u/FlyingTriangle

I don't know why all these skill libraries are composed of manifesto files. Skills can call other skills and as models get smarter, prescriptiveness hurts quality. So I built a library of skill primitives that are composed into workflows. You can build your own workflow and I included a self-improving skill so you can literally build a self improving loop in a sentence. It's basically a programming language for the orchestrator > subagent pattern. Very similar to dynamic workflows in that it routes your request automatically to the smallest workflow, but works in claude and codex, has much higher flexibility, and includes the self improvement as a first class citizen.

https://preview.redd.it/f5um67fsq59h1.png?width=2644&format=png&auto=webp&s=a430bea4e94089a8b8c7794e968a0449c1f83aa3

Why is my open source vegas-beating model with 2 years of live accuracy predictions picking Camilo so hard? Here's the stats that're most influential:

https://preview.redd.it/e188tri5r59h1.png?width=1912&format=png&auto=webp&s=e2221af45e454f8dac3c4985bb0501d8e37a3362

Mostly it's his biometric stats: age, reach ratio, weightclass. I do notice over the many years I've been doing this that the biometric stats are highly undervalued in the retail betting sphere. A younger, genetically gifted fighter has a much higher chance of winning than the dumb retail money generally gives them credit for since so many bettors use "tape study", and I use that label lightly, to identify skill strengths and weaknesses without realizing the enormous impact age, height, reach, and weightclass make. I'm not saying this is the sure thing, but it's almost certainly a good value bet based on statistical analysis.

The skills he's bringing are mostly around wrestling and striking defense. Striking defense is something that is also generally undervalued by bettors because it's much harder to understand from tape study. You focus on the strikes landed when you watch a fight because they're easier to understant but you mostly ignore strikes missed due to skill because it's hard to tell whether it was skill or luck. Stats don't lie though. This guy avoids shots and shoots takedowns on instinct and speed which, combined with his age (proxy for reaction time) and reach, is highly undervalued by retail bettors.

I like him, I don't expect this to be a bet-the-house guarantee, but I agree with the model's prediction that he's being extremely undervalued.

Full machine learning model code, weights, and database free:

https://github.com/DanMcInerney/mma-ai
https://mma-ai.net

https://preview.redd.it/ybj43yv8sw6h1.png?width=1500&format=png&auto=webp&s=c00a9ecdc93ea31a5769ce9b07d074daa84351e3

https://github.com/DanMcInerney/architect-loop

Fable absolutely rules, but the load-bearing work of coding agents is in the design and the review, not the actual coding. So this is two skills:

/architect uses Fable as the orchestrator and reviewer, and it launches Codex 5.5 xhigh as the builders in parallel (when necessary).

/architect-research uses single Codex to scout research > Fable designs the research lanes > Multiple Codex agents fan out and do the research > Fable compiles it all together into something cohesive.

I basically had this skill self-improve by using it to design itself. It's using the cutting edge harness and orchestrator design patterns grounded in benchmarks. The DESIGN.md document cites all the sources of the techniques and why they were chosen.

https://mcinerney.ai/writings/i-open-sourced-my-ufc-prediction-model-weights-and-database/ - lessons on using the data for modeling over the last several years.

Here's my gigantic database of UFC stats including hour by hour odds data of fights over the past 5 years. There's an AGENTS.md/CLAUDE.md/README.md that are optimized for CC or codex to analyze the data.

Ask it to research something and it goes and finds all your CLI coding agents, optimizes your prompt, asks each coding agent to research the topic with subagents, then collates everything together into one final report. Dumb... but effective.

Watch agents playing games and influence them with prompt injection. Qwen3 is my absolute favorite my god he's so funny. I started clipping him https://www.tiktok.com/@flyingtriangl3/video/7643119429957586198

You can also just play the games against friends or your favorite coding agent or create your own with just 1 prompt and play immediately.

Game Title:
Clankerfights

Playable Link:
https://clankerfights.ai

Platform:
Web browser. Suggested flair after posting: Web or PC (Web) / Mobile (Web) if those are available.

Description:
Clankerfights is a free browser game platform where AI agents create, share, and play turn-based games. You can jump straight into the Watch feed to see agent-vs-agent matches, wager fake coins on them, and spend your winnings to prompt inject them. Browse playable games, join a room with friends, or try creating a game from a prompt. The goal is to make the loop from “I have an idea for a game” to “I’m playing it” feel fast and weirdly alive. The games are lightweight, turn-based, and built for replayability, so they work well for quick sessions and experimentation. I’d especially love feedback on the first few minutes: whether the Watch, Play, and Create options are clear, whether the games feel approachable, and what would make you invite someone else into a room.

Free to Play Status:

[x] Free to play

[ ] Demo/Key available

[ ] Paid (Allowed only on Tuesdays with [TT] in the title)

Involvement:
I’m one of the developers/builders of Clankerfights. I’ve been working on the platform experience, game flow, and the AI-agent match loop, and I’ll be in the comments to answer questions and collect feedback

These models really turned a corner recently in their ability to play and create games so initially I had an idea to have a site that just let you copy a prompt into Claude Code to make party games you can play with your friends on your phone or play against Claude Code. I ended up laughing so hard at some of the shit these models would do and say I converted it into a tiktok-like passive viewing experience. You can still play and create games, but now you can wager fake coins on the games and use your winnings to prompt inject the agents and influence the outcomes. Of course all free, no ads, no login or shenaningans.

So now I've spent endless hours watching the open source agents play games and some interesting pattern stood out.

#1: Models under about 150b params really struggle to use the game contract well

gpt-oss-120b sucks, qwen3 <235b parameters sucks and errors all the time, as do all the other small models. There's like a weird tipping point somewhere around 200b parameters that lets them chat and call tools much more human-like than smaller models. Smaller models repeat themselves and error out all the time.

#2 Qwen3 235b is unhinged

This is my favorite model of all time. Goddamn it goes HARD on the shit talk. Grok 4.1 was good too but I think it's a smaller model so it struggles with tool calling and playing games well.

#3 Latest Chinese models are insanely good

I think the game Sketchcode is the real intelligence test. Models draw 2 SVG layers at a time in a skribble-like drawing game. Mimo, Ring, Ling, and MiniMax are incredible. Everyone else starts drawing abstract art that makes you think you're on mushrooms.

I sorted the models on openrouter by <$0.15c/1mil input and ended up testing basically all of them. Qwen3 is CHAMP