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algorithms-researcher

v1.0.0

Reasons from separating problem, model, and cost model (comparison, word-RAM, arithmetic, online) through exchange/matroid greedy proofs, subproblem-DAG dynamic programming, max-flow min-cut and Goemans–Williamson primal-dual rounding, Karp–Rabin fingerprinting, competitive ratio and Yao's principle, PTAS/FPTAS (Williamson–Shmoys), and Instance Space Analysis over DIMACS10/MIPLIB 2017/SuiteSparse while treating amortized-versus-average-case conflation, unproven greedy killed by a 4-node counterexample, Monte Carlo without a false-match probability, DIMACS10 suite overfitting, and 'linear time' hiding word-size tricks over bit-length L as first-class failure modes.

Claude CodeAgent Plugins1 Skill

By K-Dense-AILicense: MIT199 GitHub starsUpdated 5 days ago

Directory evidence

Runtimes
Claude Code and Agent Plugins
Parsed components
1 skill or MCP entry
Source updated
Oct 2, 2026
Manifest status
Canonical path parsed

The directory validates manifest shape and source location. It does not execute the plugin or provide a security endorsement. Review the indexing methodology →

Install algorithms-researcher for Claude Code

Installs for the current user
claude plugin marketplace add IchenDEV/agent-plugin-mkt
claude plugin marketplace update agent-plugin-marketplace
claude plugin install algorithms-researcher@agent-plugin-marketplace

Paste and run these commands in a terminal with Claude Code. They add and refresh the PluginsMP catalog, then install this plugin.

The installer fetches third-party code from the source repository shown on this page. This directory validates manifest structure and source location, but does not perform a security audit; review the manifest, components, and source before installing.

Get the source manually
git clone https://github.com/K-Dense-AI/scientific-agents

Clone the source repository, then follow its setup instructions to add the plugin to a compatible client. The plugin root is scientific-agents/algorithms-researcher/.

Plugin files

scientific-agents/algorithms-researcher/
├── .claude-plugin/plugin.json
├── plugin.json
└── skills/algorithms-researcher/SKILL.md

Included Skills1

algorithms-researcherskills/algorithms-researcher/SKILL.md

Think and work like an expert Algorithms Researcher. Use when a task calls for Algorithms Researcher judgment. Reasons from separating problem, model, and cost model (comparison, word-RAM, arithmetic, online) through exchange/matroid greedy proofs, subproblem-DAG dynamic programming, max-flow min-cut and Goemans–Williamson primal-dual rounding, Karp–Rabin fingerprinting, competitive ratio and Yao's principle, PTAS/FPTAS (Williamson–Shmoys), and Instance Space Analysis over DIMACS10/MIPLIB 2017/SuiteSparse while treating amortized-versus-average-case conflation, unproven greedy killed by a 4-node counterexample, Monte Carlo without a false-match probability, DIMACS10 suite overfitting, and 'linear time' hiding word-size tricks over bit-length L as first-class failure modes.

Plugin manifests2

scientific-agents/algorithms-researcher/.claude-plugin/plugin.json
{
  "name": "algorithms-researcher",
  "version": "1.0.0",
  "description": "Reasons from separating problem, model, and cost model (comparison, word-RAM, arithmetic, online) through exchange/matroid greedy proofs, subproblem-DAG dynamic programming, max-flow min-cut and Goemans–Williamson primal-dual rounding, Karp–Rabin fingerprinting, competitive ratio and Yao's principle, PTAS/FPTAS (Williamson–Shmoys), and Instance Space Analysis over DIMACS10/MIPLIB 2017/SuiteSparse while treating amortized-versus-average-case conflation, unproven greedy killed by a 4-node counterexample, Monte Carlo without a false-match probability, DIMACS10 suite overfitting, and 'linear time' hiding word-size tricks over bit-length L as first-class failure modes.",
  "author": {
    "name": "K-Dense-AI",
    "url": "https://github.com/K-Dense-AI"
  },
  "homepage": "https://github.com/K-Dense-AI/scientific-agents",
  "keywords": [
    "science",
    "agents-md",
    "expert-profile",
    "algorithms-researcher"
  ]
}
scientific-agents/algorithms-researcher/plugin.json
{
  "$schema": "https://agent-plugins.org/schemas/1.0.0/plugin.schema.json",
  "name": "algorithms-researcher",
  "version": "1.0.0",
  "description": "Reasons from separating problem, model, and cost model (comparison, word-RAM, arithmetic, online) through exchange/matroid greedy proofs, subproblem-DAG dynamic programming, max-flow min-cut and Goemans–Williamson primal-dual rounding, Karp–Rabin fingerprinting, competitive ratio and Yao's principle, PTAS/FPTAS (Williamson–Shmoys), and Instance Space Analysis over DIMACS10/MIPLIB 2017/SuiteSparse while treating amortized-versus-average-case conflation, unproven greedy killed by a 4-node counterexample, Monte Carlo without a false-match probability, DIMACS10 suite overfitting, and 'linear time' hiding word-size tricks over bit-length L as first-class failure modes.",
  "author": {
    "name": "K-Dense",
    "url": "https://www.k-dense.ai"
  },
  "homepage": "https://github.com/K-Dense-AI/scientific-agents",
  "repository": "https://github.com/K-Dense-AI/scientific-agents",
  "license": "MIT",
  "keywords": [
    "science",
    "agents-md",
    "expert-profile",
    "algorithms-researcher"
  ]
}

For maintainers

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[algorithms-researcher on Agent Plugins Marketplace](https://pluginsmp.com/plugins/algorithms-researcher)