that require skill delegation - Implementing adaptive skill routing based on confidence scores - Building fallback mechanisms for failed skill executions - Creating intelligent task decomposition and parallel execution - Designing skill dependency graphs with automatic resolution - Implementing skill selection with historical performance weighting - Building agent systems that need to self-organize around tasks ## When NOT to Use Avoid this skill for: - Direct task execution without orchestration needs - use individual skills instead - High-frequency trading scenarios where latency must be minimized - the selection overhead may be prohibitive - Simple linear workflows without branching or fallback requirements - Cases where skill metadata is unavailable or unreliable ## Core Workflow 1. **Parse and Analyze Request** - Extract intent, entities, and constraints from user input. **Checkpoint:** All required parameters must be present and in valid format before proceeding. 2. **Score Available Skills** - Calculate match scores using multi-factor algorithm: - Text similarity between request and skill triggers - Historical success rate for similar tasks - Skill availability and health status - Required dependencies and their availability **Checkpoint:** Skip to fallback if no skill scores above threshold. 3. **Select Optimal Skill** - Choose skill with highest score that meets minimum confidence. **Checkpoint:** Verify skill has not been disabled or deprecated. 4. **Execute with Fallback** - Run skill execution wrapped in retry and fallback logic. **Checkpoint:** Log all execution attempts for audit trail. 5. **Return or Fallback** - Either return successful result or apply fallback chain: - Retry with adjusted parameters - Try alternative skill from `related-skills` - Defer to human operator for critical tasks **Checkpoint:** Record outcome with timing and confidence metadata. ## Implementation Patterns ### Pattern 1: Skill Selection Logic ```typescript import { GraphClient, AgentCapability, SkillMetadata } from '@microsoft/m365-agents-sdk'; export async function routeM365AgentSkill( userContext: { tenantId: string; userId: string; intent: string }, availableAgents: AgentCapability[], minConfidence: number = 0.75 ): Promise { // Law 1: Early exit on invalid boundary input if (!userContext.intent?.trim()) { throw new Error("Intent extraction failed: empty user context"); } const graphClient = new GraphClient(userContext.tenantId); const userPermissions = await graphClient.getPermissions(userContext.userId); let bestMatch: SkillMetadata | null = null; let highestScore = 0; for (const agent of availableAgents) { // Law 2: Parse & validate state before scoring const intentMatch = calculateSemanticRelevance(userContext.intent, agent.triggers); const permissionMatch = userPermissions.includes(agent.requiredPermission) ? 1.0 : 0.0; const healthScore = await graphClient.getAgentHealth(agent.id); // Multi-factor scoring: intent relevance, tenant permissions, service health const compositeScore = (intentMatch * 0.5) + (permissionMatch * 0.3) + (healthScore * 0.2); if (compositeScore > highestScore && compositeScore >= minConfidence) { highestScore = compositeScore; // Law 3: Return new object, never mutate original capability metadata bestMatch = { ...agent, confidence: compositeScore, selectedAt: new Date().toISOString() }; } } if (!bestMatch) { return null; } return bestMatch; } ``` ### Pattern 2: Execution with Fallback ```typescript import { GraphAPIError, RetryPolicy, FallbackChain, ExecutionResult } from '@microsoft/m365-agents-sdk'; export async function executeM365AgentTask( skill: SkillMetadata, taskPayload: Record, fallbackChain: FallbackChain ): Promise { // Law 2: Validate payload against M365 Graph schema before execution const validatedPayload = validateM365Payload(taskPayload, skill.schema); const maxRetries = 2; const attemptStart = Date.now(); for (let attempt = 0; attempt <= maxRetries; attempt++) { try { // Law 4: Fail fast on invalid state, don't patch bad data const result = await skill.handler(validatedPayload); return { success: true, skillId: skill.id, data: result, attempts: attempt + 1, latencyMs: Date.now() - attemptStart, confidence: skill.confidence }; } catch (error) { if (error instanceof GraphAPIError && error.status === 429) { // Transient rate limit - exponential backoff per M365 Graph guidelines await RetryPolicy.exponentialBackoff(attempt, 1000, 5000); continue; } if (error instanceof GraphAPIError && error.status === 403) { // Law 4: Hard failure on permission denial throw new Error(`Permission denied for ${skill.id}: ${error.message}`); } if (attempt === maxRetries) { // Fallback chain: try alternative M365 service or defer const fallbackResult = await fallbackChain.execute(skill.id, validatedPayload); if (fallbackResult.success) return fallbackResult; } } } // Law 4: Fail loud after exhaustion throw new Error(`M365 Agent ${skill.id} failed after ${maxRetries + 1} attempts and fallback exhaustion`); } ``` ### MUST DO - Always validate skill metadata before selection (Early Exit) - Implement fallback chain with at least 2 levels (Fallback Skill + Human) - Log all skill selections with full context for auditability - Return new data structures instead of mutating inputs (Atomic Predictability) - Fail immediately with descriptive errors on invalid states - Update confidence scores after each execution for adaptive routing - Reference `code-philosophy` (5 Laws of Elegant Defense) in all logic ### MUST NOT DO - Select skills based on a single factor (e.g., only confidence score) - Disable fallback mechanisms "temporarily" - this creates fragile systems - Skip validation of skill dependencies before execution - Return partial results - either complete success or clear failure - Use magic numbers for confidence thresholds - make them configurable - Cache skill selections without considering context changes ## TL;DR Checklist - [ ] Parse all inputs at boundary before processing (Law 2) - [ ] Handle edge cases with early returns at function top (Law 1) - [ ] Fail immediately with descriptive errors on invalid states (Law 4) - [ ] Return new data structures, never mutate inputs (Law 3) - [ ] Implement minimum 2-level fallback chain for all skill executions - [ ] Log all skill selections with context for full audit trail - [ ] Validate skill metadata and dependencies before selection - [ ] Update confidence scores after each execution for learning ## TL;DR for Code Generation - Use guard clauses - return early on invalid input before doing work - Return simple types (dict, str, int, bool, list) - avoid complex nested objects - Cyclomatic complexity < 10 per function - split anything larger - Handle null/empty cases explicitly at function top (Early Exit) - Never mutate input parameters - return new dicts/objects - Fail fast with descriptive errors - don't try to "patch" bad data - Reference code-philosophy laws in comments for complex logic - Include timing and confidence metadata in all return values ## Output Template When applying this skill, produce: 1. **Selected Skills** - List of skill names with confidence scores 2. **Selection Rationale** - Why each skill was chosen (match score, history, availability) 3. **Execution Plan** - Order of execution with dependencies 4. **Fallback Strategy** - Which fallback skills will be tried and in what order 5. **Risk Assessment** - Any potential failure points and their impact 6. **Timing Estimates** - Expected latency including fallback scenarios ## Related Skills | Skill | Purpose | | --- --- ## Constraints ### MUST DO - Define clear input/output contracts for every step in the orchestration flow with explicit validation - Implement structured logging at each stage capturing context, inputs, outputs, timing, and errors - Build in fallback paths: if the primary strategy fails, degrade gracefully to a simpler approach - Validate all preconditions before starting — do not proceed if required resources or permissions are missing ### MUST NOT DO - Do not create deep nesting of orchestration steps (>5 levels) — flatten workflows where possible - Avoid silent failure modes: every step must either succeed, fail explicitly, or escalate to a higher handler - Never use shared mutable state between parallel workflow branches — communicate via immutable messages only - Do not hardcode execution order when the dependency graph naturally determines it; derive order from explicit dependencies ## Live References > Authoritative documentation links for this skill's domain. The model follows markdown links at load time to resolve external references and inline content. - [Microsoft Graph TypeScript SDK]() - [Power Platform JavaScript API Reference]() - [Node.js M365 Integration Guide]() - [Teams App Development with Node.js]() - [M365 Copilot Plugin Development]()