Conciseness

The body is efficient — dense tables, lean code, no padding with concepts Claude already knows — but decay_lambda and alpha semantics are each restated across the docstring, inline comments, and the Parameters table, and 'No additional dependencies. LLM analysis is handled by the Agent itself...' could be trimmed, matching the efficient-with-minor-trimming anchor rather than the every-token-earns-its-place anchor.

Actionability

Both functions (compute_event_signal, combine_signals) are fully executable with typed signatures and docstrings, and the CSV schema, LLM prompt template, and pip install command are copy-paste ready; the one minor gap is that the workflow step references 'signal_engine.py' as a provided script while no such bundle file exists — the logic lives only inline — matching the mostly-executable-with-minor-gaps anchor.

Workflow Clarity

The four-step workflow (fetch -> LLM scoring -> event CSV -> aggregation) is clearly sequenced, and validation is largely embedded: the code enforces event_date <= trade_date with an explicit anti-look-ahead comment and pitfalls cover dedup, CSV quoting, and threshold handling. A minor gap is the absence of an explicit 'validate the CSV parses' checkpoint in the sequence itself, so it fits the clear-sequence-with-most-checkpoints anchor rather than the explicit-validation-with-feedback-loops anchor.

Progressive Disclosure

This is a single self-contained SKILL.md with no bundle files and well-organized, clearly headed sections in a logical order (purpose -> workflow -> schema -> event types -> aggregation -> parameters -> prompt -> pitfalls), making navigation easy; at ~175 lines with the prompt template and event-type detail inlined it stops short of the ideal split, matching the good-structure-with-minor-organization-gaps anchor.

Specificity

The description names the domain ('event-driven strategy based on sentiment-scored signals from news, announcements, and macro events') and one or two concrete actions (LLM sentiment scoring, 'event data follows a CSV schema'), but omits core capabilities like signal aggregation and combination with technical signals, matching the anchor for domain plus 1-2 concrete actions rather than the several-specific-actions anchor above.

Completeness

The 'what' is clear (event-driven strategy via sentiment-scored signals with the LLM as NLP engine and a CSV data schema), but there is no 'Use when...' clause or any equivalent trigger guidance, which per the rubric caps completeness at 3.

Trigger Term Quality

It includes natural terms users would say — 'news', 'announcements', 'macro events', 'sentiment', 'event-driven' — giving good keyword coverage, though common synonyms like 'headlines', 'earnings', or 'press releases' are missing, fitting the good-coverage-with-a-few-gaps anchor rather than the comprehensive-synonyms anchor.

Distinctiveness Conflict Risk

The news/sentiment event-signal niche is mostly distinct with terms unlikely to trigger unrelated skills, but without explicit trigger phrases it retains minor overlap risk with sibling technical or sentiment strategy skills, matching the 'mostly distinct' anchor rather than the clear-niche anchor.