From science-superpowers
Executes pre-registered analysis plans with review checkpoints, validating each step and reporting results. Useful for reproducible data analysis without subagents.
How this skill is triggered — by the user, by Claude, or both
Slash command
/science-superpowers:executing-analysisThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Load the pre-registered plan, review it critically, execute all steps exactly as registered, report when complete.
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.
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.
For each step:
science-superpowers:verifying-results-before-claiming before marking done.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.
After all steps complete and verified:
science-superpowers:requesting-red-team-review on the whole result.science-superpowers:reporting-and-archiving-findings.STOP immediately when:
science-superpowers:investigating-anomalous-results (do NOT quietly drop data or tweak parameters)Ask rather than guess. A guess that changes the analysis silently destroys the result's credibility.
Required workflow skills:
npx claudepluginhub k-dense-ai/science-superpowers --plugin science-superpowersExecutes a pre-registered analysis plan by dispatching fresh subagents per step with two-stage review (protocol compliance then statistical rigor). Use when steps are mostly independent and stay in the current session.
Generates an executable empirical analysis plan from study_spec.md, audit report, and cleaned data structure. Outputs analysis_plan.md for human approval before analysis execution.
Verifies data science analysis results for reproducibility and completion, using guardrails to gate tool usage until approval.