Runs an Agent 365 AI Teammate agent locally and opens AgentsPlayground for interactive local testing. Works with any AI Teammate stack — .NET (AgentFramework, Semantic Kernel), Node.js (LangChain, OpenAI, Claude SDK, Semantic Kernel, Google ADK), or Python (AgentFramework, LangChain, OpenAI, Claude, Semantic Kernel, Google ADK). Checks prerequisites (agentsplayground CLI, build tools), builds the agent, starts it in the background, and launches the playground UI pointed at the local endpoint. AgentsPlayground connects to /api/messages over HTTP — the LLM framework does not matter.
Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.
Quelldateien prüfen
Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
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Runs an Agent 365 AI Teammate agent locally and opens AgentsPlayground for interactive local testing. Works with any AI Teammate stack — .NET (AgentFramework, Semantic Kernel), Node.js (LangChain, OpenAI, Claude SDK, Semantic Kernel, Google ADK), or Python (AgentFramework, LangChain, OpenAI, Claude, Semantic Kernel, Google ADK). Checks prerequisites (agentsplayground CLI, build tools), builds the agent, starts it in the background, and launches the playground UI pointed at the local endpoint. AgentsPlayground connects to /api/messages over HTTP — the LLM framework does not matter.
{"preToolUse":[{"type":"command","command":"node ${CLAUDE_PLUGIN_ROOT}/hooks/preToolUse/path-guard.js","timeout":5000}],"stop":[{"type":"command","command":"node ${CLAUDE_PLUGIN_ROOT}/hooks/stop/validate-test-local.js","timeout":15000},{"type":"prompt","prompt":"Before ending, verify ALL of the following:\n1. Agent type was detected (.NET, Node.js, or Python — any AI Teammate stack).\n2. agentsplayground CLI was verified as installed (or installed if missing).\n3. Agent project built successfully (dotnet build, npm install, or pip install).\n4. User was shown the launch commands for agent + AgentsPlayground.\nIf any item is incomplete, return {\"ok\": false, \"reason\": \"<specific item>\"}.\nIf all items are complete, or the user declined to launch, return {\"ok\": true}.\n","timeout":30000}]}
Test Agent Locally (AgentsPlayground)
Trigger phrases — any of these will activate this skill automatically:
"test this agent locally"
"run my agent locally"
"open agentsplayground"
"launch agentsplayground"
"start a local test session"
"debug this agent locally"
"test my agent without deploying to teams"
"spin up a local test"
Overview
This skill starts your agent on localhost and opens AgentsPlayground — a local web UI that
simulates a Teams-like chat interface without requiring a deployment or Bot Framework auth.
What it does:
Detects agent language and framework (.NET, Node.js, or Python)
Checks agentsplayground is installed — installs if missing
Builds the agent to confirm there are no compile errors
Starts the agent in the background
Launches AgentsPlayground pointed at the local endpoint
Guides a local test and confirms observability logs are flowing (if instrumented)
Why AgentsPlayground works for all AI Teammate stacks:
Web UI that matches the Teams message format
Connects to /api/messages over HTTP — the LLM framework on the server side does not matter
The -c emulator flag bypasses Bot Framework auth — no extra config needed for any stack
requireAuth: false in MapAgentApplicationEndpoints is the only .NET-specific prerequisite
All actions are read-only against your codebase — no code is modified.
Phase 0 — Create and Display Task List
Show the user this checklist 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 (- [ ] Detect agent type…) and edit items to - [x] as each phase completes.
TaskCreate: "Detect agent type and verify build tools"
TaskCreate: "Install agentsplayground"
TaskCreate: "Build agent"
TaskCreate: "Launch agent and AgentsPlayground"
TaskCreate: "Guide local test"
Phase 1 — Detect Agent Type
Mark task in progress: "Detect agent type and verify build tools"
Read${CLAUDE_PLUGIN_ROOT}/shared/agent-detection.md for detection heuristics.
Check for detection cache. Read .a365-workspace-detection.local.json if it exists. If detectedAt is within the last 60 minutes, load agentStack and programmingLanguage — skip the globs below and go to step 3.
If cache is missing or stale, run detection globs in parallel:
Glob**/*.py or requirements.txt / pyproject.toml → Python
Determine default port:
.NET: 5000 (HTTP, dotnet run default)
Node.js: 3978 (standard Bot Framework port)
Python: 3978 (aiohttp default in AI Teammate hosting layer)
If the user provided a port as the skill argument, use that instead.
Determine start command:
.NET: dotnet run
Node.js: npm start (fall back to npm run dev if start script absent)
Python: detect entry point — check for host_agent_server.py, app.py, or main.py. Detect the Python command: python3 --version 2>/dev/null && echo python3 || echo python. Use python3 if available (macOS/Linux default), otherwise python (Windows). Run with <python-cmd> <entry> (or uvicorn app:app --port 3978 if an ASGI app is detected).
If agent type cannot be determined, stop and ask:
AskUserQuestion:
question: "I couldn't detect the agent type. What are you working with?"
options:
- .NET (AgentFramework or Semantic Kernel)
- Node.js (LangChain, OpenAI, Claude SDK, Semantic Kernel, or Google ADK)
- Python (AgentFramework, LangChain, OpenAI, Claude, Semantic Kernel, or Google ADK)
1.3 — Check language build tool
After detection, immediately verify the required build tool is installed:
For .NET — check dotnet --version. If missing or below 8.0:
AskUserQuestion:
question: "dotnet SDK 8.0+ is required but not found. Install it now?"
options:
- "Yes — install it for me"
- "No — I'll install manually and re-run"
If yes, show the appropriate command for the detected OS and ask the user to run it:
After install, restart the terminal then run dotnet --version to confirm.
For Node.js — check node --version. If missing or below 18:
AskUserQuestion:
question: "Node.js 18+ is required but not found. Install it now?"
options:
- "Yes — install it for me"
- "No — I'll install manually and re-run"
For Python — check python3 --version 2>/dev/null || python --version. If missing or below 3.11:
AskUserQuestion:
question: "Python 3.11+ is required but not found. Install it now?"
options:
- "Yes — install it for me"
- "No — I'll install manually and re-run"
After install, confirm with python3 --version 2>/dev/null || python --version.
Mark task complete: "Detect agent type and verify build tools"
Phase 2 — Install agentsplayground
Mark task in progress: "Install agentsplayground"
Check if installed:
agentsplayground --version
If found, report the version and continue.
If not found, ask the user:
"AgentsPlayground CLI is not installed. Install it now with npm install -g @microsoft/agentsplayground?"
Options: Yes, install it / No, I'll install it manually
If the user chooses Yes:
npm install -g @microsoft/agentsplayground
Verify the install succeeded:
agentsplayground --version
If installation fails or the user chooses No, stop and tell the user:
"Install agentsplayground manually with: npm install -g @microsoft/agentsplayground
then re-run this skill."
Do NOT continue if agentsplayground cannot be confirmed installed.
Mark task complete: "Install agentsplayground"
Phase 3 — Build Agent
Mark task in progress: "Build agent"
For .NET
dotnet build
For Node.js
npm install
npm run build || npm run compile || echo"No build script — skipping compile check"
For Python
# Use pip3 on macOS/Linux, pip on Windows — try pip3 first
pip3 install -r requirements.txt 2>/dev/null || pip install -r requirements.txt || pip install .
# Verify the active Python version (use python3 on macOS/Linux, python on Windows)
python3 --version 2>/dev/null || python --version
If build fails, show the error output and stop:
"Fix the build errors above and re-run this skill to continue."
Do NOT attempt to launch a broken build.
Mark task complete: "Build agent"
Phase 4 — Launch Agent and AgentsPlayground
Mark task in progress: "Launch agent and AgentsPlayground"
4.1 — Ask user before launching
AskUserQuestion:
question: "Ready to start the agent and open AgentsPlayground for a local test?"
options:
- "Yes — start agent and open playground"
- "No — show me the commands and I'll run them manually"
4.2 — If yes: launch
Inform the user:
Two terminals are needed. Starting both now.
Start the agent (terminal 1) — command varies by language:
.NET: dotnet run
Node.js: npm start (fall back to npm run dev)
Python: python3 <entry-point> on macOS/Linux (e.g. python3 host_agent_server.py); python <entry-point> on Windows
Note for .NET: The -c "emulator" flag bypasses Bot Framework auth.
This works because MapAgentApplicationEndpoints with requireAuth: false skips
token validation for local traffic. No config changes are needed for Node.js or Python.
Poll until the agent responds (max ~20 s), then launch AgentsPlayground — same command for all stacks:
# Poll until agent responds (works on Windows/macOS/Linux — no seq dependency)for i in 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20; do
curl -s --max-time 1 "http://localhost:<port>/api/messages" > /dev/null 2>&1 && breaksleep 1
done
agentsplayground -e "http://localhost:<port>/api/messages" -c "emulator"
4.3 — If no: show commands
Present the commands for the user to run in two terminals:
Terminal 1 — start your agent:
.NET: dotnet run
Node.js: npm start
Python (macOS/Linux): python3 host_agent_server.py (or python3 app.py / python3 main.py)
Python (Windows): python host_agent_server.py (or python app.py / python main.py)
Terminal 2 — open AgentsPlayground (same for all stacks and platforms):
agentsplayground -e "http://localhost:<port>/api/messages" -c "emulator"
Mark task complete: "Launch agent and AgentsPlayground"
Phase 5 — Guide Local Test
Mark task in progress: "Guide local test"
Tell the user:
AgentsPlayground is open. Send a message in the chat window to test your agent.
What to watch for:
Agent responds — confirms the messaging stack is wired correctly.
Terminal logs — if observability is instrumented, look for these signals in this order:
Agent365Exporter: Exporting batch of N spans.
[Agent365Exporter] M non-genAI spans filtered out
[Agent365Exporter] Partitioned into K identity groups (X spans skipped)
Agent365ExporterCore: Obtained token for agent <agentId> tenant <tenantId>.
Agent365ExporterCore: Sending chunk 1 of 1 (J spans, B bytes)
to https://agent365.svc.cloud.microsoft/observability/tenants/<tenant>/otlp/agents/<agent>/traces?api-version=1.
Agent365ExporterCore: HTTP 200 exporting spans. 'x-ms-correlation-id': '<guid>'.
The HTTP 200 exporting spans line is the definitive confirmation that traces reached the A365 backend.
Partitioned into K identity groups should show K >= 1 for at least one batch after a Teams turn — if it's always 0, the agent/tenant ID is missing from baggage (likely the Guid.Empty fallback bug — verify instrument-observability was followed correctly).
To see these logs you need Microsoft.Agents.A365.Observability: Debug (or lower) in appsettings.json's Logging:LogLevel.
To export traces to the A365 service (not just console):
.NET: set EnableAgent365Exporter: true in appsettings.json (SDK defaults to false when key is absent)
Node.js / Python: set ENABLE_A365_OBSERVABILITY_EXPORTER=true in .env
Stopping the agent: Press Ctrl+C in Terminal 1.
If the user reports the agent is not responding, suggest:
Confirm the port matches (check the startup log for the listening URL)
.NET: check MapAgentApplicationEndpoints is called with requireAuth: false
Node.js: check the express listener port in index.ts
Python: check the aiohttp port in host_agent_server.py or app.py
Mark task complete: "Guide local test"
Phase 6 — Final Summary
TaskList — Show all completed tasks.
Present summary:
✅ Local test session ready!
Agent: [language + framework, e.g. Python · AgentFramework]
Agent endpoint: http://localhost:<port>/api/messages
AgentsPlayground: running (emulator mode — no auth required)
Observability check:
If instrumented — watch terminal for:
"Agent365ExporterCore: HTTP 200 exporting spans. 'x-ms-correlation-id': ..."
(and "Partitioned into K identity groups" with K >= 1)
Requires Microsoft.Agents.A365.Observability: Debug in Logging:LogLevel.
To export to A365 service:
.NET: set EnableAgent365Exporter: true in appsettings.json
Node.js/Python: set ENABLE_A365_OBSERVABILITY_EXPORTER=true in .env
To stop: Ctrl+C in the agent terminal.
Error Handling
Situation
Action
agentsplayground install fails
Report error; show manual install command; stop
Build fails
Show error; do not launch; stop
Agent port already in use
Suggest --port <other> or kill the existing process
Playground cannot connect
Check agent is listening; verify port matches
Node.js npm start not found
Try npm run dev; if neither, ask user for start command
Python entry point unclear
Check for host_agent_server.py, app.py, or main.py; ask user if ambiguous
Python port mismatch
Check aiohttp port in entry point; default is 3978 for AI Teammate hosting
