langfuse-observability
Set up comprehensive observability for Langfuse with metrics, dashboards, and alerts. Use when implementing monitoring for LLM operations, setting up dashboards, or configuring alerting for Langfuse integration health. Trigger with phrases like "langfuse monitoring", "langfuse metrics", "langfuse observability", "monitor langfuse", "langfuse alerts", "langfuse dashboard".
80
77%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./plugins/saas-packs/langfuse-pack/skills/langfuse-observability/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 skill description with strong trigger terms, clear 'when' guidance, and excellent distinctiveness due to the Langfuse-specific focus. Its main weakness is that the capability descriptions could be more concrete—listing specific tools, integrations, or detailed actions rather than high-level categories like 'metrics, dashboards, and alerts'.
Suggestions
Add more specific concrete actions, e.g., 'configure Prometheus/Grafana dashboards for Langfuse trace latency, token usage, and error rates' instead of the generic 'metrics, dashboards, and alerts'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description names the domain (Langfuse observability) and mentions some actions (metrics, dashboards, alerts), but doesn't list specific concrete actions like 'create Grafana dashboards', 'configure Prometheus exporters', or 'set up alerting rules for latency thresholds'. The actions remain somewhat high-level. | 2 / 3 |
Completeness | Clearly answers both 'what' (set up comprehensive observability with metrics, dashboards, and alerts) and 'when' (implementing monitoring for LLM operations, setting up dashboards, configuring alerting for Langfuse integration health), with an explicit 'Use when' clause and a 'Trigger with phrases' section. | 3 / 3 |
Trigger Term Quality | Excellent trigger term coverage with explicit natural phrases: 'langfuse monitoring', 'langfuse metrics', 'langfuse observability', 'monitor langfuse', 'langfuse alerts', 'langfuse dashboard'. These are terms users would naturally use, and the description also includes broader terms like 'LLM operations' and 'alerting'. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive due to the specific focus on Langfuse, which is a niche LLM observability platform. The repeated use of 'Langfuse' as a qualifier makes it very unlikely to conflict with generic monitoring or dashboard skills. | 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.
This skill provides highly actionable, executable guidance for setting up Langfuse observability with Prometheus, Grafana, and alerting. Its main weaknesses are length (could benefit from splitting detailed configs into separate files) and the absence of validation/verification steps between stages of the setup workflow. The code quality and specificity are strong throughout.
Suggestions
Add verification steps after key stages (e.g., 'curl localhost:3000/metrics to confirm metrics are exposed', 'check Prometheus targets page to verify scraping', 'trigger a test alert to confirm alerting pipeline works').
Split the detailed Grafana dashboard JSON and alert rules into separate referenced files (e.g., GRAFANA_DASHBOARD.json, ALERT_RULES.yml) to keep the SKILL.md as a concise overview.
Trim the tracedLLM wrapper to show only the essential instrumentation pattern, with a note to adapt for specific use cases.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is fairly long with substantial code blocks. Some content is efficient (metrics reference table, alert rules), but the full instrumented LLM wrapper in Step 2 is quite verbose and could be trimmed. The error handling table adds value but some entries are somewhat obvious. | 2 / 3 |
Actionability | Excellent actionability throughout — every step includes fully executable TypeScript code, complete YAML configs, and copy-paste ready Grafana dashboard JSON and Prometheus alert rules. The code is concrete and specific, not pseudocode. | 3 / 3 |
Workflow Clarity | Steps are clearly numbered and sequenced (1-6), but there are no validation checkpoints or feedback loops. After setting up metrics, dashboards, and alerts, there's no step to verify metrics are being scraped, dashboards are rendering, or alerts are firing correctly. For an observability setup involving multiple systems, verification steps are important. | 2 / 3 |
Progressive Disclosure | The skill includes external resource links at the bottom, but the body itself is a monolithic ~200-line document. The Grafana dashboard JSON, detailed wrapper code, and alert rules could be split into referenced files, keeping the SKILL.md as a concise overview with pointers. | 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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