science-superpowers

v0.1.0

An agentic skills framework & computational-science methodology that works: research framing, pre-registration, reproducible analysis, anomaly investigation, and red-team review.

CodexClaude CodeAgent Plugins16 Skills

By K-Dense, Inc.License: MIT347 GitHub starsUpdated last month

Directory evidence

Runtimes: Codex, Claude Code, and Agent Plugins
Parsed components: 16 skill or MCP entries
Source updated: Sep 13, 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 science-superpowers for Codex and Claude Code

Installs for the current user

codex plugin marketplace add IchenDEV/agent-plugin-mkt
codex plugin marketplace upgrade agent-plugin-marketplace
codex plugin add science-superpowers@agent-plugin-marketplace

Paste and run these commands in a terminal with Codex. 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/science-superpowers

Clone the source repository, then follow its setup instructions to add the plugin to a compatible client. The repository root is the plugin root.

Plugin files

science-superpowers/

├── .codex-plugin/plugin.json
├── .claude-plugin/plugin.json
├── plugin.json
├── skills/designing-the-analysis/SKILL.md
├── skills/dispatching-parallel-investigations/SKILL.md
├── skills/establishing-feasibility-first/SKILL.md
├── skills/executing-analysis/SKILL.md
├── skills/framing-research-questions/SKILL.md
├── skills/investigating-anomalous-results/SKILL.md
├── skills/preregistering-analysis/SKILL.md
├── skills/receiving-critical-review/SKILL.md
├── skills/reporting-and-archiving-findings/SKILL.md
├── skills/requesting-red-team-review/SKILL.md
├── skills/setting-up-reproducible-analysis/SKILL.md
├── skills/subagent-driven-analysis/SKILL.md
├── skills/surveying-prior-work/SKILL.md
├── skills/using-science-superpowers/SKILL.md
├── skills/verifying-results-before-claiming/SKILL.md
└── skills/writing-science-skills/SKILL.md

Included Skills16

designing-the-analysisskills/designing-the-analysis/SKILL.md

Use when you have an approved research question and need a concrete analysis plan, before touching outcome data or fitting any model

dispatching-parallel-investigationsskills/dispatching-parallel-investigations/SKILL.md

Use when facing 2+ independent investigations that can proceed without shared state - parallel literature survey, multi-dataset replication, or pre-specified robustness checks

establishing-feasibility-firstskills/establishing-feasibility-first/SKILL.md

Use when your human partner has explicitly opted into exploratory or feasibility mode - a compute-heavy simulation, an unproven pipeline, an unbenchmarked solver, an untested cluster job - or when whether the work can run at all is still unknown, when a plan's largest configuration has never been executed, when a memory or wall-clock ceiling is estimated rather than measured, or when someone proposes pre-registering over runs that have never happened

executing-analysisskills/executing-analysis/SKILL.md

Use when you have a pre-registered analysis plan to execute inline in this session with review checkpoints, on a platform without subagents

framing-research-questionsskills/framing-research-questions/SKILL.md

You MUST use this before any data analysis or investigation - before exploring a dataset, loading or profiling data, running a model, computing a statistic, or testing an idea, and before any outcome data is touched

investigating-anomalous-resultsskills/investigating-anomalous-results/SKILL.md

Use when a result is surprising, impossible, contradicts a sanity check, a pipeline fails, a model won't converge, or a replication fails - before adjusting anything

preregistering-analysisskills/preregistering-analysis/SKILL.md

Use before running any confirmatory analysis or looking at outcome data, when testing a hypothesis, computing a p-value, or about to claim an effect - locks predictions and decision rules before results are seen

receiving-critical-reviewskills/receiving-critical-review/SKILL.md

Use when receiving critical feedback on an analysis or manuscript, before implementing suggestions, especially if feedback seems unclear or methodologically questionable - requires verification, not performative agreement or blind changes

reporting-and-archiving-findingsskills/reporting-and-archiving-findings/SKILL.md

Use when an analysis is complete and verified, and you need to decide how to report it and archive the work for reproducibility

requesting-red-team-reviewskills/requesting-red-team-review/SKILL.md

Use after completing an analysis or before reporting a finding, to have a skeptical reviewer attack the conclusion before you believe it

setting-up-reproducible-analysisskills/setting-up-reproducible-analysis/SKILL.md

Use when starting analysis work that needs isolation, or before executing a pre-registered plan - ensures an isolated, reproducible workspace with pinned environment, fixed seeds, and immutable raw data

subagent-driven-analysisskills/subagent-driven-analysis/SKILL.md

Use when executing a pre-registered analysis plan with mostly independent steps in the current session

surveying-prior-workskills/surveying-prior-work/SKILL.md

Use after framing a question and before designing an analysis, or when choosing a method, judging whether a result is novel, or needing a prior effect size for a power calculation

using-science-superpowersskills/using-science-superpowers/SKILL.md

Use when starting any conversation - establishes how to find and use skills, requiring Skill tool invocation before ANY response including clarifying questions

verifying-results-before-claimingskills/verifying-results-before-claiming/SKILL.md

Use when about to claim a result, effect, significance, or that an analysis reproduces, before reporting or writing it up - requires running the analysis fresh and reading the actual output first; evidence before claims always

writing-science-skillsskills/writing-science-skills/SKILL.md

Use when creating new skills, editing existing skills, or verifying skills work before deployment

Plugin manifests3

.codex-plugin/plugin.json

{
  "name": "science-superpowers",
  "version": "0.1.0",
  "description": "An agentic skills framework & computational-science methodology that works: research framing, pre-registration, reproducible analysis, anomaly investigation, and red-team review.",
  "author": {
    "name": "K-Dense, Inc."
  },
  "homepage": "https://github.com/K-Dense-AI/science-superpowers",
  "repository": "https://github.com/K-Dense-AI/science-superpowers",
  "license": "MIT",
  "keywords": [
    "research-framing",
    "pre-registration",
    "subagent-driven-analysis",
    "skills",
    "reproducibility",
    "statistics",
    "anomaly-investigation",
    "workflow"
  ],
  "skills": "./skills/",
  "interface": {
    "displayName": "Science Superpowers",
    "shortDescription": "Framing, pre-registration, reproducible analysis, and review workflows for research agents",
    "longDescription": "Use Science Superpowers to guide agent work through research framing, prior-work survey, analysis design, pre-registration, reproducible execution, anomaly investigation, results verification, red-team review, and reporting workflows.",
    "developerName": "K-Dense, Inc.",
    "category": "Science",
    "capabilities": [
      "Interactive",
      "Read",
      "Write"
    ],
    "defaultPrompt": [
      "I've got a research question I'd like to investigate.",
      "Let's analyze this dataset."
    ],
    "websiteURL": "https://github.com/K-Dense-AI/science-superpowers",
    "privacyPolicyURL": "https://docs.github.com/en/site-policy/privacy-policies/github-general-privacy-statement",
    "termsOfServiceURL": "https://docs.github.com/en/site-policy/github-terms/github-terms-of-service",
    "brandColor": "#2563EB",
    "composerIcon": "./assets/science-superpowers-small.svg",
    "logo": "./assets/app-icon.png",
    "screenshots": []
  }
}

.claude-plugin/plugin.json

{
  "name": "science-superpowers",
  "description": "Computational-science methodology for Claude Code: research framing, pre-registration, reproducible analysis, anomaly investigation, and red-team review",
  "version": "0.1.0",
  "author": {
    "name": "K-Dense, Inc."
  },
  "homepage": "https://github.com/K-Dense-AI/science-superpowers",
  "repository": "https://github.com/K-Dense-AI/science-superpowers",
  "license": "MIT",
  "keywords": [
    "skills",
    "science",
    "research",
    "pre-registration",
    "reproducibility",
    "statistics",
    "data-analysis",
    "workflows"
  ]
}

plugin.json

{
  "$schema": "https://agent-plugins.org/schemas/1.0.0/plugin.schema.json",
  "name": "science-superpowers",
  "version": "0.1.0",
  "description": "Computational-science methodology for research agents: question framing, prior-work survey, analysis design, pre-registration, reproducible execution, anomaly investigation, results verification, and red-team review.",
  "author": {
    "name": "K-Dense, Inc.",
    "url": "https://www.k-dense.ai"
  },
  "homepage": "https://github.com/K-Dense-AI/science-superpowers",
  "repository": "https://github.com/K-Dense-AI/science-superpowers",
  "license": "MIT",
  "keywords": [
    "skills",
    "science",
    "research",
    "pre-registration",
    "reproducibility",
    "statistics",
    "data-analysis",
    "workflows"
  ]
}

For maintainers

If you maintain this plugin, link to this source-backed listing from your README so users can review its manifest and indexed components.

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