Conciseness

Dense and expert-level throughout — it never explains what OpenTelemetry or Pydantic AI is, and lines like "1.x has no `conversation_id=`: tell the user to upgrade; do not work around it" earn their tokens. Falls short of 5 only through minor duplication: `include_binary_content=False` guidance appears in Step 2a, Step 4, and the Known-behavior 413 entry, and duplicate-instrumentor handling repeats between Step 0 item 4 and the Known-behavior 'Duplicate spans' bullet. Version numbers are compatibility constraints (">= 2.16", "logfire 5.1.x pins opentelemetry-sdk<1.45"), not date-branching, so they are not penalized.

Actionability

Fully executable and copy-paste ready: the exact pip install command, a complete 15-line `tracing.py`, the full `logfire.configure(...)` block including the scrubbing callback, `conversation_id=` call patterns for `run`/`run_stream`, and a complete delegation tool example — covering both the plain-OTel and Logfire paths, the common cases. Matches anchor 5.

Workflow Clarity

Clear numbered sequence (Step 0 Detect → 1 Key/region → 2a/2b Install+init → 3 Session id → 4 Content → 5 Tools/errors/sub-agents → 6 Flush → 7 Verify) with explicit validation: a 10-item verification checklist, error-recovery feedback (401 `ingest_unauthorized` → try the other region; unset key → warn and skip export), and a no-Maple-access fallback with console-exporter checks ('silence alone proves nothing'). This is not a destructive/batch operation, so no cap applies.

Progressive Disclosure

Well-organized single-file structure with clearly labeled step headers, a checklist, and 'Do not' section — no bundle files exist, so all content lives in SKILL.md. Falls short of 5 because the 15-bullet 'Known behavior' catalog (token attribute names, cost semantics, version deprecation details, delegation rendering) is background reference material inlined in the main body rather than offloaded to a one-level-deep reference file; it fits anchor 4's 'good structure, minor organization gaps' rather than 3, since the structure is otherwise strong and nothing is buried.

Specificity

Names multiple concrete actions and outcomes — "export Pydantic AI's built-in OpenTelemetry spans (with or without Logfire)" yielding "one Maple Agent Session with transcript, tool calls, sub-agent lanes and tokens" — comprehensive, concrete coverage of what the skill produces, matching the anchor-5 example's level of specificity. Not 4: no meaningful gaps in the action list for this skill's scope.

Completeness

Explicitly answers both: what ("export Pydantic AI's built-in OpenTelemetry spans... one Maple Agent Session with transcript, tool calls, sub-agent lanes and tokens") and when ("Triggers on 'trace my pydantic ai agent', ...") with four concrete trigger phrases. This is an exact match to the anchor-5 example pattern ('Extract text and tables from PDF files... Use when working with PDF files...').

Trigger Term Quality

Four natural trigger phrases ("trace my pydantic ai agent", "add Maple to pydantic ai", "agent sessions for pydantic ai", "OpenTelemetry for pydantic ai") that users would plausibly say, but a few natural synonyms are absent (e.g., "observability", "monitor", "telemetry", "spans"). Fits anchor 4 (good coverage, a few natural terms missing) rather than 5's comprehensive synonym coverage.

Distinctiveness Conflict Risk

A clear niche — the named framework/tool pairing (Pydantic AI + Maple) with pydantic-ai-specific triggers — makes conflict with other tracing or Maple skills minimal. Distinctiveness comes from the specific product names rather than generic tracing language, so it cannot be confused with generic OTel or PDF-style skills.