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

83%

Does it follow best practices?

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Adds up to 20 points to the overall score

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Securityby

Low

Low-risk findings worth noting

Quality

Content

72%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.

Highly actionable code examples undermined by missing validation checkpoints on destructive operations and a reference file that is present but neither linked nor cleanly separated from the inlined body.

Suggestions

Add explicit validation/verification checkpoints to the GDPR deletion and retention-enforcement workflows (e.g., confirm count with the user, verify deletion succeeded, log results) so destructive operations don't proceed unverified.

Either signpost references/implementation.md from the body (e.g., 'See implementation.md for the audit trail and CSV export helpers') or remove the duplicated code so the body and reference are cleanly separated at one level of depth.

Trim the redundant usage/console.log blocks and consolidate the duplicated export, retention, and anonymization code that appears in both the body and the reference file.

Dimension	Reasoning	Score
Conciseness	Mostly efficient executable code with little concept padding, but contains minor redundancy (export/retention/anonymization code duplicated in references/implementation.md) and a few unneeded console.log usage blocks that could be trimmed.	4 / 5
Actionability	Each step provides complete, copy-paste-ready TypeScript implementations with real API calls, pagination, and rate-limit handling covering the common cases.	5 / 5
Workflow Clarity	Steps are sequenced (Step 1-5), but destructive/batch operations (GDPR deletion loop, retention enforcement delete loop) lack validation or verification checkpoints before proceeding, which caps clarity at 3 per the destructive-operation rule.	3 / 5
Progressive Disclosure	A references/implementation.md bundle exists but is never signposted or linked from the body, and substantial code is duplicated across the body and the reference file rather than cleanly split, leaving organization only partially realized.	3 / 5
	Total	15 / 20 Passed

Description

95%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 clearly states capabilities and gives explicit natural trigger phrases. Minor abstraction in the 'compliance' action keeps specificity just below maximum.

Dimension	Reasoning	Score
Specificity	Names the Langfuse domain and several concrete actions ('exporting trace data, configuring retention policies, implementing data compliance'), but actions like 'compliance' remain somewhat abstract rather than fully enumerated, leaving minor gaps versus a 5.	4 / 5
Completeness	Explicitly answers both what ('Manage Langfuse data export, retention, and compliance requirements') and when ('Use when exporting trace data...') with concrete trigger phrases, matching the top anchor.	5 / 5
Trigger Term Quality	Provides explicit natural trigger phrases users would say ('langfuse data export', 'langfuse retention', 'langfuse GDPR', 'langfuse compliance', 'export langfuse traces') with good synonym coverage, matching the comprehensive-coverage anchor.	5 / 5
Distinctiveness Conflict Risk	Targets a clear Langfuse-specific niche with distinct, prefixed trigger terms ('langfuse ...') that are unlikely to fire for unrelated skills, 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
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
Path: plugins/saas-packs/langfuse-pack/skills/langfuse-data-handling/SKILL.md
Commit: 8585d6d

Reviewed: 13 days ago

Table of Contents

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