CtrlK
BlogDocsLog inGet started
Tessl Logo

statistical-power

Sample-size and statistical power calculations for planning studies. Use whenever someone asks "how many subjects/samples/replicates do I need", wants an a priori power analysis, a minimum detectable effect (MDE), a power curve, or needs to justify a sample size for a grant, IRB protocol, or pre-registration. Covers closed-form power for t-tests, ANOVA, proportions, correlations, chi-square, and regression, plus simulation-based (Monte Carlo) power for designs with no formula — logistic/Poisson regression, mixed models, cluster-randomized trials, survival, and interactions. Use this skill even when the request only mentions an effect size, alpha, or "80% power" without saying "power analysis" explicitly. For laying out the study (randomization, blocking, factorial/DOE, crossover, sequential designs) use experimental-design; for analyzing data already collected and reporting it use statistical-analysis.

75

Quality

93%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

SecuritybySnyk

Passed

No findings from the security scan

SKILL.md
Quality
Evals
Security

Quality

Content

86%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A high-quality, actionable skill body with excellent progressive disclosure and executable examples. Minor gains are available in tightening motivational prose and making workflow feedback loops more explicit.

Suggestions

Tighten the Overview's motivational sentences and the post-hoc power blockquote to pure guidance to improve conciseness.

Add an explicit feedback loop in the Workflow (e.g., 'if required n is infeasible, revisit the effect-size justification or switch to simulation') to strengthen validation checkpoints.

Consider moving the detailed 'Adjustments people forget' and 'Common pitfalls' lists into a reference file if the body grows, keeping the overview lean.

DimensionReasoningScore

Conciseness

Dense and expert-targeted with every sentence carrying actionable content, but a few motivational/editorial sentences in the Overview and the 'Avoid post-hoc power' blockquote could be trimmed without losing guidance.

4 / 5

Actionability

Provides copy-paste-ready closed-form examples across seven test types with explicit argument values, a concrete simulation harness call with its gen_and_test signature, explicit adjustment formulas, and an adaptable reporting template.

5 / 5

Workflow Clarity

A clear 7-step Workflow sequence is present with some implicit checkpoints (sensitivity analysis as feedback, Monte Carlo CI as verification), but explicit error-recovery feedback loops ('if infeasible, reconsider X') are not stated.

4 / 5

Progressive Disclosure

Well-organized sections with one-level-deep references to the three reference files and two scripts, all of which exist; inline pointers and the Resources section clearly signal where detail lives.

5 / 5

Total

18

/

20

Passed

Description

100%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong, comprehensive description that clearly states capabilities, provides abundant natural trigger phrases, and explicitly distinguishes itself from neighboring skills. Both the 'what' and 'when' are answered concretely.

DimensionReasoningScore

Specificity

Enumerates concrete capabilities comprehensively across both closed-form tests (t-tests, ANOVA, proportions, correlations, chi-square, regression) and simulation-based power (logistic/Poisson, mixed models, cluster-randomized, survival, interactions).

5 / 5

Completeness

Explicitly answers 'what' (sample-size and power calculations via closed-form and simulation across named test families) and 'when' with concrete trigger clauses ('Use whenever…', 'Use this skill even when the request only mentions…').

5 / 5

Trigger Term Quality

Captures natural user phrasings like 'how many subjects/samples/replicates do I need', '80% power', 'a priori power analysis', 'MDE', and 'justify a sample size for a grant, IRB protocol, or pre-registration' alongside technical synonyms.

5 / 5

Distinctiveness Conflict Risk

Carves out a clear a-priori planning niche and explicitly routes adjacent work to other skills ('use experimental-design', 'use statistical-analysis'), minimizing conflict risk.

5 / 5

Total

20

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
K-Dense-AI/scientific-agent-skills
Reviewed

Table of Contents

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.