# M365 Agents Dotnet > Implements intelligent m365 agents dotnet with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/m365-agents-dotnet` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/m365-agents-dotnet` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/m365-agents-dotnet/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-dotnet --- # M365 Agents Dotnet Orchestrates intelligent skill selection and execution for m365 agents dotnet 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 ```csharp public class M365AgentOrchestrator { public async Task SelectOptimalSkillAsync( string userIntent, List availableSkills, double minConfidence = 0.75) { // Law 1: Early Exit on invalid input if (string.IsNullOrWhiteSpace(userIntent)) throw new ArgumentException("User intent cannot be empty", nameof(userIntent)); if (availableSkills == null || !availableSkills.Any()) throw new InvalidOperationException("No M365 skills registered for routing"); // Law 2: Parse at boundary, make illegal states unrepresentable var intentFeatures = ParseM365Intent(userIntent); var scoredCandidates = new List<(M365SkillMetadata Skill, double Score)>(); foreach (var skill in availableSkills) { if (!await IsM365SkillAvailableAsync(skill)) continue; var similarityScore = CalculateSemanticSimilarity(intentFeatures, skill.Triggers); var historicalScore = skill.HistoricalSuccessRate * 0.3; var availabilityScore = skill.IsHealthy ? 0.2 : 0.0; var totalScore = (similarityScore * 0.5) + historicalScore + availabilityScore; if (totalScore >= minConfidence) scoredCandidates.Add((skill, totalScore)); } if (!scoredCandidates.Any()) return null; // Law 3: Return new structure, never mutate inputs var best = scoredCandidates.OrderByDescending(c => c.Score).First(); return new SkillExecutionPlan { SelectedSkill = best.Skill, Confidence = best.Score, SelectionTimestamp = DateTimeOffset.UtcNow, RoutingContext = new Dictionary { { "intent", intentFeatures.RawText } } }; } private IntentFeatures ParseM365Intent(string input) => new IntentFeatures { RawText = input }; private double CalculateSemanticSimilarity(IntentFeatures features, List triggers) => 0.85; private Task IsM365SkillAvailableAsync(M365SkillMetadata skill) => Task.FromResult(skill.IsHealthy); } ``` ### Pattern 2: Execution with Fallback ```csharp public async Task ExecuteM365SkillAsync( SkillExecutionPlan plan, M365ExecutionContext context, int maxRetries = 2) { // Law 1: Guard clause for orchestration contract if (plan == null || context == null) throw new ArgumentNullException(nameof(plan), "Orchestration plan and context are required"); // Law 2: Validate M365 context before touching Graph SDK var validatedContext = ValidateM365Context(context); // Fallback chain: Retry -> Alternative Skill -> Human Defer var fallbackChain = new List>> { async (ctx) => await RetryWithExponentialBackoffAsync(plan.SelectedSkill, ctx), async (ctx) => await TryAlternativeM365SkillAsync(plan.SelectedSkill, ctx), async (ctx) => await DeferToHumanOperatorAsync(ctx) }; for (int attempt = 0; attempt <= maxRetries; attempt++) { try { // Law 4: Fail fast on transient Graph errors, don't patch var result = await ExecuteGraphOperationAsync(plan.SelectedSkill, validatedContext); // Law 3: Return immutable result snapshot return new ExecutionResult { Success = true, SkillName = plan.SelectedSkill.Name, Data = result, Attempts = attempt + 1, LatencyMs = Stopwatch.GetElapsedTime().Milliseconds, Confidence = plan.Confidence }; } catch (GraphServiceException ex) when (ex.StatusCode == HttpStatusCode.TooManyRequests) { if (attempt == maxRetries) return await ApplyFallbackChainAsync(fallbackChain, validatedContext); } catch (InvalidM365StateError ex) { // Law 4: Halt immediately on corrupt/invalid M365 state throw new SkillExecutionException($"Invalid M365 state for {plan.SelectedSkill.Name}: {ex.Message}", ex); } } throw new SkillExecutionException($"M365 skill {plan.SelectedSkill.Name} exhausted all {maxRetries + 1} execution attempts."); } ``` ### 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 365 Agents Documentation]() - [Microsoft Graph API for M365 Integration]() - [Power Automate Agent Builder]() - [Copilot Studio (M365 Agent Platform)]() - [Bot Framework for Custom M365 Agents (.NET)]()