scientific-critical-thinking

name	scientific-critical-thinking
description	Critically examine claims, assumptions, evidence, and argument quality in computer science papers, experiments, benchmark plans, and technical proposals. Use when stress-testing a research idea, detecting confounders, checking causal leaps, or identifying missing controls before writing, reviewing, or implementing.

Scientific Critical Thinking

Use this skill to pressure-test research reasoning in computer science. The goal is not skepticism for its own sake; the goal is to find where the evidence chain is weak and how to strengthen it.

Default Lens

For any claim, ask:

• what exactly is being claimed
• what evidence is offered
• what assumptions are required for the claim to hold
• what plausible alternative explanations remain
• what evidence would actually falsify the claim

This lens works for papers, experiment plans, benchmark designs, and internal research proposals.

CS-Specific Checks

Problem formulation

• Is the task or threat model defined precisely?
• Is the scope realistic or selectively simplified?
• Are important constraints hidden in preprocessing, oracle information, or infrastructure assumptions?

Data and benchmark design

• Are train/validation/test boundaries defensible?
• Could leakage, duplication, contamination, or benchmark saturation explain the result?
• Are datasets representative of the deployment or research setting being claimed?

Baselines and comparisons

• Are the baselines strong enough?
• Are tuning budgets, data access, tools, and compute budgets comparable?
• Is an apparent gain really due to a confounded variable such as scaling, extra data, or filtering?

Metrics and interpretation

• Do the metrics capture the failure modes that matter?
• Is the improvement practically meaningful or only statistically visible?
• Are averages hiding subgroup collapse, instability, or extreme tail behavior?

Causal language and overclaiming

• Does the evidence justify causal or general statements?
• Is a narrow result being generalized to a broader claim about intelligence, robustness, usability, or efficiency?
• Does the paper confuse correlation, proxy performance, and actual capability?

Reproducibility and robustness

• Would the conclusion survive different seeds, environments, workloads, or datasets?
• Are there ablations or controls for the components that supposedly matter?
• Are negative results or boundary conditions missing?

Domain Hints

Apply the same core logic, but emphasize different checks by subfield:

• ML / LLMs: leakage, benchmark contamination, scaling confounds, seed sensitivity, weak ablations
• systems: unrealistic workloads, missing bottlenecks, benchmark gaming, environment dependence
• software engineering: dataset labeling quality, project selection bias, threat to external validity
• HCI: task realism, study power, participant bias, ecological validity
• security: attacker model gaps, unrealistic assumptions, incomplete threat coverage
• theory: hidden assumptions, theorem-to-practice disconnect, omitted edge cases

Use Local References Selectively

Read only the references that help the current critique:

• references/experimental_design.md
• references/statistical_pitfalls.md
• references/common_biases.md
• references/logical_fallacies.md
• references/evidence_hierarchy.md
• references/scientific_method.md

Useful Output Formats

Choose one depending on the task:

• claim-to-evidence audit
• list of confounders and missing controls
• stronger experiment design
• reviewer-style critique
• risk register for a proposed research direction
• questions that must be answered before implementation or submission

Default Output

When the user simply asks for a critical analysis, return:

1. the main claims being examined
2. the strongest supporting evidence
3. the weakest links or confounders
4. what additional evidence would materially strengthen the case