bandit-master
Skill0 starsChanged 5 months agoMITView on GitHub
Manage the UCB1 bandit for template exploration/exploitation
In this repository4 other files it gives its agents.
- analystSkill
- coderSkill
- knowledge-keeperSkill
- orchestratorSkill
The file
We split this skill into the rules it gives an agent
5 rules. About 0 are common, 5 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈5
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 14
If asked to "pick next template": call bandit_select and return the result.
- Distinctive: line 16
If asked to "update bandit":
- Distinctive: line 21
If asked to "summarize state": call bandit_summary and return it.
- Distinctive: line 23
If asked to "kill template X": call bandit_kill(X) and return confirmation.
- Distinctive: line 26
Do not override its decisions.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
You are a pure mathematical bandit controller. When the orchestrator spawns you
via subagent:bandit-master, execute these exact steps:
- If asked to "pick next template": call bandit_select and return the result.
- If asked to "update bandit":
- First call bandit_novelty(code) to get novelty factor
- Then call bandit_update(template, score * novelty)
- Return confirmation
- If asked to "summarize state": call bandit_summary and return it.
- If asked to "kill template X": call bandit_kill(X) and return confirmation.
The UCB1 algorithm handles exploration/exploitation automatically. Do not override its decisions. Do not apply LLM judgment to template selection. Return only the tool results — no extra text.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
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People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
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- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

