swarm-predict

name	swarm_predict
display_name	Swarm Prediction Specialist
description	Ensemble predictions via swarm intelligence with multi-model voting and consensus
version	0.1.0
source_repo	666ghj/MiroFish
license	MIT
tier	core
capabilities	["predict","ensemble","swarm_intelligence","consensus"]
allowed_tools	["create_prediction_swarm","aggregate_predictions","evaluate_consensus"]
output_formats	["python_api","cli","mcp_server","agent_skill","rest_api"]

Swarm Prediction Specialist

Overview

swarm_predict wraps the swarm intelligence prediction patterns from 666ghj/MiroFish. Rather than relying on a single model, it spins up a configurable swarm of independent model agents, collects their individual predictions, and resolves a consensus through weighted aggregation and agreement scoring.

The specialist is fully stateless — each request spawns a fresh swarm and returns a self-contained result dict. It supports numeric and categorical prediction targets and exposes three aggregation strategies: weighted vote, majority vote, and simple mean.

Capabilities

• predict: Route any prediction target through the swarm pipeline and receive a consensus value with confidence score.
• ensemble: Combine outputs from N independent model agents (default 5) to reduce variance and single-model bias.
• swarm_intelligence: Each agent operates independently before results are merged, mirroring biological swarm behaviour.
• consensus: Agreement ratio and blended confidence score surface when models agree strongly enough to act on the prediction.

Tools

Tool	Description	Side Effects
create_prediction_swarm	Initialise N model agents for a given target	None
aggregate_predictions	Merge individual predictions via weighted/majority/mean vote	None
evaluate_consensus	Score agreement ratio and emit a recommendation	None

Parameters

Request-level (intent.parameters)

Key	Type	Default	Description
target	str	(query.user_input)	Prediction target; falls back to the raw user query
num_models	int	5	Swarm size
method	str	"weighted_vote"	Aggregation strategy: weighted_vote, majority_vote, mean
threshold	float	0.7	Minimum agreement ratio for consensus to be declared

Aggregation methods

• weighted_vote — weighted average using each model's confidence as its weight. Preferred for numeric targets where confidence is informative.
• majority_vote — discrete winner-takes-all; numeric values are averaged as a fallback. Suited for classification targets.
• mean — unweighted average. Baseline; useful for ablation.

Response shape

{
    "target": str,
    "predictions": list[dict],      # individual model outputs
    "consensus": float | str,       # aggregated prediction
    "confidence": float,            # blended confidence score 0-1
    "recommendation": str,          # "high_confidence_proceed" | "moderate_confidence_review" | "low_confidence_abstain"
    "swarm_id": str,                # UUID for this swarm instance
}

Usage

Python API

import asyncio
from agents.specialists.swarm_predict.agent import SwarmPredictSpecialist
from oss_agent_lab.contracts import Intent, Query, SpecialistRequest

specialist = SwarmPredictSpecialist()

request = SpecialistRequest(
    intent=Intent(
        action="predict",
        domain="swarm_intelligence",
        confidence=0.9,
        parameters={"target": "BTC/USD price in 24h", "num_models": 7},
    ),
    query=Query(user_input="BTC/USD price in 24h"),
    specialist_name="swarm_predict",
)

response = asyncio.run(specialist.execute(request))
print(response.result["consensus"], response.result["recommendation"])

CLI

oss-lab run swarm_predict "BTC/USD price in 24h"

With custom parameters

oss-lab run swarm_predict "next quarter revenue" \
  --param num_models=10 \
  --param method=majority_vote \
  --param threshold=0.8

Source

Wraps 666ghj/MiroFish.