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AEGIS: Combining Robot Strategy Game

#146144BGG ↗

2018 · 2-6 players · 120min · weight 2.20 · 178 ratings

v2 v3 v4 wide v4 deep

BGG raw

ID
146144
Name
AEGIS: Combining Robot Strategy Game
Year
2018
Rank
10469
Min players
2
Max players
6
Playing time
120
Min playtime
20
Max playtime
120
Avg weight
2.2
Num weights
10
Bayes avg
5.58108
Average
7.36834
Users rated
178
Num owned
475
Wanting
14
Wishing
154
Num comments
85
Fetched at
Wed Apr 29 2026 05:34:56 GMT+0000 (Coordinated Universal Time)
Mechanisms (6)
Action PointsArea Majority / InfluenceDice RollingGrid MovementModular BoardPlayer Elimination
Categories (2)
Science FictionWargame
Description (1299 chars)

Who doesn't love combining robots? A.E.G.I.S.: Combining Robot Strategy Game is the world’s best combining robot tabletop game! Using themes from all your favorite giant mecha shows, it takes elements from popular tabletop wargames and streamlines them to make a game with the same depth of strategy without the high learning curve, distance measuring and long play times. Players build teams out of five robots to duke it out against other players' teams of five. It's designed to be easy to learn, affordable to buy, and quick to play, setting it apart from other strategy games. There are five different classes of robot: Assault, Evasive, Guard, Intel and Support, and dozens of robots in each of those Classes. Each Class has its own way of playing and interacting with other bots, and you can make a team out of any five of them for an infinite amount of possible strategies! The main mechanism of the game is teambuilding. Each robot on your team shares energy with each other, so moving and attacking with one will limit what the rest of your team can do during your turn. Certain compatible robots can combine into greater forms, too! If your team is built correctly, you can unite all five of your robots together to form something right out of your favorite giant robot cartoon!

LLM v2 (wide)

Not yet enriched at v2 (wide pass).

LLM v3 (deep)

Not yet enriched at v3 (deep pass).

LLM v4 wide (controlled-vocab primitives)

Not yet enriched at v4 (wide pass).

LLM v4 deep (archetype fit)

Not in the v4 deep-pass top-20% slice.