mcpbeat

Receiving Critical Review

k-dense-ai/receiving-critical-review

Use when receiving critical feedback on an analysis or manuscript, before implementing suggestions, especially if feedback seems unclear or methodologically questionable - requires verification, not performative agreement or blind changes

1k tokens
context cost
the whole folder, loaded on every use
1
files
instructions only
0
copies elsewhere
how many repositories repackaged it
280
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/K-Dense-AI/science-superpowers --skill receiving-critical-review

The instruction itself

14 sections, as written by the author

Receiving Critical Review

Overview

Critique of an analysis requires technical evaluation, not emotional performance.

Core principle: Verify before changing. Ask before assuming. Methodological correctness over social comfort.

A reviewer pointing at a confound is doing you a favor only if you check whether the confound is real. Reflexively agreeing — or reflexively defending — both skip the verification that makes review worthwhile.

The Response Pattern

WHEN receiving review feedback:

1. READ: The complete feedback without reacting
2. UNDERSTAND: Restate the methodological concern in your own words (or ask)
3. VERIFY: Check it against the data, code, and pre-registration
4. EVALUATE: Is it correct FOR THIS analysis?
5. RESPOND: Technical acknowledgment or reasoned pushback
6. ACT: One item at a time, re-running and re-verifying each

Forbidden Responses

NEVER:

  • "You're absolutely right!" (before you've checked)
  • "Great catch!" / "Excellent point!" (performative)
  • "Let me fix that now" (before verification)

INSTEAD:

  • Restate the methodological concern
  • Ask a clarifying question
  • Push back with reasoning if it's wrong for this analysis
  • Or just verify and report what you found

Handling Unclear Feedback

IF any item is unclear:
  STOP - do not change anything yet
  ASK for clarification

WHY: Methodological items interact. Adjusting for the wrong confound
because you misread the concern can bias the result further.

Source-Specific Handling

From your human partner

  • Trusted — verify, then act
  • Still ask if scope is unclear
  • No performative agreement; skip to verification or a technical acknowledgment

From an external or red-team reviewer

BEFORE changing anything:
  1. Is it methodologically correct for THIS data and design?
  2. Would the change itself introduce bias (e.g., adding a collider as a covariate)?
  3. Is there a pre-registered reason the analysis is the way it is?
  4. Does the reviewer have the full context (the pre-registration, the data structure)?

IF the suggestion seems wrong:
  Push back with technical reasoning and evidence

IF you can't verify:
  Say so: "I can't verify this without [X]. Should I [investigate/ask]?"

IF it conflicts with the pre-registration:
  A change to the registered analysis is a deviation. It renders that
  analysis exploratory. Flag this explicitly before making the change.

The Pre-Registration Guard

A reviewer may suggest a "better" analysis. Before adopting it:

  • If it changes the pre-registered confirmatory analysis, you are moving from confirmatory to exploratory. That may be the right call — but say so, and report it accordingly.
  • Don't let good-faith "improve it" feedback silently convert a confirmatory result into a post-hoc one.

When To Push Back

  • The suggestion would introduce bias (conditioning on a collider, adjusting away a mediator you care about, leakage)
  • It treats observational data as if it supported causal claims, or vice versa
  • The reviewer lacks context the pre-registration provides
  • It contradicts your human partner's design decisions (raise with them)

How: technical reasoning, not defensiveness. Show the diagnostic, the DAG, the pre-registration line, or the reproduced number.

Acknowledging Correct Feedback

When feedback IS correct:

✅ "Verified — site does confound this. Added it; estimate drops to 0.09 [−0.01, 0.19]. Now inconclusive."
✅ "Confirmed leakage: the scaler was fit before the split. Refit on train only; AUC falls to 0.71."
✅ [Just fix it, re-run, and show the new number]

❌ "You're absolutely right!"
❌ "Great catch!"
❌ "Thanks for spotting that!"
❌ ANY gratitude or praise performance

Why no thanks: the corrected, re-verified result shows you heard the feedback. State the fix and the new evidence.

Gracefully Correcting Your Pushback

If you pushed back and were wrong:

✅ "You were right — I checked and the assumption is violated. Re-running with the robust estimator."
❌ Long apology or defense of why you pushed back

Common Mistakes

| Mistake | Fix |

|---------|-----|

| Performative agreement | State the concern or verify |

| Blind change | Check against data/code/prereg first |

| Batch edits without re-running | One at a time, re-verify each |

| Assuming the reviewer is right | Check whether the change biases the result |

| Silently changing the registered analysis | Flag it as a deviation → exploratory |

| Avoiding pushback | Methodological correctness > comfort |

The Bottom Line

External feedback = hypotheses to verify, not orders to follow.

Verify against the data and the pre-registration. Question. Then act — and re-verify with science-superpowers:verifying-results-before-claiming.

How to use it

Copy the folder

Take k-dense-ai/receiving-critical-review from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.