Conciseness

The body is lean and assumes Claude's competence: it opens directly with the two-layer architecture and repo-specific file paths, never explains what LLM observability or tracing is, and every section carries non-obvious repo knowledge (exact entry-point group 'ddtrace.integration_hooks', exact metric key constants, the finish_reason merge/omit/join rules). It matches the 5 anchor — every token earns its place; the 4 anchor's 'minor instances of over-explanation' do not materialize.

Actionability

Guidance is fully concrete and executable: exact method signatures ('_set_base_span_tags(span, **kwargs)', '_llmobs_set_tags(span, args, kwargs, response, operation)'), a copy-paste-ready Message/ToolCall/ToolResult/AudioPart code block, a table of reference integrations with exact file paths, exact env flags ('DD_TRACE_DEBUG=true'), and precise constraints ('submit_to_llmobs=True', 'ctx.dispatch_ended_event()'). Specific examples cover the common cases, matching the 5 anchor.

Workflow Clarity

The workflow is legible and mostly checkpointed: an explicit 'Always read 1-2 references before writing or modifying LLMObs code' directive, the methods to implement, a constraints checklist, and symptom-to-check debugging tips ('No LLMObs spans -- check submit_to_llmobs=True, ctx.dispatch_ended_event(), and llmobs_enabled'). However, the build sequence is implied by section order rather than an explicit numbered step flow, and the validate/retry loop lives in the referenced guides — fitting the 4 anchor (clear sequence, most checkpoints, minor gaps) rather than 5's explicit validation steps and feedback loops, but clearly above 3's implicit checkpoints.

Progressive Disclosure

The SKILL.md is a genuine overview (~150 lines) with clearly signaled one-level references ('[Implementation Guide] ... LLM-specific steps', '[Failure Modes] ... All 8 failure modes', '[Testing Guide] ... VCR cassettes, suitespec') and 875 lines of detail properly pushed to the bundle. Two minor gaps keep it below 5: nova-sonic.md exists in the bundle but is only reachable through implementation-guide.md (two levels deep) and is absent from the Reference Files section, and the ~30-line Response Metadata policy block is fairly dense for an overview file. It is well above the 3 anchor's 'content that should be separate is inline'.

Specificity

The description lists multiple concrete capabilities and topics: 'creating, modifying, or debugging LLMObs integrations' plus 'Covers BaseLLMIntegration, stream handling, message extraction, usage metrics, tool call parsing, and VCR-based testing patterns' — named classes and specific technical areas with no vague filler. This matches the anchor for multiple specific concrete actions with comprehensive coverage; nothing significant for this skill's scope is missing, so it is not the 4 anchor ('minor gaps in coverage').

Completeness

It explicitly answers both questions: 'what' via 'dd-trace-py LLMObs integration development guide ... Covers BaseLLMIntegration, stream handling, ...' and 'when' via an explicit 'Use when creating, modifying, or debugging LLMObs integrations for LLM/AI libraries in the Python tracer' clause plus concrete trigger phrases. This matches the 5 anchor exactly; the 4 anchor requires a 'when' that 'could be more explicit', which does not apply here.

Trigger Term Quality

The trigger list is comprehensive and natural for the domain: 'llmobs', 'LLMObs', 'BaseLLMIntegration', 'llmobs_set_tags', 'BaseStreamHandler', 'submit_to_llmobs', 'integration.trace', 'LLM span', 'VCR', 'cassette', 'anthropic', 'openai', 'google_genai', 'claude_agent_sdk', 'generative-ai', 'LLM integration', 'llmobs_enabled' — covering case variants, provider/library names, and both jargon and natural phrases a user would say. It matches the 5 anchor (synonyms and variations included); it is well above 4 ('a few natural terms missing').

Distinctiveness Conflict Risk

The niche is clear and most triggers ('llmobs', 'BaseLLMIntegration', 'submit_to_llmobs', 'integration.trace') are distinct to dd-trace-py LLMObs work, but broad provider terms like 'anthropic', 'openai', 'generative-ai', and 'VCR' would also naturally occur in unrelated conversations about those providers/libraries. This fits the 4 anchor (mostly distinct, minor overlap risk with closely related skills) rather than 5, whose 'minimal conflict risk' is undercut by those broad trigger terms.