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damusix/skills

arm-bandits-expert

Implements, evaluates, and deploys multi-armed bandit algorithms — including Thompson Sampling, UCB, epsilon-greedy, LinUCB, EXP3, and contextual bandits. Covers algorithm selection, experiment harnesses, offline evaluation (IPS, Doubly Robust), infrastructure patterns, and correctness verification. Use when the user asks about multi-armed bandits, exploration-exploitation tradeoffs, adaptive experiments, A/B testing alternatives, online optimization, bandit-based recommendation or personalizat…

First seen Mar 30, 2026

Installation

$ npx skills add damusix/skills --skill arm-bandits-expert
View on skills.sh ↗ GitHub repository ↗

Summary

  • Implements, evaluates, and deploys multi-armed bandit algorithms — including Thompson Sampling, UCB, epsilon-greedy, LinUCB, EXP3, and contextual bandits.
  • Covers algorithm selection, experiment harnesses, offline evaluation (IPS, Doubly Robust), infrastructure patterns, and correctness verification.
  • Use when the user asks about multi-armed bandits, exploration-exploitation tradeoffs, adaptive experiments, A/B testing alternatives, online optimization, bandit-based recommendation or personalization systems, or contextual bandits.

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
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Gemini CLI Not declared
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Repository health

Stars 63
Default branch main
Open issues 0
Status Active

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 5,740 B
  • docs SUMMARY.md 562 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 8 installs

SKILL.md

Multi-Armed Bandits Expert

Guide the user based on their actual need. Do not lecture; respond to what they're doing.

Routing

Assess the user's situation and route to the appropriate reference material. Read the relevant file(s) from skill/references/ before responding.

Entry Paths

"I need to learn about bandits" → Start with tier-1-core-algorithms.md. Progress to tier 2/3 only when the user is ready or asks.

"I need to pick an algorithm" → Use the decision framework below, then read the relevant tier reference for details.

"I need to build/evaluate an experiment" → Read experiment-harness-patterns.md for environment, policy, runner, and offline evaluation abstractions.

"I need to review/debug an implementation" → Read infrastructure-patterns.md for testing patterns and bug checklists. Cross-reference the relevant algorithm tier for formula verification.

"I need to deploy to production" → Read infrastructure-patterns.md for serving, reward pipelines, monitoring, and safety guardrails.

"I need to understand the business case" → Read business-applications.md for domain-specific guidance, real-world examples, and ROI evidence from 60+ named company deployments.

Decision Framework — Picking an Algorithm

Present trade-offs. Never prescribe a single "best" algorithm without context.

Step 0: Does the user specify an algorithm?

If the task names a specific algorithm (e.g. "implement UCB1", "use epsilon-greedy"), implement that algorithm — do not substitute a different one. Only use this decision framework when the user asks for help choosing an algorithm or says something generic like "implement a bandit."

Step 1: What kind of rewards?

Reward type Candidates
Binary (click/no-click) UCB1 is the classical default; Thompson Sampling (Beta-Bernoulli) for best empirical performance; epsilon-greedy for simplicity
Continuous (revenue, time) UCB1, Thompson Sampling (Gaussian/NIG), LinUCB
Adversarial / non-stationary EXP3, SW-UCB, D-UCB, change-point detectors

Step 2: Do you have context features?

Context Candidates
No context Epsilon-greedy, UCB1, Thompson Sampling, Softmax
User/item features available LinUCB, contextual Thompson Sampling
High-dimensional features Neural bandits (NeuralUCB/NeuralTS, last-layer Bayesian)

Step 3: What's your constraint?

Constraint Recommendation
Simplest possible baseline Epsilon-greedy with decay
Strongest theoretical guarantees UCB1 (stochastic), EXP3 (adversarial)
Best empirical performance Thompson Sampling
Delayed feedback Thompson Sampling (robust to stale posteriors)
Multiple items per round Combinatorial bandits (CUCB + oracle)
Ranked lists with position bias Cascading bandits (CascadeUCB1, CascadeLinTS)
Arms change state over time Restless bandits (Whittle index)
Reward distributions shift Non-stationary bandits (SW-UCB, GLR-UCB)

Step 4: Maturity reality check

Algorithm Maturity Production examples
Epsilon-greedy Battle-tested Optimizely, Kameleoon
UCB1 Battle-tested Widespread
Thompson Sampling Battle-tested Yahoo, Stitch Fix, Doordash
LinUCB Production-proven Yahoo News, Netflix, Spotify
EXP3 Well-established Adversarial settings
Bayesian UCB Production-proven River, MABWiser
Softmax Battle-tested Deep RL action selection
Neural bandits Early production Meta ENR (9%+ CTR lift)
Non-stationary (SW/D/GLR-UCB) Well-established SMPyBandits, monitoring
Combinatorial bandits Research Ad placement (Chen et al.)
Restless bandits Research → applied Health interventions (Armman)
Cascading bandits Early production Expedia homepage ranking

Build Phases

  1. Core Library — Implement algorithms starting with tier 1 (epsilon-greedy or Thompson Sampling). See tier-1-core-algorithms.md through tier-3-production-algorithms.md.
  2. Experiment Harness — Build environment, runner, and metrics to compare algorithms offline. See experiment-harness-patterns.md.
  3. Production Infrastructure — Add reward pipelines, serving, monitoring, safety guardrails. See infrastructure-patterns.md.

Reference Files

File Contents
references/tier-1-core-algorithms.md Epsilon-greedy, UCB1, Thompson Sampling — pseudocode, properties, pitfalls
references/tier-2-practical-algorithms.md LinUCB, EXP3, Bayesian UCB, Softmax — context handling, adversarial robustness
references/tier-3-production-algorithms.md Neural, combinatorial, non-stationary, restless, cascading bandits
references/experiment-harness-patterns.md Environment, policy, runner abstractions, metrics, offline evaluation
references/infrastructure-patterns.md Project structure, testing, reward pipelines, serving, monitoring, safety
references/business-applications.md Business decision framework, domain guides, algorithm-to-problem mapping, ROI evidence, failure modes