event-driven

Event-driven strategy based on sentiment-scored signals from news, announcements, and macro events. The LLM acts as the NLP engine, and event data follows a CSV schema.

Quality

75%

Does it follow best practices?

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Securityby

Low

Low-risk findings worth noting

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tessl review fix ./agent/src/skills/event-driven/SKILL.md

Quality

Content

100%

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

The content is a high-quality, self-contained skill: executable code, concrete schemas and parameters, a clear sequenced workflow, and well-organized sections with no redundant concept explanations.

Dimension	Reasoning	Score
Conciseness	The body is lean and assumes Claude's competence: executable code, compact schema/parameter tables, and a copy-paste prompt template, with no padding explaining basic concepts; the only mildly verbose parts (docstrings, seven pitfalls) still earn their place, fitting the score-3 anchor.	3 / 3
Actionability	Provides fully executable, copy-paste-ready Python (compute_event_signal, combine_signals), an exact CSV schema, a parameter table with defaults, and a ready-to-use scoring prompt template, matching the score-3 'fully executable, copy-paste ready' anchor.	3 / 3
Workflow Clarity	A clear four-step sequence (data collection → LLM analysis → generate CSV → signal aggregation) is paired with a Common Pitfalls section covering error modes like look-ahead bias, duplicates, and comma parsing; this is a signal-generation skill rather than a destructive/batch operation, so the score-3 anchor applies.	3 / 3
Progressive Disclosure	No bundle files exist, so all content lives in one well-organized SKILL.md with clearly headed sections (Purpose, Workflow, Schema, Parameters, Pitfalls, Dependencies); per the simple-skill guidance, well-organized sections with no need for external references score 3, and there are no nested/deep references.	3 / 3
	Total	12 / 12 Passed

Description

50%

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 states the skill's purpose and domain clearly and uses some natural trigger terms, but it omits an explicit 'Use when...' clause and leans on technical jargon, leaving both completeness and trigger-term quality at the mid level.

Suggestions

Add an explicit 'Use when...' clause naming natural triggers, e.g. 'Use when the user wants event-driven trading signals from news, announcements, or macro events.'

Replace or supplement jargon ('NLP engine', 'sentiment-scored signals', 'CSV schema') with concrete user-facing actions like 'scores news sentiment and aggregates it with technical signals into a CSV'.

List a few more concrete actions (e.g., fetch news, score sentiment, write the event CSV, combine with technical signals) to raise specificity and distinctiveness.

Dimension	Reasoning	Score
Specificity	Names the domain ('event-driven strategy', 'sentiment-scored signals from news, announcements, and macro events') and a few concrete actions ('LLM acts as the NLP engine', 'event data follows a CSV schema'), but does not list multiple specific actions, matching the score-2 anchor rather than the multi-action score-3 example.	2 / 3
Completeness	Clearly states what the skill does but lacks any explicit 'Use when...' trigger clause telling Claude when to invoke it; per the judging guidelines a missing trigger clause caps completeness at 2.	2 / 3
Trigger Term Quality	Includes relevant terms a user might say ('news', 'announcements', 'macro events', 'sentiment'), but leans on jargon ('sentiment-scored signals', 'NLP engine', 'CSV schema') and misses common natural variations, fitting the score-2 anchor.	2 / 3
Distinctiveness Conflict Risk	The event-driven sentiment/CSV niche is fairly specific and unlikely to trigger unrelated skills, but it could still overlap with other trading-strategy skills, matching the score-2 'somewhat specific but could overlap' anchor.	2 / 3
	Total	8 / 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/event-driven/SKILL.md
Commit: 8643fcd

Reviewed: 2 days ago

Table of Contents

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