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behavioral-finance

Behavioral finance applications: theories of overreaction and underreaction, behavioral explanations for momentum and reversal, investor sentiment cycles, cognitive-bias checklists, and debiasing quantitative strategies.

62

Quality

73%

Does it follow best practices?

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SecuritybySnyk

Passed

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tessl review fix ./agent/src/skills/behavioral-finance/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

65%

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

The body is highly actionable with concrete quant signals, thresholds, and checklists, but it explains some basic behavioral concepts Claude already knows and is a single monolithic file with no progressive disclosure or embedded validation checkpoints.

Suggestions

Trim explanations of well-known biases (loss aversion, anchoring) and keep only the quant-detection and debiasing columns that add new information.

Split the cognitive-bias checklist and sentiment-indicator reference into separate reference files linked from the overview, and make the sentiment Python block fully executable by defining the normalize/weighted_sum helpers.

Add an explicit validation step to each framework (e.g. out-of-sample check before adopting a signal) so the workflow has clear checkpoints.

DimensionReasoningScore

Conciseness

Mostly efficient and rich in applied specifics, but spends tokens restating concepts Claude already knows (e.g. explaining anchoring, loss aversion, and the 'core assumption' of investor irrationality) that could be trimmed without losing the valuable China A-share thresholds.

2 / 3

Actionability

Highly concrete throughout: exact thresholds (SUE > 2σ, RSI(5) < 10, turnover < 0.5%), a computable capital_gain_overhang formula, weighted sentiment components, and explicit buy/sell checklists — copy-paste ready guidance.

3 / 3

Workflow Clarity

Frameworks are numbered and use decision rules and checklists (e.g. 'at least 3 items' contrarian conditions), but there are no explicit validation checkpoints or validate→fix→retry feedback loops embedded in the analysis steps, leaving checkpoints implicit.

2 / 3

Progressive Disclosure

Well-organized into clear sections (Overview, Core Concepts, Analysis Framework, Output Format, Notes) but entirely monolithic with no external references, so detailed material like the bias tables and sentiment-indicator reference stays inline rather than being split out.

2 / 3

Total

9

/

12

Passed

Description

82%

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, uses natural domain trigger terms, and occupies a clear niche, but it omits an explicit 'Use when...' clause so it answers 'what' well while leaving 'when' only implied.

Suggestions

Add an explicit trigger clause, e.g. 'Use when analyzing momentum/reversal strategies, gauging market sentiment extremes, or debiasing quantitative trading rules.'

Lead with active verbs describing deliverables (e.g. 'Diagnoses', 'Builds', 'Debiases') to convert topic nouns into concrete actions.

DimensionReasoningScore

Specificity

Lists five concrete, distinct capabilities ('theories of overreaction and underreaction, behavioral explanations for momentum and reversal, investor sentiment cycles, cognitive-bias checklists, and debiasing quantitative strategies') matching the multiple-specific-actions anchor.

3 / 3

Completeness

Clearly answers 'what' with a concrete capability list, but lacks any 'Use when...' or equivalent explicit 'when to use it' clause, so the when half is only implied — capped at 2 per the judging guideline.

2 / 3

Trigger Term Quality

Includes natural practitioner terms a user would actually say — 'behavioral finance', 'momentum and reversal', 'investor sentiment', 'cognitive bias', 'debiasing' — giving good coverage of likely trigger phrasing.

3 / 3

Distinctiveness Conflict Risk

The behavioral-finance niche with specific triggers (momentum/reversal, sentiment cycles, debiasing) is clearly distinct and unlikely to collide with other skills' trigger terms.

3 / 3

Total

11

/

12

Passed

Validation

93%

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

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository
HKUDS/Vibe-Trading
Reviewed

Table of Contents

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