langfuse-data-handling

Manage Langfuse data export, retention, and compliance requirements. Use when exporting trace data, configuring retention policies, or implementing data compliance for LLM observability. Trigger with phrases like "langfuse data export", "langfuse retention", "langfuse GDPR", "langfuse compliance", "export langfuse traces".

Quality

—

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Advisory

Suggest reviewing before use

Quality

Content

65%

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

The body is highly actionable with complete code and useful reference tables, but it suffers from duplication with an unreferenced bundle file and lacks validation checkpoints around destructive batch operations. Restructuring to point at the reference and adding pre-deletion verification would lift the weaker dimensions.

Suggestions

Add validation/dry-run checkpoints before destructive batch operations — e.g., log the trace count to be deleted and require explicit confirmation before enforceRetention or handleDeletionRequest proceeds.

Link to references/implementation.md from the body and move the full inline implementations there, keeping SKILL.md as a concise overview rather than duplicating the same export/retention/GDPR/anonymization code in both files.

De-duplicate SKILL.md and references/implementation.md (both implement export, retention, GDPR access/erasure, and anonymization) so the canonical version lives in one place.

Dimension	Reasoning	Score
Conciseness	The body is mostly code with little conceptual fluff, but it embeds full implementations for export, retention, GDPR, and anonymization that are duplicated in references/implementation.md, so not every token earns its place. It is not level 3 because of that redundancy, and not level 1 because it avoids explaining concepts Claude already knows.	2 / 3
Actionability	Provides complete, executable TypeScript with concrete usage calls, date ranges, and output files, plus actionable data-category and error-handling tables — copy-paste ready rather than pseudocode.	3 / 3
Workflow Clarity	Steps are sequenced (Step 1–5), but destructive batch operations — enforceRetention, handleDeletionRequest — delete traces with no dry-run, count verification, or validation checkpoint, which caps workflow clarity at 2 per the rubric.	2 / 3
Progressive Disclosure	A bundle file references/implementation.md exists but is never linked or signaled from the body, and its content largely duplicates the inline code; content that should be deferred to the reference is inline. It escapes level 1 only because the body itself is well-sectioned with tables.	2 / 3
	Total	9 / 12 Passed

Description

100%

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, third-person description that states concrete capabilities, includes an explicit 'Use when' trigger clause, and lists natural trigger phrases scoped to a distinct niche. It satisfies every dimension at the top anchor.

Dimension	Reasoning	Score
Specificity	Names the domain and lists multiple concrete actions — 'data export, retention, and compliance requirements' — matching the anchor for listing several specific concrete actions rather than vague language.	3 / 3
Completeness	Explicitly answers both 'what' ('Manage Langfuse data export, retention, and compliance requirements') and 'when' with an explicit 'Use when...' clause, so it is not capped at 2.	3 / 3
Trigger Term Quality	Provides natural phrases a user would actually say — 'langfuse data export', 'langfuse retention', 'langfuse GDPR', 'langfuse compliance', 'export langfuse traces' — giving good coverage of common variations.	3 / 3
Distinctiveness Conflict Risk	The 'Langfuse' niche is highly specific and every trigger is prefixed with 'langfuse', making it clearly distinguishable and unlikely to fire for unrelated skills.	3 / 3
	Total	12 / 12 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
allowed_tools_field	'allowed-tools' contains unusual tool name(s)	Warning
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	14 / 16 Passed

Repository: jeremylongshore/claude-code-plugins-plus-skills
Commit: 3022dd3

Reviewed: 1 day ago

Table of Contents

Discovery Implementation Validation

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