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

73%

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

89%

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 well-structured description with explicit 'Use when' guidance and domain-specific terminology that creates clear distinctiveness. The main weakness is that it describes the domain and concerns rather than listing specific concrete actions the skill can perform. Adding action verbs like 'simulate', 'analyze', 'generate reports' would strengthen specificity.

Suggestions

Add specific concrete actions like 'simulate historical trades', 'calculate performance metrics', 'generate equity curves', or 'analyze drawdowns' to improve specificity

Dimension	Reasoning	Score
Specificity	Names the domain (backtesting systems for trading strategies) and mentions specific concerns (look-ahead bias, survivorship bias, transaction costs), but doesn't list concrete actions like 'simulate trades', 'generate performance reports', or 'calculate Sharpe ratios'.	2 / 3
Completeness	Clearly answers both what (build backtesting systems with proper handling of biases and costs) and when (explicit 'Use when' clause covering developing algorithms, validating strategies, or building infrastructure).	3 / 3
Trigger Term Quality	Good coverage of natural terms users would say: 'backtesting', 'trading strategies', 'trading algorithms', 'backtesting infrastructure'. These are terms a user would naturally use when seeking this functionality.	3 / 3
Distinctiveness Conflict Risk	Clear niche with distinct triggers around backtesting, trading algorithms, and specific financial concerns like look-ahead bias and survivorship bias. Unlikely to conflict with general coding or data analysis skills.	3 / 3
	Total	11 / 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, appropriately deferring detailed content to sub-resources. However, it critically lacks actionable, executable guidance - the instructions read as a high-level checklist rather than concrete steps Claude can follow. For a complex domain like backtesting, the absence of code examples, specific commands, or validation checkpoints significantly limits its utility.

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, such as 'Verify no future data leakage by checking all features use only t-1 data'

Replace abstract instructions like 'Build point-in-time data pipelines' with specific patterns or code snippets showing how to implement them

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 prone to errors (look-ahead bias, survivorship bias), there are no explicit verification steps or error recovery guidance.	2 / 3
Progressive Disclosure	Clear structure with a concise overview and well-signaled one-level-deep references to implementation-playbook.md for detailed patterns. The skill appropriately defers detailed examples to a separate resource file.	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: 332e58b

Reviewed: 17 days ago

Table of Contents

Discovery Implementation Validation

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