backtesting-frameworks

Build robust backtesting systems for trading strategies with proper handling of look-ahead bias, survivorship bias, and transaction costs. Use when developing trading algorithms, validating strategies, or building backtesting infrastructure.

Quality

78%

Does it follow best practices?

Impact

Pending

No eval scenarios have been run

Securityby

Passed

No known issues

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npx tessl skill review --optimize ./.agent/skills/backtesting-frameworks/SKILL.md

Quality

Discovery

100%

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

This is a strong skill description that follows best practices. It uses third person voice, lists specific technical capabilities relevant to the domain, includes an explicit 'Use when' clause with natural trigger terms, and occupies a clear niche in quantitative finance/algorithmic trading that distinguishes it from general programming or data analysis skills.

Dimension	Reasoning	Score
Specificity	Lists multiple specific concrete actions: 'Build robust backtesting systems', 'proper handling of look-ahead bias, survivorship bias, and transaction costs'. These are concrete, domain-specific capabilities.	3 / 3
Completeness	Clearly answers both what (build backtesting systems with bias handling and transaction costs) AND when (explicit 'Use when' clause covering developing algorithms, validating strategies, building infrastructure).	3 / 3
Trigger Term Quality	Includes natural keywords users would say: 'backtesting', 'trading strategies', 'trading algorithms', 'validating strategies', 'backtesting infrastructure'. These are terms a quant or algo trader would naturally use.	3 / 3
Distinctiveness Conflict Risk	Highly specific niche targeting quantitative trading/backtesting. Terms like 'look-ahead bias', 'survivorship bias', 'transaction costs', and 'backtesting' are domain-specific and unlikely to conflict with general coding or data analysis skills.	3 / 3
	Total	12 / 12 Passed

Implementation

57%

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

The skill is well-structured and concise, with appropriate progressive disclosure to detailed resources. However, it severely lacks actionability—the instructions read as a high-level checklist rather than executable guidance. Without concrete code examples, specific commands, or detailed implementation patterns in the main skill file, Claude would struggle to actually implement a backtesting system from this content alone.

Suggestions

Add at least one concrete, executable code example showing a minimal backtesting loop with proper train/test split

Include specific validation checkpoints in the workflow (e.g., 'Verify no future data leakage by checking all features have timestamps <= current bar')

Provide concrete examples of cost models and slippage calculations rather than just mentioning 'realistic cost models'

Add a minimal working example that demonstrates avoiding look-ahead bias in practice

Dimension	Reasoning	Score
Conciseness	The content is lean and efficient, avoiding unnecessary explanations of concepts Claude already knows. Each section serves a clear purpose without padding or verbose descriptions of what backtesting is.	3 / 3
Actionability	The instructions are vague and abstract with no concrete code, commands, or executable examples. Phrases like 'Build point-in-time data pipelines' and 'Implement event-driven simulation' describe rather than instruct, providing no copy-paste ready guidance.	1 / 3
Workflow Clarity	Steps are listed in a logical sequence but lack validation checkpoints or feedback loops. For a complex domain like backtesting where errors compound, there's no guidance on verifying each step before proceeding or handling failures.	2 / 3
Progressive Disclosure	Clear overview structure with well-signaled one-level-deep references to implementation-playbook.md for detailed patterns. The skill appropriately keeps the main file concise while pointing to resources for depth.	3 / 3
	Total	9 / 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: Dokhacgiakhoa/antigravity-ide
Commit: 3395991

Reviewed: 24 days ago

Table of Contents

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