Requesting research review

Skill jeonnoin-alt/Eureka/skills/requesting-research-review

Scientific TDD for AI coding agents: pre-registration before data, claims audit before submission, external novelty gate, reproducibility. Claude Code + 4 platforms.

Install

npx -y skills add jeonnoin-alt/Eureka --skill requesting-research-review

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

2 stars2 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Use when an experiment phase is complete, before drawing conclusions, before writing results, or before claiming findings are publication-ready

SKILL.md

4.7 KB, as published. Nobody here has run it

Requesting Research Review

Dispatch eureka:research-reviewer subagent to catch scientific issues before they propagate. The reviewer gets precisely crafted context for evaluation — never your session's history. This keeps the reviewer focused on the evidence, not your thought process, and preserves your own context for continued work.

Core principle: Review before you conclude.

When to Request Review

Mandatory:

After each major experiment phase (baselines, ablations, main experiments)
Before writing any Results or Conclusion section
Before submitting to any journal, conference, or preprint server
Before reporting results to collaborators or supervisors

Optional but valuable:

When results are surprising (real signal or artifact?)
After revising analysis in response to feedback
Before committing to a new research direction based on results
When stuck interpreting ambiguous results

How to Request

1. Identify what you're reviewing:

Determine which research phase just completed and what the reviewer needs to evaluate.

2. Gather the review context:

RESEARCH_QUESTION   = The exact hypothesis being tested
DESIGN_DOC_PATH     = Path to the approved research design document
RESULTS_PATHS       = Paths to ALL result files (not just summaries)
PHASE_DESCRIPTION   = Which phase just completed (e.g., "baseline experiments")
DOMAIN_CONTEXT      = Field-specific context, tools, typical effect sizes
TARGET_VENUE        = Target journal or conference (sets the bar)
PASS_THRESHOLD      = Score threshold (85 for mid-project, 95 for pre-submission)

3. Dispatch the research-reviewer subagent:

Use the Agent tool with eureka:research-reviewer type. Fill the template at requesting-research-review/research-reviewer-prompt.md with the gathered context.

4. Act on feedback:

CRITICAL issues: Stop work. Do not report results. Do not proceed to the next phase. Fix the issue and request re-review.
MAJOR issues: Fix before next phase. Do not build on flawed foundations.
MINOR issues: Track and address before publication.
Push back if the reviewer is wrong — with specific evidence from your data.

Choosing the Right Threshold

Situation	Threshold	Reasoning
Mid-project checkpoint	85/100	Catch major issues early without demanding final polish
Before writing results	90/100	Claims must be well-supported before committing to paper
Pre-submission gate	95/100	Publication-ready: all claims verified, all stats complete
Quick sanity check	80/100	Just checking for obvious problems

Example

[Just completed baseline experiments comparing three models on a held-out test set]

You: Let me request a research review before interpreting these results.

RESEARCH_QUESTION   = "Does Model C outperform Model A and Model B baselines
                       on outcome Y prediction?"
DESIGN_DOC_PATH     = docs/eureka/designs/2026-04-10-model-baselines-design.md
RESULTS_PATHS       = results/run_20260411_171136/cv_results.json,
                      results/baseline_modelA/, results/baseline_modelB/
PHASE_DESCRIPTION   = Baseline experiment completion (Model A, Model B, Model C)
DOMAIN_CONTEXT      = Supervised prediction on cohort data,
                      Pearson r and R-squared are standard metrics
TARGET_VENUE        = [target journal]
PASS_THRESHOLD      = 85

[Dispatch eureka:research-reviewer subagent]

[Subagent returns]:
  D1: 88  D2: 82  D3: 75  D4: 70  D5: 72  D6: 78  D7: 80
  Overall: FAIL (D3, D4 below threshold)
  Critical: Single seed results reported — need ≥5 seeds
  Major: No confidence intervals on Pearson r comparisons

You: [Fix: re-run with 10 seeds, add bootstrap CIs]
[Request re-review after fixes]

Integration

Called by: eureka:using-eureka (when experiment phase completion detected)
Dispatches: eureka:research-reviewer agent (via Agent tool)
Pairs with: eureka:receiving-research-review (for handling the feedback)

Red Flags

Never:

Skip review because "the results are obvious"
Ignore Critical issues
Proceed to writing with unresolved Major issues
Dismiss reviewer feedback without checking against your actual data

If reviewer is wrong:

Push back with specific evidence (file paths, computed values)
Show the data that contradicts the reviewer's assessment
Request clarification on unclear feedback

See template at: requesting-research-review/research-reviewer-prompt.md