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.
Install with Tessl CLI
npx tessl i github:sickn33/antigravity-awesome-skills --skill backtesting-frameworksOverall
score
74%
Does it follow best practices?
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npx tessl skill review --optimize ./path/to/skillValidation for skill structure
Discovery
90%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 system characteristics (handling biases) rather than listing concrete actions the skill performs. Adding specific capabilities like 'simulate historical trades' or 'generate performance metrics' would strengthen it.
Suggestions
Add specific concrete actions like 'simulate historical trades', 'calculate performance metrics', 'generate equity curves' 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 focused specifically on trading strategy backtesting with distinct technical triggers (look-ahead bias, survivorship bias, transaction costs) that are 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 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. For a complex domain like backtesting, the absence of concrete 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 bias prevention (e.g., point-in-time data access pattern)
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 at minimum, pseudocode showing the key implementation detail
| 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 validation or feedback loops mentioned (e.g., how to verify bias-free data, validate cost models). | 2 / 3 |
Progressive Disclosure | Clear overview structure with appropriate delegation to a single external resource (implementation-playbook.md) for detailed patterns. References are one level deep and clearly signaled. | 3 / 3 |
Total | 9 / 12 Passed |
Validation
75%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 12 / 16 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
metadata_version | 'metadata' field is not a dictionary | Warning |
license_field | 'license' field is missing | Warning |
body_output_format | No obvious output/return/format terms detected; consider specifying expected outputs | Warning |
body_steps | No step-by-step structure detected (no ordered list); consider adding a simple workflow | Warning |
Total | 12 / 16 Passed | |
- Reviewed
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