spectral-analysis

Frequency-domain analysis — FFT, power spectral density (Welch/periodogram), spectrograms, wavelet transforms, and coherence. Use for any signal with periodic, quasi-periodic, or transient frequency content in physics data.

Quality

87%

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Passed

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Quality

Content

82%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 actionable reference with executable code across all six spectral-analysis workflows and useful trade-off/pitfall tables. Minor conciseness and validation-checkpoint refinements would raise it further.

Suggestions

Trim a few explanatory inline comments (e.g., "better noise averaging than raw FFT", "raw, no averaging") that restate what the code already shows.

Add a brief validation/ sanity-check note for each workflow (e.g., confirm Nyquist condition fs >= 2*f_max before interpreting spectra) to strengthen workflow clarity.

Consider moving deeper parameter-tuning guidance into a short references/ file and linking it, to keep the main body as a lean overview.

Dimension	Reasoning	Score
Conciseness	Code-heavy and mostly lean with no padding of concepts Claude already knows; a few inline asides ("better noise averaging than raw FFT") could be trimmed.	4 / 5
Actionability	Every one of the six workflows provides copy-paste-ready, fully executable Python with concrete parameters, units, and example signals covering the common cases.	5 / 5
Workflow Clarity	Each technique is a clear self-contained sequence and the pitfall table aids error recovery, but there are no explicit validation checkpoints; acceptable since the operations are non-destructive analysis.	4 / 5
Progressive Disclosure	Well-organized sections (Overview, When to Use, six numbered Core Workflows, trade-off and pitfall tables) with no nested or missing references; a few advanced variants could optionally be split out.	4 / 5
	Total	17 / 20 Passed

Description

92%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, specific description that concretely enumerates the skill's capabilities and gives an explicit use-when clause tied to physics signal characteristics. Only trigger-term synonym coverage keeps it from a perfect score.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete analyses — "FFT, power spectral density (Welch/periodogram), spectrograms, wavelet transforms, and coherence" — giving comprehensive coverage of the spectral-analysis domain.	5 / 5
Completeness	Explicitly answers both what ("FFT, power spectral density... spectrograms, wavelet transforms, and coherence") and when ("Use for any signal with periodic, quasi-periodic, or transient frequency content in physics data").	5 / 5
Trigger Term Quality	Strong natural terms (FFT, spectrograms, wavelet transforms, coherence, frequency content) plus method variants, but a few everyday synonyms ("power spectrum", "spectrum") are only implicit via "power spectral density".	4 / 5
Distinctiveness Conflict Risk	Occupies a clear niche (frequency-domain analysis of physics signals) with distinctive triggers unlikely to fire for unrelated skills.	5 / 5
	Total	19 / 20 Passed

Validation

93%

Warnings & errors only

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

Validation — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	15 / 16 Passed

Repository: synthetic-sciences/openscience
Path: backend/cli/skills/physics/spectral-analysis/SKILL.md
Commit: 7dabbf0

Reviewed: 3 days ago

Table of Contents

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