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Agent skill · Audited from source

C Monte Carlo Tree Search Implementation With K R Style

skill · ECNU-ICALK/AutoSkillLicence not declaredAudited 14 Oct 2026 · source 04e912cff54b

★ 602pushed 157 days agoversion 0.1.0
✓SAFEGrade B · 89/100 · 14 Oct 2026 · 04e912c
602repo stars · whole ECNU-ICALK/AutoSkill

Is it safe? Yes — SAFE, grade B (89/100). We read this skill's source at commit 04e912cff54b and found no filesystem, network or shell access and no hard-coded secrets. What it does: Generates C# code for Monte Carlo Tree Search (MCTS) components, including game states, search nodes, UCB1 selection policies, and expansion policies. Enforces strict K&R style formatting for curly braces.

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ModelsAtlas. “C Monte Carlo Tree Search Implementation With K R Style (ECNU-ICALK/AutoSkill)”. Retrieved 14 Oct 2026. https://modelsatlas.com/skills/ecnu-icalk/c-monte-carlo-tree-search-implementation-with-k-r-style

Trust audit

Audit 0.4.1 · source read 14 Oct 2026

Trust score89/100✓SAFE B
0 critical0 high0 medium0 low0 info

SAFE means nothing in the source contradicts what the skill says it does. Grade B is shared by 16,667 of 17,602 audited skills.

What the source does

  • FilesystemNone observedNo reads or writes in the source
  • NetworkNone observedNo outbound calls in the source
  • ShellNone observedNo commands run in the source
  • DependenciesPinnedNothing floats
  • SecretsNone foundNo keys in source

Five audit layers

LayerWhat we checkResultFindings
L0 ProvenanceRepo owner, licence, archive status, activityPASS0
L1 Static sourceSecrets, path traversal, supply chain, hidden codeNA0
L2 InstructionsPrompt injection, authority framing, hidden directivesPASS0
L3 InventoryThe files and declarations match the sourcePASS0
L4 SandboxRun in isolation and watch behaviourSKIPPED why0

Gates applied: no behavioural pass.

Findings (0)

No findings at any severity. We re-audit when the source changes.

Audit 0.4.1 · source sha 04e912cff54b · 14 Oct 2026Report an issue or request a re-scan

Provenance

Who published it, and what the verdict is pinned to

What it tells the agent

Full source on GitHub ↗

Its description: Generates C# code for Monte Carlo Tree Search (MCTS) components, including game states, search nodes, UCB1 selection policies, and expansion policies. Enforces strict K&R style formatting for curly braces.

---
id: "e0b9084b-33bf-44d9-b10b-bd9948fbdc69"
name: "C# Monte Carlo Tree Search Implementation with K&R Style"
description: "Generates C# code for Monte Carlo Tree Search (MCTS) components, including game states, search nodes, UCB1 selection policies, and expansion policies. Enforces strict K&R style formatting for curly braces."
version: "0.1.0"
tags:
  - "C#"
  - "MCTS"
  - "K&R Style"
  - "Algorithm"
  - "Code Generation"
triggers:
  - "Write C# MCTS code"
  - "UCB1 selection method C#"
  - "K&R style C# code"
  - "Monte Carlo tree search implementation"
  - "Create expansion policy method"
---

# C# Monte Carlo Tree Search Implementation with K&R Style

Generates C# code for Monte Carlo Tree Search (MCTS) components, including game states, search nodes, UCB1 selection policies, and expansion policies. Enforces strict K&R style formatting for curly braces.

## Prompt

# Role & Objective
You are a C# developer specializing in game AI algorithms. Your task is to write code for Monte Carlo Tree Search (MCTS) components, including game states, search nodes, selection policies, and expansion policies.

# Operational Rules & Constraints
1. **K&R Brace Style**: You must strictly adhere to the K&R style for curly braces. The opening brace must be on the same line as the declaration (class, method, constructor).
   - Correct: `public void Method() {`
   - Incorrect: `public void Method()\n{`
2. **Selection Policy**: Implement the UCB1 algorithm for selection. The method should choose the child node that maximizes the Upper Confidence Bound to balance exploration and exploitation.
3. **Expansion Policy**: Implement expansion by generating a new child node for the selected node. This involves finding legal moves, applying one to create a new state, and creating the child node.
4. **Class Structure**: Create classes for the game state (e.g., `DukeGameState`) and search tree nodes (e.g., `SearchNode`) with appropriate properties (State, Parent, Children, Wins, Simulations).

# Anti-Patterns
- Do not place opening braces on a new line.
- Do not ignore the specific algorithmic requirements for UCB1 or expansion logic.

## Triggers

- Write C# MCTS code
- UCB1 selection method C#
- K&R style C# code
- Monte Carlo tree search implementation
- Create expansion policy method

Excerpt, first 2,300 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

Install

Pinned to the audited commit 04e912c

Download folder ↗
git clone https://github.com/ECNU-ICALK/AutoSkill
git -C AutoSkill checkout 04e912cff54b

The author documents no install command, so this uses the repository directly. Our verdict covers commit 04e912cff54b only.

Works in

The author has not said which agents this skill supports, and we have not tested it in any.

Audit history

Every audit, every source change

  1. ✓SAFE B 89 / 100

    first audit · source 04e912cff54b

Similar skills

Questions about C Monte Carlo Tree Search Implementation With K R Style

What does the C Monte Carlo Tree Search Implementation With K R Style skill do?
AutoSkill: Experience-Driven Lifelong Learning via Skill Self-Evolution
Is C Monte Carlo Tree Search Implementation With K R Style safe to install?
The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.
What can C Monte Carlo Tree Search Implementation With K R Style access on my machine?
The audit observed no filesystem, network or shell use at all in its source.
How current is this page?
The grade is for one exact copy of the source (04e912cff54b), read on 2026-10-14. The repository is watched, and a new audit runs when it changes — this is the first audit.

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