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langsmith-observability

LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.

64

Quality

76%

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tessl review fix ./backend/cli/skills/llm-tools/langsmith/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

68%Weight 40%Scale 1-5

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

A comprehensive, code-rich reference that is actionable and well-structured with valid external references. Its main weaknesses are the absence of explicit validation feedback loops and a large volume of inlined advanced content that would benefit from being moved into the existing reference files.

Suggestions

Add an explicit end-to-end workflow with validation checkpoints (e.g., trace -> verify run landed in LangSmith -> inspect -> iterate) so multi-step operations have feedback loops.

Move advanced sections (manual runs, sampling, async client, hub prompts) into references/advanced-usage.md, leaving SKILL.md as a leaner overview with signaled links.

Make code examples self-contained or note placeholder helpers (vector_store, llm, retrieve_context) so they are directly runnable rather than illustrative.

DimensionReasoningScore

Conciseness

Mostly tight, code-driven content with minimal concept-explanation padding, though the opening line restates the description and a few sections could be trimmed.

4 / 5

Actionability

Extensive, mostly copy-paste-ready code across tracing, evaluation, datasets, and the client API, but several examples reference undefined helpers (vector_store, llm, retrieve_context) making them not directly runnable.

4 / 5

Workflow Clarity

The body is a feature reference rather than a sequenced workflow; the quick-start has a rough sequence and the CI/CD section has an assert checkpoint, but there are no validation feedback loops spanning the operations.

3 / 5

Progressive Disclosure

Two real one-level-deep references (advanced-usage.md, troubleshooting.md) are clearly signaled in a References section, though a large amount of advanced API content is inlined that could live in the reference files.

4 / 5

Total

15

/

20

Passed

Description

83%Weight 40%Scale 1-5

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

A strong description that clearly communicates both capability and trigger context with natural phrasing. It is specific and distinct, with only minor room to add synonym-level trigger terms and more granular action verbs.

DimensionReasoningScore

Specificity

Names the LLM-observability domain and three concrete capabilities ('tracing, evaluation, and monitoring'), but the actions are more abstract than the granular verb list in the anchor-5 example.

4 / 5

Completeness

Explicitly states what it does ('LLM observability platform for tracing, evaluation, and monitoring') and when to use it via a 'Use when...' clause with concrete trigger phrases.

5 / 5

Trigger Term Quality

Includes natural phrases users would say ('debugging LLM applications', 'evaluating model outputs against datasets', 'monitoring production systems', 'building systematic testing pipelines'), though synonym coverage is limited.

4 / 5

Distinctiveness Conflict Risk

Occupies a clear LLM-observability niche with distinct triggers, with only minor overlap risk against general ML-monitoring tools.

4 / 5

Total

17

/

20

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository
synthetic-sciences/openscience
Reviewed

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