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.
80
Quality
78%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./docs/v19.7/configuration/agent/skills_external/antigravity-awesome-skills-main/skills/backtesting-frameworks/SKILL.mdQuality
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, appropriately delegating detailed implementation to a separate resource. However, the main content lacks actionable guidance—it reads as a high-level checklist rather than executable instructions. Without at least one concrete code example or specific command, Claude cannot act on this skill without immediately needing the referenced playbook.
Suggestions
Add at least one concrete, executable code example showing a minimal backtesting loop structure or data pipeline setup
Include specific validation checkpoints in the workflow (e.g., '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 at minimum a code skeleton
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is lean and efficient, avoiding explanations of concepts Claude already knows (like what backtesting is or how trading works). Every section serves a clear purpose without padding. | 3 / 3 |
Actionability | The instructions are vague and abstract ('Build point-in-time data pipelines', 'Implement event-driven simulation') without any concrete code, commands, or executable examples. It describes what to do rather than showing how. | 1 / 3 |
Workflow Clarity | Steps are listed in a logical sequence but lack validation checkpoints. For a complex domain like backtesting where errors compound, there's no explicit verification between steps or feedback loops for catching bias issues. | 2 / 3 |
Progressive Disclosure | Clear overview structure with appropriate delegation to a single external resource (implementation-playbook.md). The reference is one level deep and clearly signaled for when detailed examples are needed. | 3 / 3 |
Total | 9 / 12 Passed |
Validation
100%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 11 / 11 Passed
Validation for skill structure
No warnings or errors.
- Repository
- duclm1x1/Dive-Ai
- Commit
20ba150
- Reviewed
Table of Contents
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