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statistical-theory-analysis

Analyze theoretical properties of statistical methods under the formal formulation: identifiability, bias, variance, consistency, asymptotics, coverage, error bounds, robustness, and limitations.

64

Quality

77%

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tessl review fix ./external/agents/stat_research_agent/skills/statistical-theory-analysis/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

72%Weight 40%Scale 1-3

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

The body is concise and well-structured for a self-contained instruction skill, with a reusable theorem template. Its main weakness is actionability and workflow clarity: the theory-output directives are abstract and lack a worked example or explicit validation checkpoints.

Suggestions

Add one fully worked theorem example (filled-in Proposition, Proof Sketch, Interpretation, Limitations) to demonstrate the template in use and lift actionability.

Reframe the Theory Outputs and Experimental Predictions into a short numbered workflow with an explicit checkpoint (e.g., 'verify each theoretical claim yields at least one falsifiable empirical prediction').

DimensionReasoningScore

Conciseness

The body is lean and assumes Claude's competence — it lists theoretical outputs and a template skeleton without defining basic concepts like bias or consistency, so every token earns its place.

3 / 3

Actionability

It offers a copy-paste theorem template and concrete prediction bullets, but the 'Theory Outputs' entries are abstract directives ('Identifiability argument', 'Bias or variance calculation') with no worked example showing the template filled in, leaving guidance incomplete.

2 / 3

Workflow Clarity

A loose sequence is implied via section ordering and the 'after method proposal and before final experimental comparison' framing, but there is no explicit numbered workflow or validation checkpoints.

2 / 3

Progressive Disclosure

At under 50 lines with no need for external references, the well-organized sections (Overview, Theory Outputs, Theorem Template, Experimental Predictions) satisfy progressive disclosure per the simple-skills scoring note.

3 / 3

Total

10

/

12

Passed

Description

82%Weight 40%Scale 1-3

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

The description is specific and well-targeted with strong natural trigger terms for its domain, but it omits any explicit 'when to use' guidance, which caps its completeness. Adding a 'Use when...' clause would make it fully actionable as a trigger.

Suggestions

Append a 'Use when...' clause (e.g., 'Use when evaluating the formal theoretical properties of a statistical method such as consistency, bias, or coverage') to satisfy the 'when' half of completeness.

Drop the jargony phrase 'under the formal formulation' — it adds no trigger value and slightly muddies the otherwise clean enumeration.

DimensionReasoningScore

Specificity

The description enumerates many concrete analysis targets — 'identifiability, bias, variance, consistency, asymptotics, coverage, error bounds, robustness, and limitations' — listing multiple specific actions rather than vague language.

3 / 3

Completeness

It clearly answers 'what' (analyze theoretical properties) but provides no 'Use when...' clause or equivalent explicit trigger guidance, capping completeness at 2 per the judging guidelines.

2 / 3

Trigger Term Quality

Terms like 'bias', 'variance', 'consistency', 'asymptotics', 'coverage', and 'robustness' are natural language a statistical-theory researcher would say when requesting this skill.

3 / 3

Distinctiveness Conflict Risk

The niche domain of formal statistical-theory analysis with specific technical triggers is clearly distinguishable and unlikely to fire for unrelated skills.

3 / 3

Total

11

/

12

Passed

Validation

93%

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

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_version

'metadata.version' is missing

Warning

Total

15

/

16

Passed

Repository
aiming-lab/AutoResearchClaw
Reviewed

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