Loading...
Loading...
Found 11 Skills
Targeted Chatroom: Recommend experts based on topics or accept user-specified experts to simulate multi-role conversations. Trigger methods: /dbs-chatroom, /targeted-chatroom, "Targeted Chatroom"
Structured multi-perspective deliberation through adversarial dialogue
Generates YAML signal configs for agent simulation experiments. Use when the user wants to define what signals to track, how to extract them from run artifacts, and how to aggregate them into experiment-level metrics. Trigger when users say: "generate a signal config", "create signals for my experiment", "I want to track [metric]", "write a signal YAML", "set up extraction for [thing]", "how do I measure [behavior] across runs", "configure signals for [experiment]", "create a signal config", "create signal config file", or "build a signal config".
Many turns in one call. Instant communication. No round-trips.
Agent simulation and GEO simulation prompt generation for AI visibility auditing. Use when the user wants to create simulation tasks via the TPC CLI, generate unbranded GEO prompts to test whether AI recommends a product, or run agent simulations.
A skill is documentation that learned to do things.
Run a simulated meeting with multiple expert personas to analyze a subject from diverse perspectives, reach a decision, and propose a solution before implementation. Optionally posts the meeting analysis to a linked GitLab or GitHub issue.
Domain-agnostic strategic decision analysis and wargaming. Auto-classifies scenario complexity: simple decisions get structured analysis (pre-mortem, ACH, decision trees); complex or adversarial scenarios get full multi-turn interactive wargames with AI-controlled actors, Monte Carlo outcome exploration, and structured adjudication. Generates visual dashboards and saves markdown decision journals. Use for business strategy, crisis management, competitive analysis, geopolitical scenarios, personal decisions, or any consequential choice under uncertainty. NOT for simple pros/cons lists, non-strategic decisions, or academic debate.
Fully local multi-agent swarm intelligence simulation engine using Neo4j + Ollama for public opinion, market sentiment, and social dynamics prediction.
Use when you want to verify a site works by having agents act as real users completing common tasks — browsing, subscribing, purchasing, searching. Each agent has a goal and clicks through the real flow using browser MCP tools.
Runs LiveKit agent simulations and acts on the results. Use when the user says "run my simulations", "regression test my agent before deploying", "run the scenarios", "use lk agent simulate", "did my agent pass", "why did this scenario fail", "run simulations in CI", "test the audio pipeline", "check turn-taking and interruptions", or wants to check whole-conversation behavior before shipping. Covers text and audio mode and what each catches, running against a local or deployed agent, degraded-audio flags, automating a pre-release run, and triaging failures with list, view and export. For writing the scenarios use writing-livekit-scenarios. Not the default for a bare "test my agent", which goes to debugging-livekit-agents. Use this skill when the user names simulations, scenarios, a run, CI, or shipping.