Imported from david-lorenzo88/agent-framework-a365-agent (.agents/skills/make-ai-teammate/SKILL.md). Install upstream with npx skills add david-lorenzo88/agent-framework-a365-agent --skill make-ai-teammate. Copyright stays with the author.

Make AI Teammate

Trigger phrases — any of these will activate this skill:

• "make this agent an ai teammate"
• "transform this agent into an ai teammate"
• "publish this agent to teams"
• "make this agent available in microsoft teams"
• "publish this agent to microsoft copilot"
• "add teams support to this agent"
• "set up ai teammate hosting for this agent"
• "convert this agent to a teams agent"
• "make this agent work with microsoft 365"

What this skill does: It wraps your existing LLM logic with the Microsoft Agent 365 AI Teammate layer — hosting, routing, and notifications. Your existing LLM code (models, prompts, tools, business logic) is preserved and integrated into the new structure. Nothing is deleted.

Prerequisite: Run a365-setup first — it registers the agent with Agent 365 and writes the detection cache that this skill reads.

Supported languages: Node.js (LangChain, OpenAI Agents SDK, Claude SDK, Semantic Kernel, Google ADK) · .NET (AgentFramework, Semantic Kernel) · Python (AgentFramework, LangChain, OpenAI, Claude, Semantic Kernel, Google ADK)

Phase 0A — Workspace Triage and Detection Cache

Step 1 — Triage the workspace

Run in parallel and combine results:

• Glob **/*.csproj, package.json, requirements.txt, pyproject.toml, src/**/*.ts, **/*.cs, **/*.py → does any agent code or project file exist? Call this hasProjectFiles.
• Read .a365-workspace-detection.local.json → does the cache exist, and is detectedAt within 60 minutes? Call this cacheState (fresh, stale, or missing).
• Parse $ARGUMENTS for an explicit framework hint (e.g. dotnet, dotnet-sk, langchain, openai, claude, semantickernel, googleadk, python) and the word create. Store as argFramework and argCreateIntent.

Decide what to do next from this table — do not fall through to Step 2 until one of these branches has run:

Why this triage exists: Phase 0A.5 was designed for the empty-workspace new-agent path, but is only reachable after Step 2 succeeds. Without this triage, a user running /make-ai-teammate create a dotnet agentframework agent in an empty directory gets a "run a365-setup first" wall instead of the scaffold flow they asked for.

Step 2 — Load Detection Cache

🛑 STOP — .a365-workspace-detection.local.json MUST exist before this step. Read the file path .a365-workspace-detection.local.json in the working directory. If it does not exist, you arrived at Step 2 by skipping Step 1's triage routing. Do NOT proceed. Do NOT invent default cache values. Do NOT run any further phase (no package install, no file edits, no a365 CLI commands). Instead:

1. Tell the user verbatim: "I skipped the Step 1 triage and the detection cache wasn't written. Running a365-setup now to fix that, then I'll return here."
2. Read ${CLAUDE_PLUGIN_ROOT}/skills/a365-setup/SKILL.md and follow it to completion — a365-setup is what writes .a365-workspace-detection.local.json.
3. Re-verify the file now exists, then continue with "Load from cache" below.

This guard exists because earlier sessions have rationalised past Step 1's triage and run all of Phase 1 onward without the cache, producing partially-wired agents with no detection metadata. The stop hook (validate-make-ai-teammate.js) will fail the session at end if the cache file is still missing.

(Only reached once the cache is fresh — either it already was, or a365-setup just wrote it.)

Load from cache:

• programmingLanguage → use as language
• agentStack

Find existing LLM entry point (not stored by a365-setup — still required):

NodeJS: Glob src/**/*.ts and Grep for LLM instantiation (ChatOpenAI, AzureChatOpenAI, OpenAI, Anthropic, Kernel, @google/generative-ai, @google/adk), chain/agent creation, or existing HTTP server.

DotNet: Glob **/*.cs and Grep for AddAgent<, AgentApplication, IChatClient, Microsoft.SemanticKernel, or WebApplication.CreateBuilder. Store Program.cs and agent .cs files.

Python: Glob **/*.py and Grep for ChatAgent, AzureOpenAIChatClient, CloudAdapter (or legacy CloudAdapterAiohttp), or AgentInterface.

Store the main source file(s) as existingFiles.

Phase 0A.5 — New Agent Path (no source files found)

This phase is normally entered directly from the Phase 0A Step 1 triage when cacheState = missing and hasProjectFiles = false. It can also be entered as a fallback when Phase 0A Step 2 runs but finds no LLM entry point.

Argument pre-selection: If $ARGUMENTS contained argFramework from Phase 0A, map it to the option below and skip the menu — only show the menu when the user gave no framework hint:

When pre-selected, tell the user the inference: "Picked option {N} ({sample name}) based on your request. Proceeding to clone…" and jump straight to Step 1 (Verify git) below.

Empty-directory fallback check (only when not entered from Phase 0A triage): If existingFiles is empty AND no .csproj, package.json, or requirements.txt exists anywhere in the working directory, the user is starting fresh — show the menu below.

In this case, do NOT fail — offer to scaffold from an official sample:

No agent code found in this directory. Would you like to start from an official
Agent365-Samples project?

Pick a framework and I'll clone the sample, then continue with the AI Teammate setup:

  .NET
    1. Agent Framework  — classic AgentApplication pattern with IChatClient
       https://github.com/microsoft/Agent365-Samples/tree/main/dotnet/agent-framework/sample-agent
    2. Semantic Kernel   — Kernel + IChatCompletionService pattern
       https://github.com/microsoft/Agent365-Samples/tree/main/dotnet/semantic-kernel/sample-agent

  Node.js
    3. LangChain         — ReactAgent with AzureChatOpenAI / ChatOpenAI
       https://github.com/microsoft/Agent365-Samples/tree/main/nodejs/langchain/sample-agent
    4. OpenAI Agents SDK — @openai/agents with run()
       https://github.com/microsoft/Agent365-Samples/tree/main/nodejs/openai/sample-agent

  Python
    5. Agent Framework   — ChatAgent with AzureOpenAIChatClient
       https://github.com/microsoft/Agent365-Samples/tree/main/python/agent-framework/sample-agent
    6. Claude SDK        — ClaudeSDKClient with ClaudeAgentOptions
       https://github.com/microsoft/Agent365-Samples/tree/main/python/claude/sample-agent
    7. Google ADK        — google.adk Agent + Runner
       https://github.com/microsoft/Agent365-Samples/tree/main/python/google-adk/sample-agent

  0. I'll bring my own code — skip cloning

If the user picks a sample (1–7), run prerequisite checks BEFORE cloning:

Step 1 — Verify git is installed

git --version

If the command fails:

"git is not installed. Please install it from https://git-scm.com/downloads and restart your terminal, then try again."

Stop until the user confirms git is installed.

Step 2 — Verify GitHub CLI is installed

gh --version

If the command fails:

"GitHub CLI (gh) is not installed. Install it from https://cli.github.com/ and restart your terminal. The CLI is used to authenticate with GitHub before cloning the sample."

Stop until the user confirms gh is installed.

Step 3 — Verify GitHub authentication

gh auth status

Check the output:

• If output contains Logged in to github.com → authenticated, proceed.
• If output contains not logged in or exits non-zero:

"You are not logged in to GitHub. Run the following command to authenticate:

gh auth login

Choose GitHub.com, then HTTPS, then Login with a web browser. Follow the prompts, then come back here."

Stop until gh auth status succeeds.

Step 4 — Verify language-specific toolchain (pre-clone)

Run the relevant check for the chosen sample:

If the check fails:

"{tool} is not installed or is below the minimum version. Please install it from {install URL} and restart your terminal."

Stop until the check passes.

Step 5 — Clone the sample

Once all checks pass, clone and copy the chosen sample into the current directory:

# Pattern — replace {path} with the framework subfolder
git clone --depth 1 https://github.com/microsoft/Agent365-Samples.git _tmp_a365samples

Then copy only the chosen sample subfolder:

# Example for option 3 (Node.js LangChain):
cp -r _tmp_a365samples/nodejs/langchain/sample-agent/. .
rm -rf _tmp_a365samples

Tell the user:

"✅ Sample cloned into the current directory. Continuing with AI Teammate setup…"

Set language and agentStack from the chosen option, re-run the LLM entry point detection above, then continue to Phase 0B as normal.

Step 6 — Install sample dependencies (post-clone)

Before continuing, install the sample's dependencies so subsequent build steps succeed:

If uv is not installed for Python:

pip3 install uv 2>/dev/null || pip install uv
uv sync

If the user picks 0 (bring own code): Ask: "What language and framework are you using?" and set language and agentStack accordingly, then continue to Phase 0B.

Check what's already present (parallel Grep). For has_obs and has_workiq the project's history may contain partial wiring left over from earlier skill runs that crashed, were interrupted, or were generated by an older plugin version. Treat the entry-point symbol alone as insufficient — compute a complete vs partial signal so Phase 9.5 / 9.6 can route into recovery instead of silently skipping the gaps.

NodeJS:

• AgentApplication in src/**/*.ts → hasAgentApp
• CloudAdapter in src/**/*.ts → hasHosting
• onAgentNotification in src/**/*.ts → hasNotifications
• ToolingManifest.json exists → hasManifest
• Observability composite — compute three sub-signals, then combine:
  • obs_entry = useMicrosoftOpenTelemetry in any src/**/*.ts
  • obs_token = tokenResolver OR AgenticTokenCacheInstance in any src/**/*.ts (S2S also accepts getS2SObservabilityToken / startTokenService)
  • obs_handler = BaggageBuilder OR BaggageBuilderUtils OR InvokeAgentScope in any src/**/*.ts
  • has_obs_complete = obs_entry && obs_token && obs_handler
  • has_obs_partial = obs_entry && !has_obs_complete
  • has_obs = has_obs_complete (only "true" when the wiring is end-to-end)

DotNet:

• AgentApplication in **/*.cs → hasAgentApp
• adapter.ProcessAsync or IAgentHttpAdapter in **/*.cs → hasHosting
• OnConversationUpdate or InstallationUpdate in **/*.cs → hasNotifications
• ToolingManifest.json exists → hasManifest
• Observability composite:
  • obs_entry = UseMicrosoftOpenTelemetry in Program.cs (or legacy AddA365Tracing)
  • obs_token = OBO: distro auto-registers IExporterTokenCache<AgenticTokenStruct> so accept UseMicrosoftOpenTelemetry itself; S2S: ObservabilityTokenService / AddAgent365Observability
  • obs_handler = BaggageBuilder OR BaggageTurnMiddleware OR InvokeAgentScope.Start in **/*.cs
  • has_obs_complete = obs_entry && obs_token && obs_handler
  • has_obs_partial = obs_entry && !has_obs_complete
  • has_obs = has_obs_complete

Python:

• AgentInterface in **/*.py → hasAgentApp
• CloudAdapter or legacy CloudAdapterAiohttp in **/*.py → hasHosting
• on_agent_notification in **/*.py → hasNotifications
• ToolingManifest.json exists → hasManifest
• Observability composite:
  • obs_entry = use_microsoft_opentelemetry in any **/*.py
  • obs_token = token_resolver OR AgenticTokenCache OR cache_agentic_token OR S2S: run_token_service / get_s2s_observability_token
  • obs_handler = BaggageBuilder OR populate_baggage OR InvokeAgentScope in any **/*.py
  • has_obs_complete = obs_entry && obs_token && obs_handler
  • has_obs_partial = obs_entry && !has_obs_complete
  • has_obs = has_obs_complete

Skill-state signals (language-agnostic):

• has_workiq (composite — replaces the disk-only check):
  • wiq_manifest = ToolingManifest.json exists AND its top-level mcpServers array (or servers in legacy v1 schema) is non-empty
  • wiq_code = per-language MCP wiring symbol present in the agent code:
    • NodeJS LangChain: addToolServersToAgent in src/**/*.ts
    • NodeJS OpenAI / Claude SDK: addToolServersToAgent in src/**/*.ts
    • .NET AF: GetMcpToolsAsync in **/*.cs
    • .NET SK: AddToolServersToAgentAsync in **/*.cs
    • Python AF / OpenAI / Google ADK: add_tool_servers_to_agent in **/*.py
  • wiq_word_mention (gated — only relevant when stack = Node.js LangChain AND manifest contains mcp_WordServer): WpxComment AND proactive AND userKeyToConversationId all present in src/**/*.ts
  • has_workiq_complete = wiq_manifest && wiq_code && (Word-mention gate satisfied OR not applicable)
  • has_workiq_partial = wiq_manifest && !has_workiq_complete
  • has_workiq = has_workiq_complete
• disk_blueprint_present — a365.generated.config.json exists on disk AND has a non-empty agentBlueprintId. Computed at read-time from disk, never from the cache. This is a disk signal, not a truth claim — the file can be stale (blueprint deleted in Entra, file copied from another project, agent-name mismatch). For advisory display only (matrix view, summary).
• blueprint_verified_for_session — set to true ONLY after the user has gone through Step 9.7.1a's three-way prompt in this session and explicitly chose Reuse (or Re-run / Fresh completed successfully). Until then, treat as false regardless of disk_blueprint_present. This is the gate that downstream logic must consult before treating the blueprint claim as authoritative.

Cache discipline: .a365-workspace-detection.local.json stores STATIC detection data (language, framework, programming language, agentType, authMode). It does NOT track disk_blueprint_present, blueprint_verified_for_session, has_obs_partial, or has_workiq_partial — all are derived at read-time from the live project files. The CLI can mutate blueprint state between sessions (cleanup, fresh setup-all) without updating the cache, and even disk state can lie about tenant state.

These flags (has_obs, has_workiq, disk_blueprint_present) drive the 8-row state matrix in Phase 0C — but for the blueprint dimension the matrix is advisory only (see Step 9.7.1a verification gate). The _partial variants are read by Phase 9.5 / 9.6 to choose between "skip — already complete" and "re-enter — recover the missing pieces".

Phase 0B — Confirm and Create Task List

Show the user the upcoming task list visibly BEFORE Phase 1. Exactly one task in_progress at a time; complete before moving on. Use whichever mechanism the runtime supports:

• Claude Code: call TaskCreate for each item below (already in allowed-tools); the list renders natively. Use TaskUpdate to flip statuses.
• VS Code Copilot Chat / GitHub Copilot CLI: allowed-tools is ignored — emit a markdown checklist directly in chat (- [ ] Install required packages…) and edit items to - [x] as each phase completes.

Present all detections in one message:

Language: {language}  |  Framework: {agentStack}  |  Existing code: {existingFiles.join(', ')}

AI Teammate scaffolding:
  • Hosting layer:    {hasHosting ? "✅" : "❌"}
  • Agent class:      {hasAgentApp ? "✅" : "❌"}
  • Notifications:    {hasNotifications ? "✅" : "❌"}

Agent 365 capabilities:
  • Observability:    {has_obs ? "✅ already wired" : "❌ will be added"}
  • WorkIQ tools:     {has_workiq ? "✅ already wired" : "❌ will be offered"}
  • Blueprint setup:  {disk_blueprint_present ? "✅ registered (Blueprint ID: " + existingBlueprintId + ")" : "❌ will run a365 setup all"}

Reply **yes** to confirm, or describe corrections.

If agentStack is still unknown, ask which LLM framework the agent uses.

If agentStack is unrecognized, tell the user:

"This skill supports all major frameworks: .NET (AgentFramework, Semantic Kernel), Node.js (LangChain, OpenAI Agents SDK, Claude SDK, Semantic Kernel, Google ADK), and Python (AgentFramework, LangChain, OpenAI, Claude, Semantic Kernel, Google ADK). For other frameworks, I'll add the hosting layer and agent class, but you'll need to integrate your LLM calls manually."

NodeJS tasks (only create if not already present):

TaskCreate: "Install required @microsoft/agents-* npm packages"
TaskCreate: "Configure tsconfig.json for node16 module resolution"  [skip if already correct]
TaskCreate: "Add src/index.ts — Express + CloudAdapter hosting"     [skip if hasHosting]
TaskCreate: "Add src/agent.ts — AgentApplication class"             [skip if hasAgentApp]
TaskCreate: "Add src/client.ts — LLM client factory"               [skip if exists]
TaskCreate: "Update .env / .env.example with A365 variables"
TaskCreate: "Validate build (npm run build)"
TaskCreate: "Add Observability"
TaskCreate: "Add WorkIQ Tools (optional)"
TaskCreate: "Register, publish, and deploy"

.NET tasks (only create if not already present):

TaskCreate: "Add Microsoft.Agents.A365.* NuGet packages"
TaskCreate: "Update Program.cs — A365 services + /api/messages + /api/health"  [skip if hasHosting]
TaskCreate: "Add Agent/MyAgent.cs — AgentApplication subclass"                  [skip if hasAgentApp]
TaskCreate: "Update appsettings.json with A365 auth and connection config"
TaskCreate: "Validate build (dotnet build)"
TaskCreate: "Add Observability"
TaskCreate: "Add WorkIQ Tools (optional)"
TaskCreate: "Register, publish, and deploy"

Python tasks (only create if not already present):

TaskCreate: "Add microsoft_agents_a365_* to pyproject.toml"
TaskCreate: "Add agent_interface.py"                                             [skip if exists]
TaskCreate: "Add host_agent_server.py — aiohttp server + A365 routing"          [skip if hasHosting]
TaskCreate: "Update agent.py — AgentInterface implementation"                   [skip if hasAgentApp]
TaskCreate: "Update .env / .env.template with A365 variables"
TaskCreate: "Validate setup (uv sync or pip install)"
TaskCreate: "Add Observability"
TaskCreate: "Add WorkIQ Tools (optional)"
TaskCreate: "Register, publish, and deploy"

Phase 0C — Resolve State and Route

The 8-row state matrix below decides what runs vs skips vs short-circuits based on (has_obs, has_workiq, disk_blueprint_present). Compute the row, print the resolved plan to the user, and route accordingly.

Note on setup — disk presence is not verification: the matrix's "skip setup" rows (5–8) are advisory only. Step 9.7.1a is the canonical verification gate — it reads a365.generated.config.json, shows the blueprint ID + agent name to the user, and asks Reuse / Re-run / Fresh explicitly. Only that interaction sets blueprint_verified_for_session = true. The matrix's ~~setup~~ cells mean the default suggestion is reuse, not that setup-all is silently skipped. This handles the cases where disk lies about tenant state: blueprint deleted in Entra, file copied from another project, agent-name mismatch.

Print the resolved state to the user, verbatim, before any work runs:

Resolved state (row {N}): has_obs={T/F}, has_workiq={T/F}, disk_blueprint_present={T/F}

Plan:
  • Observability:   {run | skip — already wired}
  • WorkIQ:          {ask user | skip — already wired | skip — guard (row 4/8)}
  • Setup (register): {run a365 setup all --aiteammate --m365 (no existing blueprint)
                     | ask user at 9.7.1: reuse / re-run / fresh (blueprint exists)}
  • Run Target:      asked at Phase 9.7.2 (Prod vs Local). For Prod, a hosting
                     sub-question (Phase 9.7.2b) follows: dev tunnel or cloud
                     endpoint (Azure / AWS / Google Cloud). For Local, agent
                     runs at http://localhost:3978/api/messages and only
                     AgentsPlayground is launched — no Teams reachability.

Row 8 sub-question — only if row 8 (T/T/T):

Everything is already wired and registered:
  • Blueprint ID:  {existingBlueprintId}
  • Observability: present
  • WorkIQ:        present

What would you like to do?

  1. Re-publish — repackage manifest (a365 publish), re-upload zip, refresh Dev Portal
     config. Useful after code changes.
  2. Verify only — print the resolved state and exit. No CLI commands run.

• If 1 (Re-publish): skip Phases 1–9.6, jump to Phase 9.7.2 (Run Target). For Prod, run publish + Dev Portal + instance flow. For Local, jump to smoke test only.
• If 2 (Verify): print the final summary (Phase 10) and exit.

Routing for rows 1–7: continue with Phase 1 (Install Packages). The skip-gates in Phases 9.5 / 9.6 / 9.7.1 enforce the row-specific behavior.

Phase 1 — Install Required Packages

Mark task in progress.

Read the language-appropriate reference file for the full package list, then install only missing packages:

• NodeJS — ${CLAUDE_PLUGIN_ROOT}/skills/make-ai-teammate/references/nodejs-ai-teammate.md (see "Required Packages" for the {agentStack} variant, and "Dev dependencies")
• .NET — ${CLAUDE_PLUGIN_ROOT}/skills/make-ai-teammate/references/dotnet-ai-teammate.md (see "Required NuGet Packages")
• Python — ${CLAUDE_PLUGIN_ROOT}/skills/make-ai-teammate/references/python-ai-teammate.md (see "Required Dependencies")

Node.js LangChain — @langchain/core version pre-flight (skip for OpenAI Agents SDK / Claude SDK / Semantic Kernel / Google ADK)

Before running the install, Read package.json and check the @langchain/core version. If the existing range is ^0.3.* or ~0.3.*, the install will force a major-version upgrade to v1 (peer dep of @langchain/mcp-adapters@^1.0.0). Warn the user verbatim:

"Your project depends on @langchain/core@0.3.x. Installing the MCP adapter requires @langchain/core@^1.0.0 — a breaking upgrade. Known compile-break spots in v1: bindTools return type changed (you may need to migrate or add as never casts on the model arg), and ToolMessage content shape changed. Proceed with the upgrade and fix-forward? (yes / no)"

If yes, install and continue to Phase 9 (build validation) — fix-forward any type errors there. If no, stop and tell the user the AI Teammate MCP wiring requires v1; they can either upgrade or skip WorkIQ tools at Phase 9.6.

If @langchain/core is not in package.json or is already ^1.0.0, no warning needed — install directly.

Mark task complete.

Phase 2 — Language Setup

Mark task in progress.

NodeJS — Configure tsconfig.json

Read tsconfig.json if it exists. Check "module": "node16" and "moduleResolution": "node16" are both present.

If missing or wrong, Edit (or Write if not present) using the tsconfig template from nodejs-ai-teammate.md. Preserve existing rootDir/outDir — only update module/moduleResolution.

.NET — Skip

No tsconfig equivalent needed. Proceed to Phase 4.

Python — Skip

No tsconfig equivalent needed. Proceed to Phase 4.

Mark task complete.

Phase 4 — Hosting Layer

Mark task in progress.

NodeJS — Add src/index.ts

Read src/index.ts if it exists.

If index.ts does NOT exist or has no CloudAdapter: Write src/index.ts using the pattern from nodejs-ai-teammate.md.

If index.ts already has an HTTP server (Express or other): Migrate it to the CloudAdapter pattern:

1. Add configDotenv() as the very first line (before existing imports).
2. Replace or augment the existing server with CloudAdapter, authorizeJWT, and loadAuthConfigFromEnv.
3. Add /api/health endpoint BEFORE authorizeJWT.
4. Replace the existing message endpoint with /api/messages using adapter.process().
5. Replace server.listen('0.0.0.0', ...) with the production/dev host detection pattern.
6. Preserve any existing routes or middleware the user has.

Non-destructive rule: Never delete existing routes. Add the A365 routes alongside them.

.NET — Update Program.cs

Read Program.cs if it exists.

If Program.cs does NOT exist: Write Program.cs using the pattern from dotnet-ai-teammate.md.

If Program.cs exists but is missing A365 services: Edit Program.cs to add:

1. app.MapPost("/api/messages", ...) using adapter.ProcessAsync()
2. app.MapGet("/api/health", () => Results.Ok(...)) — health check endpoint, NO auth required

Non-destructive rule: Preserve existing services and middleware. Add A365 registrations after existing ones.

Python — Add host_agent_server.py

Glob host_agent_server.py. If it exists, check for CloudAdapter (or legacy CloudAdapterAiohttp), /api/messages, /api/health, and on_agent_notification.

If it does not exist, Write host_agent_server.py using the pattern from python-ai-teammate.md. Also create agent_interface.py using the pattern from python-ai-teammate.md if it does not exist.

Mark task complete.

Phase 5 — Agent Class

Mark task in progress.

NodeJS — Add src/agent.ts

Read src/agent.ts if it exists. Grep AgentApplication in src/**/*.ts.

If agent.ts does NOT exist: Write src/agent.ts using the full pattern from nodejs-ai-teammate.md.

• Replace MyAgent class name with a name derived from the project.
• Replace placeholder session description with what the agent does.

If an AgentApplication subclass already exists: Check each handler and add only what is missing:

• onAgentNotification('agents:*', ...) with priority 1 and [authHandlerName]
• onActivity(ActivityTypes.Message, ...) with [authHandlerName]
• onActivity(ActivityTypes.InstallationUpdate, ...)
• handleAgentNotificationActivity() dispatching on NotificationType.EmailNotification
• handleEmailNotification() using createEmailResponseActivity
• handleInstallationUpdateActivity()
• Typing indicator loop (setInterval every 4000ms) in message handler

Critical import: import '@microsoft/agents-a365-notifications' (side-effect form) must be present. Without it, notification routing silently breaks at runtime.

.NET — Add Agent/MyAgent.cs

Glob **/*.cs and Grep for AgentApplication.

If no AgentApplication subclass exists: Create Agent/MyAgent.cs using the full pattern from dotnet-ai-teammate.md.

• Rename MyAgent to match the project name (from .csproj).
• Register agent in Program.cs: builder.AddAgent<MyAgent>();

If an AgentApplication subclass already exists: Check each registration and add only what is missing:

• OnConversationUpdate(ConversationUpdateEvents.MembersAdded, WelcomeMessageAsync)
• OnActivity(ActivityTypes.InstallationUpdate, OnInstallationUpdateAsync, isAgenticOnly: true, autoSignInHandlers: agenticHandlers)
• OnActivity(ActivityTypes.InstallationUpdate, OnInstallationUpdateAsync, isAgenticOnly: false)
• OnActivity(ActivityTypes.Message, OnMessageAsync, isAgenticOnly: true, autoSignInHandlers: agenticHandlers)
• OnActivity(ActivityTypes.Message, OnMessageAsync, isAgenticOnly: false, autoSignInHandlers: oboHandlers)
• Typing indicator loop (4 second interval) in OnMessageAsync

Prompt injection guard: GetAgentInstructions() must sanitize Activity.From.Name by stripping control characters ([\p{Cc}\p{Cf}]) and capping at 64 characters.

Python — Update agent.py

Read agent.py if it exists.

If agent.py does NOT implement AgentInterface: Write agent.py using the full pattern from python-ai-teammate.md.

• Preserve existing LLM client configuration (endpoint, deployment, API key env vars).
• Preserve existing system prompt if one exists.

If agent.py already implements AgentInterface: Check and add only what is missing:

• _sanitize_display_name() before injecting into system prompt
• handle_agent_notification_activity() handling NotificationTypes.EMAIL_NOTIFICATION

⚠️ Preserve prior-skill additions when re-writing agent.py. If a prior run of add-workiq-tools ran, agent.py will already contain tool_service, mcp_servers_initialized, and a setup_mcp_servers(...) method called from process_user_message. The full-rewrite branch above MUST preserve these — otherwise re-running make-ai-teammate after WorkIQ silently clobbers the MCP wiring. Pre-check: grep agent.py for tool_service, add_tool_servers_to_agent, or McpToolRegistrationService BEFORE overwriting. If present, switch to the additive branch (check-and-add) and explicitly keep those lines. Same rule applies to instrument-observability additions (BaggageBuilder import, with builder.build(): block) — preserve, don't overwrite.

Mark task complete.

Phase 6 — LLM Client Factory (Node.js only)

Mark task in progress: "Add src/client.ts — LLM client factory"

NodeJS — Add src/client.ts

This wraps the user's existing LLM code behind the Client interface called by agent.ts.

6.1 Read existing LLM code

Read all files in existingFiles. Identify:

• How the LLM model is instantiated (constructor, env vars used)
• How agents/chains are created and invoked
• Any existing system prompts

6.2 If client.ts does NOT exist: Write src/client.ts using the {agentStack} variant from nodejs-ai-teammate.md.

• Preserve the user's existing model instantiation and env vars
• Preserve the user's existing system prompt if one exists

6.3 If client.ts already exists: Check for each required element and add what is missing:

• getClient() factory returning a Client interface
• Client interface with invoke(prompt: string): Promise<string>

6.4 Wire existing LLM invocation: The user's existing LLM invocation goes inside the Client.invoke() method. Show the user a diff summary.

.NET — Skip

Agent calls _chatClient directly in OnMessageAsync. No changes needed.

Python — Skip

Agent calls self._agent.run() directly in process_user_message(). No changes needed.

Mark task complete.

Phase 7 — ToolingManifest.json (owned by add-workiq-tools — DO NOT write here)

Do NOT create ToolingManifest.json in this skill. The file is owned by add-workiq-tools, which writes it via a365 develop add-mcp-servers using authoritative server metadata pulled from a365 develop list-available. Pre-populating the manifest here would:

• Force Calendar + Mail on every user without asking.
• Set has_workiq = true automatically, causing Phase 9.6's skip-gate to silently bypass add-workiq-tools so the user can never pick Teams / SharePoint / OneDrive / etc.
• Hardcode audience GUIDs and URLs that go stale when Microsoft updates the catalog.

If ToolingManifest.json already exists (the user ran add-workiq-tools earlier, or it was carried over from a sample clone), leave it alone — Phase 9.6's has_workiq detection picks it up.

Mark task complete: skipped — manifest creation is owned by add-workiq-tools at Phase 9.6.

Phase 8 — Update Environment Configuration

Mark task in progress: "Update env config with A365 variables"

NodeJS — Update .env / .env.example

Read .env.example or .env. Read the .env — Complete Template section from nodejs-ai-teammate.md for the canonical key list and the "What reads what" table.

Additive rule: Append only the keys missing from the current .env. Do NOT delete existing keys the user has set (even if they're dead — e.g. USE_AGENTIC_AUTH, agentic_connectionName, agent365Observability__agentBlueprintId/__clientId/__clientSecret/__sponsorUser*). The reference file's "What's NOT in this template" section documents which keys are inert; the skill flags them at the end of Phase 8 so the user can clean up if they want, but it does not auto-delete.

Run-target rewrite (the one exception to additive-only): Read runTarget and runTargetHosting from .a365-workspace-detection.local.json and rewrite these two keys in .env to match:

If the user has hand-set NODE_ENV or ENABLE_A365_OBSERVABILITY_EXPORTER to a non-conforming value for the chosen runTarget, tell them verbatim: "Updating NODE_ENV=<new> and ENABLE_A365_OBSERVABILITY_EXPORTER=<new> to match your runTarget choice (was <old>). If this is wrong, change runTarget in .a365-workspace-detection.local.json instead of hand-editing .env." Then update both keys.

Populate prod / dev-tunnel keys from a365.generated.config.json when they're empty:

• connections__service_connection__settings__clientId ← agentBlueprintId
• connections__service_connection__settings__clientSecret ← agentBlueprintClientSecret
• connections__service_connection__settings__tenantId ← tenantId
• agent365Observability__agentId ← agentBlueprintId
• agent365Observability__tenantId ← tenantId
• agent365Observability__agentName ← agentBlueprintDisplayName (if available)
• agent365Observability__agentDescription ← agentDescription (if available)

For runTarget=local, leave the above empty — they're inert in the AgentsPlayground path. Do not populate.

After Phase 8 completes, surface a one-line summary: "Updated .env for runTarget=<value>. NODE_ENV=<value>, ENABLE_A365_OBSERVABILITY_EXPORTER=<value>. Found N dead/stray keys (see reference doc — safe to delete)."

.NET — Update appsettings.json

Read appsettings.json if it exists. Add any missing sections using the pattern from dotnet-ai-teammate.md:

• AgentApplication section with AgenticAuthHandlerName and UserAuthorization.Handlers.agentic
• TokenValidation section with Audiences
• Connections.ServiceConnection section
• ConnectionsMap array
• AIServices.AzureOpenAI section (with placeholder values "")

Do NOT overwrite existing values — only add missing keys.

Python — Update .env / .env.template

Read .env.template or .env. Read the .env template section from python-ai-teammate.md. Append only the missing variables.

Mark task complete.

Phase 9 — Validate Build

Mark task in progress: "Validate build"

NodeJS

npm install
npm run build || npx tsc --noEmit

Fix errors:

• Module resolution errors → check "module": "node16" AND "moduleResolution": "node16" in tsconfig
• Import path errors → add .js extension in imports for node16 module resolution
• Cannot find module → add the missing package

.NET

dotnet restore
dotnet build

Fix errors:

• namespace not found → check the package is installed and using directive is present
• IChatClient not found → ensure Microsoft.Extensions.AI.OpenAI is installed (AgentFramework)
• Kernel / IChatCompletionService not found → ensure Microsoft.SemanticKernel is installed (Semantic Kernel)
• IAgentHttpAdapter not found → ensure Microsoft.Agents.Hosting.AspNetCore is installed

Python

uv sync
# or: pip3 install -e . 2>/dev/null || pip install -e .
python3 -c "import host_agent_server; import agent; print('imports OK')" 2>/dev/null || python -c "import host_agent_server; import agent; print('imports OK')"

Fix errors:

• ModuleNotFoundError for microsoft_agents_a365_* → run uv add <package> or pip install <package>
• requires-python mismatch → ensure Python 3.11+ is active

Do NOT revert changes on build failure — fix forward.

Mark task complete.

Phase 9.5 — Add Observability

Mark task in progress: "Add Observability"

Three-way gate — branch on the composite signal computed in Phase 0A.3:

• If has_obs_complete = true (rows 2, 4, 6, 8 with full end-to-end wiring): tell the user verbatim "Observability already wired end-to-end (entry-point + token resolver + handler-side baggage / scopes) — skipping. Run /agent365:instrument-observability to reconfigure." and mark the task complete. Do NOT invoke the sub-skill.
• If has_obs_partial = true (any signal of obs in the project but at least one of entry / token / handler is missing): do not skip — recover. Tell the user verbatim "Found partial observability wiring (entry-point present, but token resolver and/or message-handler scopes are missing). Completing the wiring now — re-entering /agent365:instrument-observability to finish what was left half-done." Then proceed to the same steps as the has_obs = false branch below. The sub-skill is idempotent and additive, so partial-recovery is safe.
• If has_obs_complete = false && has_obs_partial = false (rows 1, 3, 5, 7 — no obs at all): proceed to the steps below.

Steps (for partial-recovery and fresh-wire paths):

1. Pre-populate the detection cache so the sub-skill skips its agent-kind / auth-mode wizard. Read .a365-workspace-detection.local.json, merge { "agentType": "ai-teammate", "authMode": "agentic-user" } into it, and Write it back. AI Teammate always uses the agentic-user identity — there's no obo/s2s decision to make.
2. Read ${CLAUDE_PLUGIN_ROOT}/skills/instrument-observability/SKILL.md and follow it now. Observability is part of the AI Teammate package — do not ask whether to add it. The sub-skill's Phase 0.5 will see the pre-populated cache and skip the confirmation prompt automatically.

Mark task complete: "Add Observability"

Phase 9.6 — Offer WorkIQ Tools (optional)

Mark task in progress: "Add WorkIQ Tools (optional)"

Three-way gate — branch on the composite WorkIQ signal computed in Phase 0A.3:

• If has_workiq_complete = true (rows 3, 4, 7, 8 with manifest + agent-code wiring + Word @mention wiring if applicable): tell the user verbatim "WorkIQ tools already wired end-to-end (manifest, agent code, and — where applicable — Word @mention handler). Skipping. Run /agent365:add-workiq-tools to add more or reconfigure." and mark the task complete. Do NOT invoke the sub-skill.

• If has_workiq_partial = true (manifest non-empty but the agent code or the Word-mention wiring is missing): do not skip — recover. Tell the user verbatim "Found a populated ToolingManifest.json but the agent-code wiring is incomplete (addToolServersToAgent / GetMcpToolsAsync / add_tool_servers_to_agent missing, or — for Node.js LangChain with mcp_WordServer — the Word @mention handler was never added). Re-entering /agent365:add-workiq-tools to finish the wiring; nothing already in the manifest will be touched." Then proceed to the steps below as if the user picked Yes. The sub-skill is idempotent — Phase 3 will detect existing manifest entries and only Phase 4 / 4.5 code wiring will be patched in.

• If has_workiq_complete = false && has_workiq_partial = false (rows 1, 2, 5, 6 — no manifest yet): ask the user:

  "Would you like to add WorkIQ MCP tools now? WorkIQ lets your AI Teammate use Calendar, Mail, and other M365 tools via MCP servers."

  • Yes → Read ${CLAUDE_PLUGIN_ROOT}/skills/add-workiq-tools/SKILL.md and follow it in full.
  • Skip → inform the user they can run /agent365:add-workiq-tools later.

Mark task complete: "Add WorkIQ Tools (optional)"

Phase 9.7 — Register, Publish, and Deploy

Mark task in progress: "Register, publish, and deploy"

This is the full registration + publish + Teams Developer Portal + instance-request pipeline — about 370 lines of step-by-step guidance. It lives in its own reference file because the steps are largely language-agnostic and would dominate the SKILL.md otherwise.

Read ${CLAUDE_PLUGIN_ROOT}/skills/make-ai-teammate/references/deploy-pipeline.md and follow it in full. The step numbering matches Phase 9.7 (9.7.1 through 9.7.7) so this skill's stop-hook prompt, the eval expectations, and the README all keep pointing at the same places. Inside the deploy pipeline you'll go through:

• 9.7.1 — a365 setup all --aiteammate --m365. Re-checks disk for an existing a365.generated.config.json and asks the user explicitly: reuse (skip setup-all), re-run for refresh (idempotent), or create fresh (cleanup first). The cache-based skip-gate is advisory only — the user always sees the choice when a blueprint already exists. --m365 is always passed for AI Teammate — no separate user question.
• 9.7.2 / 9.7.2a / 9.7.2b / 9.7.2c / 9.7.2d — choose Run Target (prod vs local), collect the production hosting sub-question (dev tunnel vs cloud), reconcile chosenEndpoint onto the blueprint by running a365 setup blueprint --update-endpoint --m365 unconditionally for prod (mandatory — not gated on a diff with messagingEndpoint), and validate the environment-config table (per-language .env / appsettings.json keys plus cloud-platform env-var checks).
• 9.7.3 → 9.7.6 — manifest verify (read-only — CLI owns the file), a365 publish to produce manifest.zip, manual upload at M365 Admin Center, Teams Developer Portal verify (Agent Type = API Based, Notification URL = messagingEndpoint — auto-registered via --update-endpoint --m365; manual fallback only), and the agent-instance request + admin approval. Skipped entirely when runTarget = "local".
• 9.7.7 — smoke test (Teams for prod, AgentsPlayground for local).

When you return from the reference, mark the task complete and continue to Phase 10.

Mark task complete: "Register, publish, and deploy"

Phase 10 — Final Summary and Next Steps

TaskList — show all completed tasks, then build a state-aware summary. Adapt each bullet to whether the step ran, was skipped because already wired, or was skipped because of runTarget = "local":

✅ AI Teammate flow complete!

Resolved state at entry: has_obs={T/F}, has_workiq={T/F}, disk_blueprint_present={T/F}
Run Target: {prod | local}{runTarget = "prod" ? " — hosting: " + runTargetHosting + " (" + chosenEndpoint + ")" : ""}

Your agent now has:
  • Hosting layer         (/api/health + /api/messages)
  • Agent routing         (message, notification, InstallationUpdate handlers)
  • Email notifications + install/uninstall lifecycle
  • ToolingManifest.json  {has_workiq-at-entry || (user picked yes in Phase 9.6)
                              ? "wired by add-workiq-tools (a365 develop add-mcp-servers)"
                              : "not created — run /agent365:add-workiq-tools to wire WorkIQ tools"}
  • Blueprint              {disk_blueprint_present-at-entry
                              ? "reused (Blueprint ID: " + existingBlueprintId + ")"
                              : "registered (a365 setup all --aiteammate --m365)"}
  • Observability          {has_obs-at-entry
                              ? "already wired — skipped"
                              : "OpenTelemetry + A365 tracing exporter wired"}
  • WorkIQ tools           {has_workiq-at-entry
                              ? "already wired — skipped"
                              : (user picked yes
                                   ? "M365 data access via MCP wired"
                                   : "offered, user skipped — run /agent365:add-workiq-tools later")}

{If runTarget = "prod":}
  • Hosting               {runTargetHosting = "devtunnel"
                              ? "Dev tunnel — " + chosenEndpoint
                              : "Cloud (Azure / AWS / GCP) — " + chosenEndpoint}
  • Manifest packaged     (a365 publish → manifest.zip; uploaded manually to M365 Admin Center)
  • Dev Portal configured (Agent Type=API Based, Notification URL={chosenEndpoint})
  • Instance requested    (request from Teams Apps; admin approves at admin.cloud.microsoft/#/agents/all/requested)

{If runTarget = "local":}
  • Run mode: Local       Agent runs at http://localhost:3978/api/messages.
                          (publish, Dev Portal config, MAC upload, instance request all SKIPPED)
                          When you're ready for production, re-run /agent365:make-ai-teammate
                          and choose Prod at Step 9.7.2 (then dev tunnel or cloud at 9.7.2b).

Useful commands:
  cat a365.generated.config.json            — show Blueprint ID, App ID, and agent details
  a365 cleanup instance                     — remove Agentic User if re-provisioning is needed
  devtunnel host <name> --port 3978         — restart dev tunnel for local testing

Next steps:
  1. {runTarget = "local"
       ? "Run the test-local skill for guided AgentsPlayground testing — your agent isn't deployed yet."
       : "Wait for tenant admin to approve the instance request at admin.cloud.microsoft, then test in Teams."}
  2. Re-run this skill any time — it's idempotent and detects what's already wired.

Row 8 Verify-only sub-case: if the Phase 0C sub-question resolved to "Verify only", skip the deployment bullets entirely and show just the resolved state + a confirmation line:

✅ Everything is already wired. Nothing to do.

  • has_obs:       true (Observability is wired)
  • has_workiq:    true (WorkIQ is wired)
  • disk_blueprint_present:     true (Blueprint ID: {existingBlueprintId})

If you want to push a code change, re-run /agent365:make-ai-teammate and pick "Re-publish" at the row 8 sub-question.

Error Handling

CLI error surfacing: When any CLI command (a365, az, dotnet build, npm, etc.) exits non-zero or prints a warning or error line, show the complete output verbatim in a fenced code block before suggesting a fix. Do not abstract, paraphrase, or discard CLI output — the exact error message is always more useful than a summary. If the error is not in the table below, display it and ask the user how to proceed.

Idempotency

On re-runs, read the detection cache (Phase 0A) and skip phases where patterns are already present. Never overwrite a file that already has the required pattern — only add what is missing.

References

Node.js patterns:

• ${CLAUDE_PLUGIN_ROOT}/skills/make-ai-teammate/references/nodejs-ai-teammate.md
• ${CLAUDE_PLUGIN_ROOT}/skills/make-ai-teammate/references/nodejs-notifications.md

.NET patterns:

• ${CLAUDE_PLUGIN_ROOT}/skills/make-ai-teammate/references/dotnet-ai-teammate.md

Python patterns:

• ${CLAUDE_PLUGIN_ROOT}/skills/make-ai-teammate/references/python-ai-teammate.md

Deploy pipeline (Phase 9.7 — language-agnostic):

• ${CLAUDE_PLUGIN_ROOT}/skills/make-ai-teammate/references/deploy-pipeline.md

Shared:

• ${CLAUDE_PLUGIN_ROOT}/shared/agent-detection.md