Conciseness

The 379-line body is noticeably padded: the legacy-endpoint retirement warning is repeated four times (Overview, Step 3 error handling, Resources 'Legacy endpoint retired', Error Handling), 402-tier handling three times, and there are two overlapping error-handling sections; Step 5's impact primer ("FOMC rate decisions, ECB/BOJ policy meetings... High Impact", "High inflation environment → CPI/PPI elevated importance") re-teaches market knowledge Claude already has, and the full worked example event entry with invented market implications adds bulk. This fits the 2 anchor ('several unnecessary explanations or padded sections') — it is not a 1 since it never explains basics like what JSON is, and not a 3 because duplication is pervasive rather than occasional.

Actionability

Guidance is mostly copy-paste ready: all documented flags (--from, --to, --api-key, --format, --output) match the actual argparse in scripts/get_economic_calendar.py, the JSON event schema is shown, and error semantics are concrete (exit code 1, ZERO_RESULT_REASON=... stderr markers). The one real gap is the script path prefix "skills/economic-calendar-fetcher/scripts/get_economic_calendar.py", which does not match the bundle layout (scripts/get_economic_calendar.py relative to the skill root, per the script's own usage docstring), so it lands at 4 rather than 5.

Workflow Clarity

The six-step workflow is clearly sequenced with explicit validation and feedback loops: date-range validation rules in Step 2 (start ≤ end, ≤90 days, past-date warning) and per-error recovery guidance in Step 3 (verify key on 401, wait/re-run after 429, upgrade guidance on 402, investigate rather than assume on empty/404). This matches the 5 anchor ('explicit validation steps; feedback loops for error recovery').

Progressive Disclosure

Structure is good: a real one-level-deep reference (references/fmp_api_documentation.md) is clearly signaled in Resources with a content outline, and the scripts are documented. Minor gaps keep it at 4: the body duplicates large API-details and error-handling sections that belong in the reference, the referenced doc is written against the retired legacy endpoint (bannered as historical), and the vendored scripts/fmp_client.py is mentioned only parenthetically rather than listed in Resources.

Specificity

Quotes multiple concrete actions — "Fetch upcoming economic events and data releases using FMP API", "Retrieve scheduled central bank decisions, employment reports, inflation data, GDP releases" — plus parameter defaults ("default: next 7 days") and an explicit output contract ("The script outputs raw JSON or text; the assistant filters, assesses impact, and generates the Markdown report"). Coverage is comprehensive for the domain, matching the 5 anchor rather than the 4 anchor's 'minor gaps'.

Completeness

The 'what' is clear and detailed, but there is no 'Use when...' clause or equivalent explicit trigger guidance anywhere in the description — only parameter framing like "for specified date ranges (default: next 7 days)". Per the rubric guideline, a missing explicit trigger clause caps completeness at 3, which the text fits exactly; it is not a 4 because 'when' is absent rather than merely imprecise.

Trigger Term Quality

Good natural-term coverage — "economic events", "data releases", "central bank decisions", "employment reports", "inflation data", "GDP releases" — but it omits "economic calendar" (the skill's own name and the most common user phrasing) and variants like "jobs report". This matches the 4 anchor ('a few natural terms missing') rather than the 5 anchor's synonym-complete coverage.

Distinctiveness Conflict Risk

The niche is distinct — scheduled economic indicators via a named provider (FMP) with clear role division between script and assistant — and trigger territory (economic releases, central bank decisions) is unlikely to fire for unrelated skills. It matches the 5 anchor ('clear niche with distinct triggers; minimal conflict risk').