langfuse-hello-world

Create a minimal working Langfuse trace example. Use when starting a new Langfuse integration, testing your setup, or learning basic Langfuse tracing patterns. Trigger with phrases like "langfuse hello world", "langfuse example", "langfuse quick start", "first langfuse trace", "simple langfuse code".

Quality

77%

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./plugins/saas-packs/langfuse-pack/skills/langfuse-hello-world/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 hello world skill with excellent actionability — all code examples are complete and executable with proper imports and setup. However, it suffers from presenting four alternative approaches as sequential steps, which is misleading, and the overall content is more verbose than necessary for a quick-start skill. The error handling table and trace hierarchy diagram are strong additions.

Suggestions

Restructure Steps 1-4 as clearly labeled alternatives (e.g., 'Option A: v4+ Modular SDK', 'Option B: Python') rather than sequential steps, since they are independent approaches.

Move the legacy v3 SDK example and Python example to separate bundle files (e.g., LEGACY_V3.md, PYTHON.md) and reference them from the main skill to reduce inline bulk.

Add a verification checkpoint after each example: 'Open your Langfuse dashboard at LANGFUSE_BASE_URL → confirm the hello-world trace appears with the expected hierarchy before proceeding.'

Dimension	Reasoning	Score
Conciseness	The skill provides four separate hello world examples (v4 modular, observe wrapper, legacy v3, Python) which creates significant redundancy. While each example is individually clean, the overall content is verbose for a 'hello world' skill — the v3 legacy example and the observe wrapper example could be condensed or moved to separate files. Some sections like the overview explain things Claude already knows.	2 / 3
Actionability	All code examples are fully executable with proper imports, complete function bodies, and copy-paste ready patterns. The error handling table provides specific solutions for common issues, and the examples include necessary setup like SDK initialization and flush calls.	3 / 3
Workflow Clarity	The steps are labeled sequentially but they're actually independent alternatives (v4 vs observe wrapper vs v3 vs Python), not a sequential workflow. This is misleading — they should be presented as options, not steps. There's no validation checkpoint to confirm the trace actually appeared in the dashboard before proceeding.	2 / 3
Progressive Disclosure	The skill includes a lot of inline code that could be split — the legacy v3 example and Python example could be separate reference files. The 'Next Steps' and 'Resources' sections provide good navigation, but with no bundle files, all content is crammed into one file. The trace hierarchy diagram is a nice inline summary though.	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 with strong trigger terms and clear 'what/when' guidance. Its main weakness is that the capability description is somewhat narrow — it only describes one action (creating a minimal trace example) rather than listing multiple concrete actions. However, for a narrowly-scoped skill like a 'hello world' generator, this level of specificity may be appropriate.

Suggestions

Consider listing 2-3 more specific actions the skill performs, e.g., 'Sets up Langfuse client initialization, creates a basic trace with spans, and demonstrates observation nesting.'

Dimension	Reasoning	Score
Specificity	It names the domain (Langfuse tracing) and one action ('Create a minimal working Langfuse trace example'), but doesn't list multiple concrete actions beyond that single capability.	2 / 3
Completeness	Clearly answers both 'what' (create a minimal working Langfuse trace example) and 'when' (starting a new integration, testing setup, learning basic patterns) with explicit trigger phrases.	3 / 3
Trigger Term Quality	Excellent coverage of natural trigger terms users would say: 'langfuse hello world', 'langfuse example', 'langfuse quick start', 'first langfuse trace', 'simple langfuse code'. These are realistic phrases a user would type.	3 / 3
Distinctiveness Conflict Risk	Highly distinctive — Langfuse is a specific tool, and the focus on 'hello world' / minimal trace examples creates a clear niche that is 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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