seasonal

Seasonal/calendar-effect strategy. Generates trading signals from time-based patterns such as month-of-year effects and day-of-week effects. Suitable for any OHLCV data.

Quality

78%

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tessl review fix ./agent/src/skills/seasonal/SKILL.md

Quality

Content

85%

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

The body is concise, well-structured, and clearly sequences a simple signal-generation workflow with useful pitfalls. Its main gap is the absence of an executable code example implementing the described logic.

Suggestions

Add a minimal executable snippet showing signal generation, e.g. using pd.DatetimeIndex.month/weekday to map bullish/bearish months to 1/-1/0, since the dependencies section already implies code.

Include an explicit neutral-month verification step ('assert signal is 0 for months not in bullish/bearish lists') to turn the pitfall into a concrete checkpoint.

Show one combined-mode example (month × weekday gating) to make the optional overlay immediately reproducible.

Dimension	Reasoning	Score
Conciseness	The body is lean and table/bullet-driven with no padding of concepts Claude already knows (it does not explain what OHLCV or pandas is); every section — pitfalls like 'pd.DatetimeIndex.month starts from 1' — earns its place, matching the 'lean and efficient' anchor.	3 / 3
Actionability	Parameters, signal values (1/-1/0), and logic are concrete, but there is no executable code implementing signal generation despite a 'pip install pandas numpy' dependencies section, so guidance is actionable yet incomplete — closer to the 'some concrete guidance but incomplete' anchor.	2 / 3
Workflow Clarity	For this single-purpose skill the signal-generation flow is unambiguous and clearly sequenced (month effect default → optional weekday overlay → combined month×weekday gate), and the pitfalls section supplies verification notes ('Neutral months... must output 0 and must not be skipped'), satisfying the simple-skill allowance for a 3.	3 / 3
Progressive Disclosure	It is a self-contained, well-organized single file with clear sections (Purpose, Signal Logic, reference and parameter tables, Pitfalls) and no need for external references, meeting the simple-skill criterion for a 3 on progressive disclosure.	3 / 3
	Total	11 / 12 Passed

Description

72%

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

The description is specific and distinctive with good natural trigger terms, but it lacks an explicit 'Use when...' clause, so it answers 'what' well while leaving 'when' only implied. Adding explicit invocation guidance would raise completeness.

Suggestions

Add an explicit trigger clause, e.g. 'Use when building calendar/seasonal trading strategies or when the user mentions month-of-year, day-of-week, or sell-in-May effects.'

Mention concrete outputs or a second action (e.g. 'outputs long/short/flat signals for backtesting') to strengthen specificity.

Optionally list common user phrasings ('seasonal strategy', 'calendar effect backtest') to broaden trigger-term coverage.

Dimension	Reasoning	Score
Specificity	It names the domain concretely ('Seasonal/calendar-effect strategy') and one clear action ('Generates trading signals from time-based patterns such as month-of-year effects and day-of-week effects'), but lists only a single action rather than multiple distinct concrete actions, so it is closer to 'names domain and some actions' than to the multi-action top anchor.	2 / 3
Completeness	It clearly states what the skill does, but there is no 'Use when...' clause or equivalent explicit trigger guidance ('Suitable for any OHLCV data' describes data applicability, not when to invoke it), so per the judging guidelines completeness is capped at 2.	2 / 3
Trigger Term Quality	Natural domain terms a user would say are well covered — 'seasonal', 'calendar-effect', 'trading signals', 'month-of-year effects', 'day-of-week effects' — matching the 'good coverage of natural terms' anchor.	3 / 3
Distinctiveness Conflict Risk	'Seasonal/calendar-effect strategy' is a clear, narrow niche with distinct triggers unlikely to overlap with other skills, matching the 'clear niche with distinct triggers' anchor.	3 / 3
	Total	10 / 12 Passed

Validation

93%

Warnings & errors only

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

Validation — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	15 / 16 Passed

Repository: HKUDS/Vibe-Trading
Path: agent/src/skills/seasonal/SKILL.md
Commit: 8643fcd

Reviewed: 2 days ago

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