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Executing analysis

Skill K-Dense-AI/science-superpowers/skills/executing-analysis

Composable computational-science methodology skills for AI research agents — pre-registration over TDD. A science-domain reimplementation of Superpowers.

Install
npx -y skills add K-Dense-AI/science-superpowers --skill executing-analysis

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

SKILL.md

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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

  1. Read the analysis plan and the frozen pre-registration.
  2. Review critically — any concern about the design, the decision rule, or feasibility?
  3. If concerns: raise them with your human partner before starting. A change to the registered analysis is a documented deviation and renders that part exploratory.
  4. If no concerns: create a TodoWrite with all steps and proceed.

Step 2: Execute Steps

For each step:

  1. Mark in_progress.
  2. For nontrivial estimators: validate the pipeline on known/simulated data first.
  3. Run the step exactly as registered. Keep the seed. Read immutable raw / named derived; write a new artifact.
  4. Validate the output (shape, ranges, counts) — nothing changes silently.
  5. Apply the registered decision rule as written; do not re-decide it after seeing the number.
  6. Use science-superpowers:verifying-results-before-claiming before marking done.
  7. Commit. Mark completed.

Checkpoints

After each natural group of steps (e.g., data prep, then primary model), pause and report results to your human partner before continuing. Keep confirmatory and exploratory results clearly separated in every report.

Step 3: Complete

After all steps complete and verified:

  • Use science-superpowers:requesting-red-team-review on the whole result.
  • Then science-superpowers:reporting-and-archiving-findings.

When to Stop and Ask

STOP immediately when:

  • A result is surprising, impossible, or contradicts a sanity check → use science-superpowers:investigating-anomalous-results (do NOT quietly drop data or tweak parameters)
  • The plan requires deviating from the pre-registration to proceed
  • A validation fails repeatedly
  • An instruction is unclear

Ask rather than guess. A guess that changes the analysis silently destroys the result's credibility.

Remember

  • Pre-registration frozen before execution
  • Validate pipelines on known data
  • Run steps exactly as registered; never silently deviate
  • Label anything unregistered as exploratory
  • Don't skip validation or verification
  • Stop on anomalies; investigate by root cause

Integration

Required workflow skills:

  • science-superpowers:setting-up-reproducible-analysis — run first
  • science-superpowers:preregistering-analysis — frozen before execution
  • science-superpowers:investigating-anomalous-results — on surprising results
  • science-superpowers:verifying-results-before-claiming — before any completion claim
  • science-superpowers:reporting-and-archiving-findings — after all steps complete

Keep looking

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