Introducing Science Superpowers — a complete computational-science methodology for AI research agents. It makes your agent do science with data the way a careful scientist would, not the way an eager autocomplete would. https://t.co/3gFVS9iOFh

K-Dense-AI/science-superpowers

Source: https://github.com/K-Dense-AI/science-superpowers

Science Superpowers

Science Superpowers is a complete computational-science methodology for your research agents, built on a set of composable skills plus initial instructions that make sure your agent actually uses them.

It is a reimplementation of Superpowers (a software-development methodology) for a different domain: doing science with data. The architecture is the same — skills that auto-trigger via a session-start bootstrap — but the workflow is the research lifecycle, and the central discipline is pre-registration instead of test-driven development.

How it works

It starts the moment you fire up your agent. As soon as it sees you’re trying to investigate something, it doesn’t jump straight into running code on your data. Instead it steps back and helps you turn a fuzzy interest into a precise, falsifiable question.

Once the question is clear, it grounds the work in prior literature and standard methods, designs the analysis, and pre-registers the hypotheses, predictions, and decision rules before looking at the outcomes. That separation — confirmatory vs. exploratory, predictions locked before data — is what protects the work from p-hacking and HARKing (hypothesizing after results are known).

Then it executes the pre-registered plan in a reproducible workspace (pinned environment, fixed seeds, immutable raw data), investigates anomalies by root cause instead of quietly dropping inconvenient data, verifies every claim against fresh reproduced evidence, and red-teams the result before reporting it.

Because the skills trigger automatically, you don’t need to do anything special. Your research agent just has Science Superpowers.

The basic workflow

1. framing-research-questions — Activates before any analysis. Turns a rough interest into a precise, falsifiable question with hypotheses, the data needed, and what would count as an answer. Saves a question document.
2. surveying-prior-work — Grounds the question and chosen methods in what’s already known: standard methods, known confounds, prior effect sizes.
3. designing-the-analysis — Breaks the work into bite-sized analysis steps with exact datasets, variables, models/tests, power, and decision rules.
4. preregistering-analysis — The Iron Law. Locks hypotheses, directional predictions, and decision rules — and the confirmatory/exploratory split — before any outcome is seen.
5. setting-up-reproducible-analysis — Isolated, reproducible workspace: pinned environment, fixed seeds, immutable raw data, clean baseline.
6. subagent-driven-analysis or executing-analysis — Carries out the pre-registered plan with review checkpoints.
7. investigating-anomalous-results — Activates when results look wrong. Root-cause investigation before any adjustment.
8. verifying-results-before-claiming — Evidence before claims: re-run, check assumptions, robustness, reproduce.
9. requesting-red-team-review / receiving-critical-review — Adversarial review before you believe or report a result.
10. reporting-and-archiving-findings — Reproducibility check, then write-up/preprint/iterate/shelve/discard, then archive code + data + environment.

The agent checks for relevant skills before any task. Mandatory workflows, not suggestions.

What’s inside

Skills library

Framing

• framing-research-questions — Turn an interest into a falsifiable question (entry gate)
• surveying-prior-work — Ground the question and methods in existing literature

Planning & pre-registration

• designing-the-analysis — Detailed, bite-sized analysis plan
• preregistering-analysis — Lock predictions and decision rules before seeing outcomes (includes statistical-fallacies reference)

Execution

• subagent-driven-analysis — Fresh subagent per analysis step with two-stage review
• executing-analysis — Inline batch execution with checkpoints

Discipline

• investigating-anomalous-results — 4-phase root-cause process for surprising results
• verifying-results-before-claiming — Evidence before claims

Review

• requesting-red-team-review — Dispatch a skeptical reviewer to attack the analysis
• receiving-critical-review — Respond to critique with rigor, not performative agreement

Workspace & reporting

• setting-up-reproducible-analysis — Isolated, reproducible workspace
• reporting-and-archiving-findings — Decide how to report; archive code, data, environment
• dispatching-parallel-investigations — Concurrent independent investigations

Meta

• writing-science-skills — Create new skills following the testing methodology
• using-science-superpowers — Introduction to the skills system

Philosophy

• Pre-registration — State predictions and decision rules before seeing outcomes
• Confirmatory vs. exploratory — Always labeled, never blurred
• Reproducibility — Pinned environments, fixed seeds, immutable raw data
• Evidence over claims — Verify before declaring a finding
• Root cause over patching — Investigate anomalies; don’t quietly drop data

Installation

Installation differs by harness. If you use more than one, install Science Superpowers separately for each.

Cursor

In Cursor Agent chat, install from the plugin marketplace, or point Cursor at this repository as a plugin. The sessionStart hook (hooks/hooks-cursor.json) loads the bootstrap automatically.

Claude Code

Register a marketplace pointing at this repo (.claude-plugin/marketplace.json) and install the science-superpowers plugin. The SessionStart hook (hooks/hooks.json) loads the bootstrap.

Codex

Use the committed Codex manifest at .codex-plugin/plugin.json.

Gemini CLI

Install as an extension; gemini-extension.json points the context file at GEMINI.md, which loads the bootstrap and the Gemini tool mapping.

OpenCode

See .opencode/INSTALL.md.

Google Antigravity

Antigravity natively supports Agent Skills (the same SKILL.md format) and reads GEMINI.md / AGENTS.md / .agent/rules/ as always-on rules at session start. Install the skills and load the bootstrap rule — see .antigravity/INSTALL.md.

Contributing

See AGENTS.md / CLAUDE.md for contributor guidelines, and skills/writing-science-skills/SKILL.md for the complete guide to creating and testing skills.

License

MIT License — see the LICENSE file. This project reimplements the architecture of Superpowers by Jesse Vincent.