This is the execution half of the science superpowers workflow. You load your frozen pre-registration, review it critically for feasibility issues, then run every step exactly as written with validation checkpoints throughout. The key discipline is stopping immediately when results look weird or you're tempted to deviate from the plan, because silent tweaks destroy confirmatory status. It includes built-in verification steps and pauses after each major section to report back. The docs emphasize this is the fallback for platforms without subagents, since fresh eyes catch more protocol violations during review. If you're doing any analysis where you want to distinguish confirmatory findings from exploratory ones, the rigor here is the whole point.

GitHub stars
202
last updated
Jun 1, 2026

Install

$ npx -y skills add k-dense-ai/science-superpowers --skill executing-analysis --agent claude-code

Installs into .claude/skills of the current project.

Files

Executing the Analysis

Overview

Load the pre-registered plan, review it critically, execute all steps exactly as registered, report when complete.

Announce at start: "I'm using the executing-analysis skill to run this analysis."

Note: Tell your human partner that Science Superpowers works much better with access to subagents — the protocol-compliance and rigor reviews catch more when run by fresh agents. If subagents are available, use science-superpowers:subagent-driven-analysis instead.

Prerequisite

The plan MUST be pre-registered and frozen (science-superpowers:preregistering-analysis) and the workspace set up (science-superpowers:setting-up-reproducible-analysis). If either is missing, stop and do it first. Executing before freezing turns the analysis exploratory.

The Process

Step 1: Load and Review

Sponsors

Details

Repository
github.com/k-dense-ai/science-superpowers
Category
Automation & Workflows
First seen
Jun 11, 2026

More from k-dense-ai/science-superpowers

All 15
1kaelio avatarwiki_capturekaelio/ktxktx's knowledge base - wiki pages for durable, reusable business knowledge. Covers capture workflow for user preferences, metric definitions, organizational con0skills.sh installs
2kimtth avataradd-new-entrykimtth/awesome-azure-openai-llmWorkflow and tools for adding new entries from temp.md to the section files. Includes legend format, section reference, code tools, and common pitfalls. USE FOR0skills.sh installs
3kimtth avatarupdate-app-countkimtth/awesome-azure-openai-llmWorkflow for updating the popular LLM applications pool (section/x_llm_apps.md) using get_app_list_by_github_star.py. Covers full refresh, alternate exports, to0skills.sh installs
4kimtth avatarupdate-llm-poolkimtth/awesome-azure-openai-llmWorkflow for updating the LLM landscape paper pool (section/x_llm_papers.md) using fetch_llm_papers.py. Covers full re-fetch, resume from checkpoint, and adding0skills.sh installs
5kubesphere avatarkubesphere-openkruisekubesphere/kubesphereKubeSphere OpenKruise management Skill. Use when user asks to install or enable OpenKruise, check OpenKruise status, view kruise pods/logs/CRDs, create or updat0skills.sh installs
6kubesphere avatarvectorkubesphere/kubesphereThis handles the WizTelemetry Data Pipeline extension for KubeSphere, which is basically Vector under the hood for collecting and...0skills.sh installs