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hypothesis-generation

Formulates evidence-bounded scientific questions, candidate hypotheses, rival explanations, causal or associational claims, discriminating predictions, measurements, and preregistration-ready analysis plans. Used when turning observations or preliminary findings into transparent, testable research plans without treating hypotheses as facts.

67

Quality

84%

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SecuritybySnyk

Passed

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SKILL.md
Quality
Evals
Security

Quality

Content

71%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 well-structured, highly procedural skill body with an exceptionally clean progressive-disclosure design: an overview and 12-step workflow in SKILL.md, verified one-level-deep references, and bundled deterministic validation CLIs. The main costs are length for its scope — dated, time-sensitive detail living in the main flow — and the absence of a worked example or an explicit validate-and-retry loop.

Suggestions

Move the dated source-ledger refresh notes and 2026 policy distinctions (and the arXiv fetching instructions) into references/source_ledger.md or a clearly marked maintenance section, keeping the body time-independent.

Add one short worked example inline — a two-candidate hypothesis record with one prediction and one rival contrast — so the artifact format is visible without opening a template.

Close the validation loop explicitly: after each CLI invocation, state "on exit code 1, fix the reported declaration errors and re-run until exit code 0".

DimensionReasoningScore

Conciseness

The body is dense and directive rather than padded, but at roughly 275 lines it includes unnecessary tightening opportunities: time-sensitive dated material in the main flow (the 2026-10-01 source-ledger refresh, the July 2026 vs August 2026 policy distinction, arXiv version-fetching instructions) and redundancy between the "Never" list and workflow steps 1 and 12. This fits "mostly efficient but could be tightened" (3) better than the minor-trims anchor (4).

3 / 5

Actionability

Concrete, copy-paste-ready commands appear throughout ("python3 scripts/validate_hypothesis_schema.py record.json", "python3 scripts/check_operationalization.py local-operationalization.json") with per-step record checklists specifying exactly what to document. It falls short of 5 because no worked example of a completed record, matrix, or scaffold is shown inline; specifics are deferred to templates and references.

4 / 5

Workflow Clarity

The 12-step workflow is clearly sequenced with validation tooling embedded at the relevant steps and exit-code semantics documented (0/1/2). It misses 5 because there is no explicit error-recovery loop (e.g., "if exit code 1, fix the reported errors and re-run") tying the validators back into the step that produced the artifact; no destructive or batch operations are involved so the cap does not apply.

4 / 5

Progressive Disclosure

SKILL.md works as an overview-plus-workflow while details are split into 10 reference files, each given a one-line description in the References section, plus scripts and template assets; every cited path was verified to exist and references point only to top-level assets, so navigation is one level deep and clearly signaled. This matches the clear-overview, well-signaled anchor exactly.

5 / 5

Total

16

/

20

Passed

Description

88%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.

The description is a strong, third-person, action-enumerating statement with an explicit "Used when..." trigger clause. Its main weaknesses are a few missing natural trigger synonyms and slightly broad tail terms that create minor overlap risk with adjacent research-methods skills.

Suggestions

Add one or two common user phrasings to the trigger clause, e.g. "Use when planning a study, designing experiments, or drafting a preregistration".

Tighten the tail of the capability list ("measurements, and preregistration-ready analysis plans") so generic words like "measurements" do not invite triggering for general measurement or statistics tasks.

DimensionReasoningScore

Specificity

"Formulates evidence-bounded scientific questions, candidate hypotheses, rival explanations, causal or associational claims, discriminating predictions, measurements, and preregistration-ready analysis plans" lists multiple specific concrete actions covering the full workflow, matching the comprehensive-coverage anchor rather than the minor-gaps anchor at 4.

5 / 5

Completeness

Both parts are explicit: the "what" is the enumerated list of artifacts it formulates, and the "when" is the concrete trigger clause "Used when turning observations or preliminary findings into transparent, testable research plans without treating hypotheses as facts". This clearly matches the anchor requiring both what and when with concrete trigger phrases.

5 / 5

Trigger Term Quality

Good natural keyword coverage ("hypotheses", "rival explanations", "observations", "preregistration", "research plans", "predictions") but a few natural phrasings a user would say are missing, such as "study design" or "experiment planning". It sits above the missing-variations anchor (3) but short of the synonym-complete anchor (5).

4 / 5

Distinctiveness Conflict Risk

The hypothesis-generation/preregistration framing carves a distinct niche with unlikely mis-triggering, but broad tail phrases like "measurements" and "analysis plans" leave minor overlap risk with general experiment-design or statistics-analysis skills, matching the mostly-distinct anchor (4) rather than minimal-conflict (5).

4 / 5

Total

18

/

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.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

K-Dense-AI/scientific-agent-skills

Path

skills/hypothesis-generation/SKILL.md

Reviewed

Table of Contents

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