Agent skill

reporting-and-archiving-findings

Use when an analysis is complete and verified, and you need to decide how to report it and archive the work for reproducibility

K-Dense-AIgithub.com/K-Dense-AIGitHub ↗
claude-codeNOASSERTION
Install
npx skills add K-Dense-AI/science-superpowers --skill reporting-and-archiving-findings --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 1
SKILL.md size: 6 KB
Bundled scripts: none
Path: skills/reporting-and-archiving-findings/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 280
Language: Shell

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Reporting and Archiving Findings ## Overview Complete an investigation by confirming reproducibility, presenting clear options, handling the chosen one, and archiving everything needed to reproduce the result. **Core principle:** Confirm reproducibility -> separate confirmatory from exploratory -> present options -> execute choice -> archive code + data + environment + pre-registration. **Announce at start:** "I'm using the reporting-and-archiving-findings skill to complete this work." ## Step 1: Verify Reproducibility Before reporting anything, confirm the whole analysis reproduces from immutable raw data with the fixed seed in the pinned environment. ```bash # From a clean state: re-run the pipeline end to end # Confirm headline numbers match what you intend to report ``` Use `science-superpowers:verifying-results-before-claiming`. **If it doesn't reproduce, stop** — fix reproducibility (possibly via `science-superpowers:investigating-anomalous-results`) before reporting. Don't report a number you can't regenerate. For any confirmatory claim, also run the pre-registration audit and keep its output for the report: ```bash # ships with science-superpowers:preregistering-analysis

What's inside
Steps it walks through
  1. Overview
  2. Step 1: Verify Reproducibility
  3. Step 2: Detect Environment
  4. Step 3: Determine Base Branch
  5. Step 4: Present Options
  6. Step 5: Execute Choice
  7. Option 1: Merge Locally
  8. Option 2: Write Up and Share
  9. Option 3: Keep As-Is
  10. Option 4: Discard
  11. Step 6: Cleanup Workspace
  12. Report Content
  13. Archive for Reproducibility
  14. Red Flags
Commands it runs
From a clean state: re-run the pipeline end to end
Confirm headline numbers match what you intend to report
ships with science-superpowers:preregistering-analysis
git merge-base HEAD main 2>/dev/null || git merge-base HEAD master 2>/dev/null
cd "$MAIN_ROOT"
git checkout <base-branch> && git pull && git merge <feature-branch>
Re-run the pipeline on the merged result; confirm it still reproduces
git push -u origin <feature-branch>
gh pr create --title "<title>" --body "$(cat <<'EOF'
git worktree remove "$WORKTREE_PATH"
More from science-superpowers
All skills →
About this skill
What does the reporting-and-archiving-findings skill do?

Use when an analysis is complete and verified, and you need to decide how to report it and archive the work for reproducibility

How do I install it?

Run `npx skills add K-Dense-AI/science-superpowers --skill reporting-and-archiving-findings --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From K-Dense-AI/science-superpowers, a repository with 280 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going