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

77%

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Advisory

Suggest reviewing before use

Optimize this skill with Tessl

npx tessl skill review --optimize ./plugins/saas-packs/langfuse-pack/skills/langfuse-data-handling/SKILL.md

Quality

Content

64%

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

This is a solid, actionable skill with executable TypeScript code covering the full data lifecycle. Its main weaknesses are the lack of validation/confirmation steps for destructive operations (deletion, retention enforcement) and repetitive pagination boilerplate that inflates token usage. The content would benefit from dry-run modes and being split across multiple files for progressive disclosure.

Suggestions

Add a dry-run/confirmation step before destructive operations (enforceRetention, handleDeletionRequest) — e.g., log what will be deleted and require explicit confirmation before proceeding, plus verify deletion afterward.

Extract the repeated pagination pattern into a shared helper function to reduce code duplication and token usage.

Split GDPR handling and anonymization into separate bundle files (e.g., GDPR.md, ANONYMIZATION.md) and reference them from the main skill for better progressive disclosure.

Dimension	Reasoning	Score
Conciseness	The skill is mostly efficient with executable code examples, but there's some verbosity — the pagination pattern is repeated nearly identically across multiple functions (exportTraces, exportScores, enforceRetention), and the prerequisites section includes unnecessary items like 'Understanding of your compliance requirements' which Claude doesn't need to be told.	2 / 3
Actionability	The skill provides fully executable TypeScript code for every operation — export, retention, GDPR access/erasure, and anonymization. Code includes rate limiting, pagination, and concrete usage examples that are copy-paste ready.	3 / 3
Workflow Clarity	Steps are clearly numbered and sequenced, but there are no validation checkpoints. The deletion operations (enforceRetention, handleDeletionRequest) are destructive batch operations with no confirmation step, dry-run option, or verify-after-delete check, which should cap this at 2 per the rubric's feedback loop requirement.	2 / 3
Progressive Disclosure	The content is well-structured with clear sections and tables, but it's a long monolithic file (~200 lines of code) with no bundle files to offload detail. The anonymization and GDPR sections could be split into separate reference files. External resource links are provided but no internal file references exist.	2 / 3
	Total	9 / 12 Passed

Description

89%

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

This is a well-structured skill description that excels in completeness and distinctiveness by providing explicit 'Use when' and 'Trigger with' clauses tied to a very specific niche (Langfuse data management). The main weakness is that the capability descriptions could be more concrete—listing specific actions like exporting to particular formats or handling specific compliance frameworks beyond just GDPR. Overall, it's a strong description that would perform well in skill selection.

Suggestions

Add more specific concrete actions, e.g., 'Export traces to CSV/JSON, configure TTL-based retention policies, handle GDPR deletion requests, manage data anonymization' to improve specificity.

Dimension	Reasoning	Score
Specificity	Names the domain (Langfuse data management) and some actions (export, retention, compliance), but the actions are somewhat high-level rather than listing multiple concrete specific operations like 'export trace data to CSV, configure TTL retention policies, delete user data for GDPR requests'.	2 / 3
Completeness	Clearly answers both 'what' (manage Langfuse data export, retention, and compliance) and 'when' (explicit 'Use when' clause with specific triggers, plus a 'Trigger with phrases like' section providing concrete activation terms).	3 / 3
Trigger Term Quality	Includes strong natural trigger terms that users would actually say: 'langfuse data export', 'langfuse retention', 'langfuse GDPR', 'langfuse compliance', 'export langfuse traces'. These cover multiple natural variations and are specific to the skill's niche.	3 / 3
Distinctiveness Conflict Risk	Highly distinctive due to the specific combination of Langfuse + data export/retention/compliance. The trigger terms are narrowly scoped to Langfuse data management, making it very unlikely to conflict with other skills.	3 / 3
	Total	11 / 12 Passed

Validation

81%

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 — 9 / 11 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	9 / 11 Passed

Repository: jeremylongshore/claude-code-plugins-plus-skills
Commit: 00c0702

Reviewed: 2 days ago

Table of Contents

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