edge

Find mispriced markets by comparing to external models and data sources

Quality

52%

Does it follow best practices?

Impact

Pending

No eval scenarios have been run

Securityby

Advisory

Suggest reviewing before use

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npx tessl skill review --optimize ./src/skills/bundled/edge/SKILL.md

Quality

Discovery

32%

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 identifies a specific niche (finding mispriced markets) but lacks the completeness needed for reliable skill selection. It provides no explicit trigger guidance and uses somewhat generic language around 'external models and data sources' without specifying concrete actions or user scenarios.

Suggestions

Add a 'Use when...' clause with trigger terms like 'arbitrage', 'mispricing', 'odds comparison', 'market inefficiency', or 'pricing discrepancy'

List specific concrete actions such as 'fetch external odds', 'calculate expected value', 'identify arbitrage opportunities', or 'compare pricing across platforms'

Include natural user terms for the specific market type (e.g., 'sports betting', 'prediction markets', 'financial markets') to improve trigger term coverage

Dimension	Reasoning	Score
Specificity	Names the domain (market analysis/pricing) and describes a general action (comparing to external models and data sources), but lacks specific concrete actions like 'calculate arbitrage opportunities', 'fetch odds from APIs', or 'generate pricing reports'.	2 / 3
Completeness	Describes what the skill does but completely lacks a 'Use when...' clause or any explicit trigger guidance. Per rubric guidelines, missing explicit trigger guidance should cap completeness at 2, and this has no 'when' component at all.	1 / 3
Trigger Term Quality	Contains some relevant keywords ('mispriced markets', 'external models', 'data sources') but misses common user terms like 'arbitrage', 'odds', 'betting', 'pricing discrepancy', 'market inefficiency', or specific market types users might mention.	2 / 3
Distinctiveness Conflict Risk	'Mispriced markets' provides some specificity, but 'external models and data sources' is generic enough to potentially overlap with other data analysis or financial skills. Could conflict with general market analysis or data comparison skills.	2 / 3
	Total	7 / 12 Passed

Implementation

72%

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

This skill is well-structured and concise, effectively communicating the concept of edge detection in prediction markets. However, it functions more as a specification than an actionable skill - it describes what outputs should look like without providing the executable code or API integration details Claude would need to actually fetch and compare data from external sources.

Suggestions

Add executable code snippets showing how to fetch data from at least one source (e.g., CME FedWatch API call or web scraping pattern)

Include a complete Kelly criterion calculation function rather than just showing the formula in an example

Add validation steps for data freshness and source reliability before presenting edge calculations

Dimension	Reasoning	Score
Conciseness	The skill is lean and efficient, presenting commands, data sources, and examples without explaining concepts Claude already knows. Every section serves a purpose with no padding or unnecessary context.	3 / 3
Actionability	Commands are clearly shown and examples demonstrate expected behavior, but the actual implementation is missing - there's no executable code for fetching data from sources, comparing prices, or calculating Kelly criterion programmatically.	2 / 3
Workflow Clarity	The examples show input/output patterns but lack explicit multi-step workflows. For edge detection involving multiple data sources and calculations, there should be clearer sequencing and validation steps (e.g., verify data freshness, handle API failures).	2 / 3
Progressive Disclosure	For a skill of this size (~80 lines), the content is well-organized into logical sections (Commands, Data Sources, Examples, Output Format) with clear headers. No external references needed for this scope.	3 / 3
	Total	10 / 12 Passed

Validation

90%

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 — 10 / 11 Passed

Validation for skill structure

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

	Total	10 / 11 Passed

Repository: alsk1992/CloddsBot
Commit: 2a8c94e

Reviewed: 28 days ago

Table of Contents

Discovery Implementation Validation

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