backtest
Test trading strategies on historical data with Monte Carlo simulation
70
59%
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 ./src/skills/bundled/backtest/SKILL.mdQuality
Discovery
40%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
The description identifies a clear, distinctive domain (trading strategy backtesting with Monte Carlo simulation) but lacks completeness due to missing trigger guidance. It would benefit from explicit 'Use when...' language and additional natural trigger terms users might employ when requesting this functionality.
Suggestions
Add a 'Use when...' clause with trigger terms like 'backtest', 'backtesting', 'strategy testing', 'simulate trades', or 'portfolio simulation'
Include common variations of terminology: 'backtest', 'paper trading', 'historical simulation', 'risk analysis', 'drawdown analysis'
Expand concrete actions: 'backtest portfolios, simulate market scenarios, calculate risk metrics, analyze drawdowns'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (trading strategies) and mentions two specific techniques (historical data testing, Monte Carlo simulation), but doesn't list comprehensive concrete actions like 'backtest portfolios, simulate drawdowns, calculate risk metrics'. | 2 / 3 |
Completeness | Describes what the skill does but completely lacks a 'Use when...' clause or any explicit trigger guidance for when Claude should select this skill. Per rubric guidelines, missing explicit trigger guidance caps completeness at 2, and this has no 'when' component at all. | 1 / 3 |
Trigger Term Quality | Includes relevant terms like 'trading strategies', 'historical data', and 'Monte Carlo simulation', but misses common variations users might say like 'backtest', 'backtesting', 'strategy testing', 'market simulation', or 'portfolio analysis'. | 2 / 3 |
Distinctiveness Conflict Risk | The combination of 'trading strategies', 'historical data', and 'Monte Carlo simulation' creates a clear, specific niche that is unlikely to conflict with other skills. This is a well-defined domain. | 3 / 3 |
Total | 8 / 12 Passed |
Implementation
79%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a well-written API reference with excellent actionability and conciseness. The code examples are complete and executable, and the metrics tables provide useful context. However, it lacks explicit workflow guidance for multi-step validation processes and could benefit from splitting into overview + detailed reference files for better progressive disclosure.
Suggestions
Add a 'Recommended Workflow' section showing the sequence: load data → validate data → run backtest → verify results → run walk-forward → run Monte Carlo, with explicit validation checkpoints
Split into SKILL.md (overview + quick start) and separate files for API_REFERENCE.md, STRATEGIES.md, and MONTE_CARLO.md
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is lean and efficient, presenting API references and examples without explaining basic concepts Claude already knows. Every section serves a purpose with no padding or unnecessary context. | 3 / 3 |
Actionability | Provides fully executable TypeScript code examples that are copy-paste ready, concrete chat commands, and specific parameter examples. The custom strategy example is complete and runnable. | 3 / 3 |
Workflow Clarity | While individual API calls are clear, there's no explicit workflow for running a complete backtest validation process. Missing validation checkpoints between steps (e.g., verify data loaded correctly before running backtest, validate results before Monte Carlo). | 2 / 3 |
Progressive Disclosure | Content is well-organized with clear sections and tables, but it's a monolithic document. Advanced topics like custom strategies, walk-forward analysis, and Monte Carlo could be split into separate reference files with links from a shorter overview. | 2 / 3 |
Total | 10 / 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.
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
- alsk1992/CloddsBot
- Commit
2a8c94e
- Reviewed
Table of Contents
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