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".
64
77%
Does it follow best practices?
Impact
—
No eval scenarios have been run
Passed
No known issues
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npx tessl skill review --optimize ./plugins/saas-packs/langfuse-pack/skills/langfuse-hello-world/SKILL.mdQuality
Discovery
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 description with strong completeness and excellent trigger terms. Its main weakness is that the 'what' portion is somewhat thin—it describes only one action (creating a minimal trace example) rather than listing specific concrete steps or capabilities. Overall it would perform well in skill selection due to its clear niche and explicit trigger guidance.
Suggestions
Add more specific concrete actions to the 'what' portion, e.g., 'Initializes the Langfuse client, creates a trace with spans, and logs a sample generation to verify connectivity.'
| 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 like creating spans, logging generations, or setting up the client. | 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 | Includes excellent natural trigger terms that users would actually say: 'langfuse hello world', 'langfuse example', 'langfuse quick start', 'first langfuse trace', 'simple langfuse code'. These cover common variations of how someone would request this. | 3 / 3 |
Distinctiveness Conflict Risk | Langfuse is a specific tool, and the description narrows further to 'minimal working example' / 'hello world' patterns, making it very unlikely to conflict with other skills. The trigger terms are highly specific to this niche. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
64%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The skill excels at actionability with complete, executable code examples across multiple SDK versions and languages, plus a helpful error handling table. However, it's overly verbose for a 'hello world' skill by including four separate approaches inline rather than focusing on one canonical example and referencing alternatives. The step numbering implies a sequential workflow but the steps are actually independent alternatives, which could confuse Claude.
Suggestions
Restructure to show one canonical example (v4+ modular SDK) as the primary path, and move the observe wrapper, legacy v3, and Python examples to referenced files or collapse them into a brief 'Alternatives' section with links.
Rename or restructure the steps to clarify they are alternatives, not sequential steps (e.g., 'Option A: Modular SDK', 'Option B: observe wrapper') to avoid implying all four must be executed.
Add an explicit verification checkpoint after the main example: 'Verify: Open your Langfuse dashboard and 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 explanatory comments are unnecessary for Claude. | 2 / 3 |
Actionability | All code examples are fully executable with proper imports, complete function bodies, and necessary setup/teardown (SDK initialization, flush calls). The error handling table provides specific solutions for common issues. Code is copy-paste ready across both TypeScript and Python. | 3 / 3 |
Workflow Clarity | The steps are labeled sequentially but they're actually independent alternatives rather than a true sequential workflow — Step 1-3 are different TypeScript approaches and Step 4 is Python. There's no validation checkpoint to confirm the trace actually appeared in the dashboard before proceeding, and no explicit feedback loop for troubleshooting beyond the error table. | 2 / 3 |
Progressive Disclosure | The skill includes external resource links and references to next steps (langfuse-core-workflow-a, langfuse-local-dev-loop), but the body itself is monolithic with ~150 lines of code examples that could be split. The four alternative approaches would benefit from being in separate referenced files, with the main SKILL.md showing just one canonical example. | 2 / 3 |
Total | 9 / 12 Passed |
Validation
81%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 | |
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