spyderweb47 Predict Analysis Skill
Previously known as
swarm_intelligence. The skill idswarm_intelligenceis retained as an alias for backward compatibility. Seedocs/PREDICT_ANALYSIS.mdfor 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).
| Role(s) | Count | What they do |
|---|---|---|
| Asset classifier | 1 | Identifies the asset + its price drivers |
| Context analyser | 1 | Extracts regime + key levels from bars |
| Intelligence gatherer | 1 | Web-searches news / analysis / regulation / indicators |
| Personas (bull/bear/neutral/observer) | 50 | Debate the asset for 30 rounds |
| Cross-examiner | 1 | Probes divergent personas with targeted questions |
| Reporter | 1 | Synthesises final research note |
All coordination — parallelism, timeouts, retries, event recording — is handled by the Agent Swarm Service, not this skill.
Pipeline (5 stages, ~10-30 minutes total)
- Context Analysis — classify asset + extract market context + build 6 specialisation data feeds
- Intelligence Gathering — 4 web searches → synthesise bull/bear briefing
- Persona Generation — 50 personas with distinct backgrounds, biases, influence weights, specialisations, tool access
- Iterative Research — each persona plans its own research queries (min 3, max 8) using their assigned tools
- Multi-Round Debate — 30 rounds × 15 speakers with per-agent memory + selective thread routing
- Cross-Examination — press the 6-8 most divergent personas with targeted questions
- 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
| Tool | When | Purpose |
|---|---|---|
simulation.set_debate |
On completion | Push full debate payload to the store |
bottom_panel.activate_tab |
On completion | Switch to DAG Graph tab |
notify.toast |
On completion | Toast with consensus summary |
Output tabs
| Tab | Shows |
|---|---|
| DAG Graph | React Flow pipeline visualisation |
| Personalities | 50 persona cards; click → full profile + research trail + live /interview chat |
| Debate Thread | Flat list of all messages with sentiment bars + tool chips + agreement references |
| Run Stats | Consensus + briefing + market context + data feeds + cross-exams + convergence chart + PDF export + Run Warnings banner |
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.