Agent skill · Data & Analytics

evidence-assessment

Evaluating the quality, provenance, and relevance of evidence that supports or undermines a claim. Covers source credibility, sampling quality, study design, levels of evidence (anecdote to meta-analysis), base rate integration, distinguishing primary from secondary sources, and calibrating belief to evidence strength. Use when the question is not whether an argument is valid but whether its premises are actually supported by the available data.

majiayu000github.com/majiayu000GitHub ↗
claude-codeMIT
Install
npx skills add majiayu000/claude-skill-registry --skill evidence-assessment --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 14 KB
Bundled scripts: none
Path: skills/analysis/evidence-assessment/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 534
Language: HTML

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Evidence Assessment Valid reasoning from false premises proves nothing. Evidence assessment is the discipline of testing whether the premises of an argument are actually supported by the data, what kind of data would be decisive, and how much confidence the available evidence warrants. This skill covers the evaluation of sources, study designs, sampling, levels of evidence, and the integration of new evidence with prior knowledge. **Agent affinity:** elder (structural reconstruction of evidence claims), tversky (base rates, inductive strength), kahneman-ct (evidence evaluation under System 1/2) **Concept IDs:** crit-evidence-quality, crit-sourcing, crit-burden-of-proof, crit-scientific-literacy ## The Assessment Toolbox at a Glance | # | Operation | Question answered | |---|---|---| | 1 | Source identification | Where did this claim come from? | | 2 | Primary vs. secondary | Is this the original source or a report of it? | | 3 | Source credibility | Does the source have relevant expertise and a track record? | | 4 | Funding and conflicts | Who paid for this? What do they stand to gain? | | 5 | Sample quality | How were the data collected and from whom? | | 6 | Study design | Obse

What's inside
Steps it walks through
  1. The Assessment Toolbox at a Glance
  2. The Levels of Evidence Hierarchy
  3. Operation 1 — Source Identification
  4. Operation 2 — Primary vs. Secondary Sources
  5. Operation 3 — Source Credibility
  6. Operation 4 — Funding and Conflicts of Interest
  7. Operation 5 — Sample Quality
  8. Operation 6 — Study Design
  9. Operation 7 — Scope Check
  10. Operation 8 — Reproducibility and Replication
  11. Operation 9 — Base Rate Integration
  12. Operation 10 — Burden of Proof
  13. Standard Assessment Procedure
  14. When to Use
Ships with 1 file
  • metadata.json
More from claude-skill-registry
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About this skill
What does the evidence-assessment skill do?

Evaluating the quality, provenance, and relevance of evidence that supports or undermines a claim. Covers source credibility, sampling quality, study design, levels of evidence (anecdote to meta-analysis), base rate integration, distinguishing primary from secondary sources, and calibrating belief to evidence strength. Use when the question is not whether an argument is valid but whether its premises are actually supported by the available data.

How do I install it?

Run `npx skills add majiayu000/claude-skill-registry --skill evidence-assessment --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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