assess-construct-validity
SkillDev toolsLets your agent check whether a benchmark actually measures the capability it claims to measure.
Available today. Use it from your connected AI after setup.
No other account needed.
Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Then ask your AI: use the assess-construct-validity skill
About this skill
Assess whether a benchmark measures its claimed capability using content, convergent, discriminant, and confound analysis.
What this skill tells your AI
The instructions your AI receives, as published by yogsoth-ai/de-anthropocentric-research-engine in skills/assess-construct-validity/SKILL.md and read by ahel’s review.
Purpose
Assess whether a benchmark or evaluation construct measures the claimed capability rather than content cues, confounds, or unrelated skill.
Input contract
required: [construct_claim, benchmark_specification, evaluation_records]
optional: [content_analysis, convergent_measures, discriminant_measures, confound_hypotheses]
constraints: [each validity judgment requires an observable indicator, comparison basis, and provenance]
Procedure
- State the target construct and map benchmark tasks, labels, and metrics to its intended components.
- Check content coverage and plausible construct-irrelevant cues against the benchmark specification.
- Compare convergent and discriminant evidence where available, preserving missing comparisons.
- Test confound hypotheses with controlled contrasts or artifact probes and record residual uncertainty.
If construct validity depends on whether the operationalization covers the intended domain, consider map-coverage-space as the next tactic.
Output contract
produces: [construct_map, content_validity_assessment, convergent_discriminant_evidence, confound_report, validity_judgment]
delta_fields: [findings, evidence_updates, uncertainties, decisions, open_questions]
Quality gates
- Every validity claim cites a task, measure, contrast, or artifact observation.
- Content coverage, convergence, discrimination, and confounds are reported separately.
- A missing diagnostic is marked unresolved rather than treated as evidence of validity.
Failure and counterexamples
Do not infer construct validity from a high score, face validity, or agreement with another measure that shares the same confound.
Provenance map
resolved: construct-validity-assessment
Signals
- GitHub stars
- 501
- Forks
- 41
- Last commit
- Sep 2026
Advanced
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- skill
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assess-construct-validity- Source
- github.com/yogsoth-ai/de-anthropocentric-research-engine