id: predict_analysis aliases:

• swarm_intelligence name: Predict Analysis tagline: Multi-persona debate description: > Runs a team of 50 LLM personas through a 30-round structured debate to predict market direction. Uses the Canvas Agent Swarm Service — the same shared infrastructure other skills use for smaller agent teams. Output is an influence-weighted consensus direction + trade recommendation with a transparent record of every argument, research query, and cross-examination. version: 2.0.0 author: Vibe Trade Core category: simulation icon: users color: "#8b5cf6" tools:
• simulation.run_debate
• simulation.set_debate
• simulation.reset
• bottom_panel.activate_tab
• bottom_panel.set_data
• data.fetch_market
• notify.toast output_tabs:
• id: dag_graph label: DAG Graph component: DAGGraphTab
• id: personalities label: Personalities component: PersonalitiesTab
• id: debate_thread label: Debate Thread component: DebateThreadTab
• id: run_stats label: Run Stats component: RunStatsTab store_slots:
• currentDebate
• debateHistory input_hints: placeholder: "Predict direction via multi-persona debate..." supports_fingerprint: false

Predict Analysis Skill

Previously known as swarm_intelligence. The skill id swarm_intelligence is retained as an alias for backward compatibility. See docs/PREDICT_ANALYSIS.md for the full technical walkthrough.

The team

This skill uses the largest team of any skill — 50 agents in total — orchestrated via the shared Agent Swarm Service (core/engine/agent_swarm.py).

All coordination — parallelism, timeouts, retries, event recording — is handled by the Agent Swarm Service, not this skill.

Pipeline (5 stages, ~10-30 minutes total)

1. Context Analysis — classify asset + extract market context + build 6 specialisation data feeds
2. Intelligence Gathering — 4 web searches → synthesise bull/bear briefing
3. Persona Generation — 50 personas with distinct backgrounds, biases, influence weights, specialisations, tool access
4. Iterative Research — each persona plans its own research queries (min 3, max 8) using their assigned tools
5. Multi-Round Debate — 30 rounds × 15 speakers with per-agent memory + selective thread routing
6. Cross-Examination — press the 6-8 most divergent personas with targeted questions
7. ReACT Report — synthesise + apply influence-weighted consensus math

Multi-chart (portfolio) mode

When the Canvas has multiple chart windows, the focused chart is the primary asset (drives the full pipeline); siblings are summarised into the intel briefing as portfolio context. Personas reference them naturally in their arguments.

See docs/PREDICT_ANALYSIS.md § 5 for the processor-level normalisation (focused → index 0, missing-dataset warnings, etc.).

Tool calls emitted

Output tabs

Input

• Natural language: "run a swarm debate on BTC", "predict direction for CL=F", "what does the committee think about AAPL?"
• Requires at least one dataset loaded on the Canvas.
• Optional: message text is passed through as additional context into every persona's prompt.

Known limitations

See docs/PREDICT_ANALYSIS.md § 13. Summary: no streaming (user waits for full 10-30 min run), no persona caching (every run regenerates), global DDG rate limiter serialises web searches, no cross-session memory.