designing-the-analysis
Use when you have an approved research question and need a concrete analysis plan, before touching outcome data or fitting any…
Use when you have a pre-registered analysis plan to execute inline in this session with review checkpoints, on a platform without subagents
$ npx -y skills add K-Dense-AI/science-superpowers --skill executing-analysis --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/executing-analysisContext preview
The summary Claude sees to decide when to auto-load this skill.
Use when you have a pre-registered analysis plan to execute inline in this session with review checkpoints, on a platform without subagents
name: executing-analysis description: Use when you have a pre-registered analysis plan to execute inline in this session with review checkpoints, on a platform without subagents
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.
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.
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.
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:
**STOP immediately when:**
**Ask rather than guess.** A guess that changes the analysis silently destroys the result's credibility.
**Required workflow skills:**
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.
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