visual-data-dictionary

Build a Visual Data Dictionary — an interactive qsv viz smart dashboard (a Data Schematic) driven by an LLM-inferred JSON Schema data dictionary, with the dictionary browsable beside the charts. Use when the user asks for a visual data dictionary, a documented dashboard, a dictionary-driven dashboard or Data Schematic, or wants to explore and document a CSV at the same time. Optionally bins rows into GeoJSON regions.

Quality

88%

Does it follow best practices?

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Securityby

Low

Low-risk findings worth noting

Quality

Content

85%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.

The body is an information-dense, highly actionable multi-stage workflow with explicit sequencing and validation — a strong code/ops skill. Its main weakness is progressive disclosure: it ships as a single 600-line file with no bundle, and the edit_dictionary.py script it repeatedly references is absent.

Suggestions

Ship edit_dictionary.py in a scripts/ directory so the Stage 2.5 TUI reference resolves to a real file rather than a dead path.

Move the large inline python helpers (GeoJSON feature-id discovery, dictionary coverage check, HTML verification) into scripts/ and reference them by path to slim SKILL.md into an overview.

Add a short 'Quick start' summary at the top pointing to each stage, so the five-stage flow is navigable without scrolling the whole file.

Dimension	Reasoning	Score
Conciseness	Dense with qsv-specific gotchas Claude would not know (flag semantics, --bivariate implicitly enabling --dictionary infer, x-qsv key behavior), avoiding generic explanations of concepts Claude already knows; a few passages like the top scope note and the --dict-info description could be trimmed.	4 / 5
Actionability	Provides copy-paste-ready bash and python for every stage, defines all shell variables in a Naming table, and walks through a concrete worked example (NMBGMRManualWaterLevels.csv) covering the common cases.	5 / 5
Workflow Clarity	Five stages are explicitly ordered ('in this order and no other', 'The order is load-bearing') with validation checkpoints (Stage 0 preconditions, Stage 1 'Report first', Stage 2 coverage check, Stage 5 'Verify, then report'), feedback loops, and a Guardrails section — satisfying the destructive/batch validation requirement.	5 / 5
Progressive Disclosure	Section headers and staging give internal structure, but the skill is monolithic — no references/scripts/assets bundle exists, large inline python blocks (the ~80-line GeoJSON discovery script, verification scripts) are inlined, and the one referenced external file edit_dictionary.py is missing from the bundle, creating a dead reference at Stage 2.5.	3 / 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.

The description is strong: it states concrete capabilities, provides explicit 'Use when...' trigger guidance with synonyms, and occupies a distinct niche. It uses appropriate third-person/imperative voice throughout. The only minor gap is the absence of file-extension triggers.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — 'Build a Visual Data Dictionary', 'LLM-inferred JSON Schema data dictionary', 'dictionary browsable beside the charts', 'explore and document a CSV', 'bins rows into GeoJSON regions' — giving comprehensive coverage of what the skill does.	5 / 5
Completeness	Explicitly answers both what ('Build a Visual Data Dictionary — an interactive qsv viz smart dashboard...') and when ('Use when the user asks for a visual data dictionary, a documented dashboard...') with concrete trigger phrases.	5 / 5
Trigger Term Quality	Good coverage of natural trigger phrases with synonyms ('visual data dictionary', 'documented dashboard', 'dictionary-driven dashboard', 'Data Schematic', 'explore and document a CSV'), though no file extensions like '.csv' are included and the terms are fairly specialized jargon.	4 / 5
Distinctiveness Conflict Risk	Occupies a clear niche with distinct triggers ('Data Schematic', 'dictionary-driven dashboard', 'visual data dictionary') that are unlikely to fire for unrelated skills, giving minimal conflict risk.	5 / 5
	Total	19 / 20 Passed

Validation

87%

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 — 14 / 16 Passed

Validation for skill structure

Criteria	Description	Result
skill_md_line_count	SKILL.md is long (617 lines); consider splitting into references/ and linking	Warning
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	14 / 16 Passed

Repository: dathere/qsv
Path: .claude/skills/visual-data-dictionary/SKILL.md
Commit: a46782a

Reviewed: about 3 hours ago

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