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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.

51

Quality

56%

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SecuritybySnyk

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tessl review fix ./.agent/skills/backtesting-frameworks/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

22%

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

This skill is essentially a table of contents with no substantive content. It lists high-level steps for building backtesting systems but provides zero executable code, no concrete examples, no specific patterns, and no validation checkpoints. All meaningful content is deferred to referenced files that aren't provided, making the SKILL.md itself nearly useless as standalone guidance.

Suggestions

Add at least one concrete, executable code example in the SKILL.md itself — e.g., a minimal event-driven backtest loop skeleton in Python showing the core pattern.

Include explicit validation checkpoints in the workflow, such as verifying no look-ahead bias by checking that signals only use data available at each timestamp, and checking that transaction cost assumptions are realistic before running full backtests.

Replace the abstract instruction bullets with specific, actionable steps — e.g., instead of 'Build point-in-time data pipelines', show a concrete pattern for ensuring point-in-time correctness with a code snippet.

Consolidate the reference paths — the skill references both `resources/implementation-playbook.md` and `./sub-skills/implementation-playbook.md` which appear to be different files or a naming inconsistency that should be clarified.

DimensionReasoningScore

Conciseness

The 'Use this skill when' and 'Do not use this skill when' sections add moderate value but are somewhat verbose for Claude. The instructions themselves are lean bullet points, but the overall structure includes some padding (e.g., the 'Safety' section states obvious things Claude already knows about not giving financial advice).

2 / 3

Actionability

The instructions are entirely abstract and descriptive — 'Define hypothesis, universe, timeframe', 'Build point-in-time data pipelines', 'Implement event-driven simulation' — with zero concrete code, commands, specific examples, or executable guidance. Everything is deferred to a referenced playbook that isn't provided.

1 / 3

Workflow Clarity

While the five bullet points in Instructions loosely imply a sequence, there are no explicit validation checkpoints, no feedback loops, and no concrete steps for a multi-step process that involves complex, error-prone operations like backtesting infrastructure. The workflow is essentially 'do these things' without any verification or error recovery.

1 / 3

Progressive Disclosure

The skill references `resources/implementation-playbook.md` and a sub-skill, which is a reasonable one-level-deep structure. However, no bundle files are provided, so we can't verify the references exist. The SKILL.md itself is too thin — it's essentially just a pointer to other files with almost no standalone value, and the sub-skills section at the bottom references a different path than the resources section, creating confusion.

2 / 3

Total

6

/

12

Passed

Description

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-crafted description that clearly identifies a specialized domain (trading strategy backtesting) with explicit trigger guidance. Its main weakness is that the 'what' portion could list more concrete actions (e.g., generating performance reports, running walk-forward analysis, simulating order execution) rather than the somewhat general 'build robust backtesting systems'. Overall it performs strongly across all dimensions.

Suggestions

Add more specific concrete actions to the 'what' portion, e.g., 'simulate order execution, generate performance metrics, run walk-forward optimization' to improve specificity.

DimensionReasoningScore

Specificity

Names the domain (backtesting trading strategies) and mentions specific concerns (look-ahead bias, survivorship bias, transaction costs), but doesn't list multiple concrete actions beyond 'build backtesting systems'. Actions like 'developing trading algorithms' and 'validating strategies' are somewhat vague.

2 / 3

Completeness

Clearly answers both 'what' (build robust backtesting systems with proper handling of biases and costs) and 'when' (explicit 'Use when' clause covering developing trading algorithms, validating strategies, or building backtesting infrastructure).

3 / 3

Trigger Term Quality

Includes strong natural keywords users would say: 'backtesting', 'trading strategies', 'trading algorithms', 'look-ahead bias', 'survivorship bias', 'transaction costs', 'backtesting infrastructure'. These cover both general and specific terms a user working in this domain would naturally use.

3 / 3

Distinctiveness Conflict Risk

Highly distinctive niche — backtesting trading strategies with specific financial/quantitative concerns. Unlikely to conflict with other skills due to the specialized domain terminology like 'look-ahead bias', 'survivorship bias', and 'backtesting infrastructure'.

3 / 3

Total

11

/

12

Passed

Validation

90%

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

Validation10 / 11 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

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

Warning

Total

10

/

11

Passed

Repository
Dokhacgiakhoa/antigravity-ide
Reviewed

Table of Contents

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