langfuse-core-workflow-b

Execute Langfuse secondary workflow: Evaluation, scoring, and datasets. Use when implementing LLM evaluation, adding user feedback, or setting up automated quality scoring and experiment datasets. Trigger with phrases like "langfuse evaluation", "langfuse scoring", "rate llm outputs", "langfuse feedback", "langfuse datasets", "langfuse experiments".

Quality

81%

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Advisory

Suggest reviewing before use

Quality

Content

72%

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

This is a well-structured, highly actionable skill with complete executable TypeScript examples covering the full evaluation workflow. Its main weakness is the lack of validation checkpoints between steps (e.g., verifying dataset creation before running experiments) and some minor verbosity in comments. The progressive disclosure and cross-referencing to other skills is well done.

Suggestions

Add validation checkpoints between steps, e.g., after Step 4 verify the dataset exists before proceeding to Step 5's experiment run, and after Step 5 verify scores appear in the UI.

Trim obvious inline comments (e.g., '// Thumbs up/down', '// Optional: score a specific generation') to improve conciseness.

Dimension	Reasoning	Score
Conciseness	The skill is mostly efficient with executable code examples, but includes some unnecessary verbosity like inline comments explaining obvious things ('Optional: score a specific generation', 'Thumbs up/down') and the user feedback endpoint example is somewhat tangential. The error handling table is useful but some entries are obvious.	2 / 3
Actionability	Every step provides fully executable TypeScript code with concrete examples — scoring traces, collecting feedback, fetching prompts, creating datasets, running experiments, and LLM-as-a-Judge evaluation. All code is copy-paste ready with realistic values and proper imports.	3 / 3
Workflow Clarity	Steps are clearly sequenced (1-6) and logically ordered, but there are no explicit validation checkpoints or feedback loops. For a workflow involving dataset creation and experiment execution, there should be verification steps (e.g., confirm dataset was created before running experiments, validate scores appeared in UI).	2 / 3
Progressive Disclosure	The skill is well-structured with a clear overview, sequential steps, an error handling table, and external resource links. It references related skills (langfuse-core-workflow-a, langfuse-common-errors, langfuse-ci-integration) for navigation. Content is appropriately scoped without being monolithic.	3 / 3
	Total	10 / 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 clearly identifies its niche (Langfuse evaluation and scoring workflows), provides explicit trigger guidance, and distinguishes itself from other skills. The main weakness is that the specific capabilities could be more concrete—listing actual operations rather than broad categories like 'evaluation' and 'scoring'.

Suggestions

Add more concrete action verbs describing specific operations, e.g., 'Create evaluation datasets, attach scores to traces, configure automated evaluators, collect user feedback on LLM outputs.'

Dimension	Reasoning	Score
Specificity	Names the domain (Langfuse evaluation/scoring/datasets) and some actions (evaluation, scoring, user feedback, automated quality scoring, experiment datasets), but the actions are somewhat high-level and not fully concrete (e.g., doesn't specify specific operations like 'create dataset items', 'run evaluators', 'attach scores to traces').	2 / 3
Completeness	Clearly answers both 'what' (evaluation, scoring, datasets for Langfuse) and 'when' (explicit 'Use when' clause with scenarios plus a 'Trigger with phrases like' section listing specific trigger terms).	3 / 3
Trigger Term Quality	Includes a good set of natural trigger terms: 'langfuse evaluation', 'langfuse scoring', 'rate llm outputs', 'langfuse feedback', 'langfuse datasets', 'langfuse experiments'. These cover multiple natural phrasings a user might use.	3 / 3
Distinctiveness Conflict Risk	The description is clearly scoped to Langfuse's secondary workflow (evaluation/scoring/datasets), distinguishing it from general LLM tools or even other Langfuse workflows (e.g., tracing). The 'langfuse' prefix on most trigger terms reduces conflict risk significantly.	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

Discovery Implementation Validation

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