analytics

Performance attribution, trade analytics, and strategy optimization

Quality

45%

Does it follow best practices?

Impact

Pending

No eval scenarios have been run

Securityby

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./src/skills/bundled/analytics/SKILL.md

Quality

Discovery

32%

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 is a brief, high-level label that names a financial domain but lacks concrete actions, natural trigger terms, and any explicit guidance on when to use the skill. It reads more like a category heading than a functional skill description, making it difficult for Claude to reliably select this skill from a large pool.

Suggestions

Add a 'Use when...' clause with explicit triggers, e.g., 'Use when the user asks about portfolio returns, trade P&L analysis, risk-adjusted performance, or strategy backtesting.'

List specific concrete actions instead of category names, e.g., 'Decomposes portfolio returns by factor/sector, calculates risk-adjusted metrics (Sharpe, Sortino), analyzes trade execution quality, and optimizes strategy parameters.'

Include natural user-facing keywords and file types, e.g., 'portfolio performance, trading signals, backtest results, returns attribution, .csv trade logs'.

Dimension	Reasoning	Score
Specificity	Names the domain (trading/finance) and lists some actions ('performance attribution', 'trade analytics', 'strategy optimization'), but these are high-level category names rather than concrete specific actions like 'calculate Sharpe ratios' or 'decompose returns by sector'.	2 / 3
Completeness	Only addresses 'what' at a high level and completely lacks any 'when should Claude use it' guidance. There is no 'Use when...' clause or equivalent explicit trigger guidance, which per the rubric should cap completeness at 2, but since the 'what' is also weak, a score of 1 is appropriate.	1 / 3
Trigger Term Quality	Includes some relevant keywords like 'performance attribution', 'trade analytics', and 'strategy optimization' that a finance user might use, but misses common variations like 'portfolio analysis', 'P&L', 'returns', 'backtesting', 'trading performance', or 'alpha'.	2 / 3
Distinctiveness Conflict Risk	The finance/trading domain provides some distinctiveness, but terms like 'performance' and 'analytics' and 'optimization' are generic enough to potentially overlap with general data analytics, business intelligence, or other optimization-focused skills.	2 / 3
	Total	7 / 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 provides comprehensive, actionable API reference content with executable TypeScript examples and clear chat commands. However, it suffers from being a monolithic document that should be split into overview + detailed references, and the repetitive console.log patterns across many similar API methods add bulk without proportional value. The lack of workflow guidance for when/how to combine these analytics tools is a missed opportunity.

Suggestions

Split into SKILL.md (overview + chat commands + quick start) with references to REFERENCE.md (full API) and METRICS.md (tables/best practices) for better progressive disclosure.

Reduce the TypeScript examples to 2-3 representative methods with full code, then list remaining methods with just their signatures and brief descriptions.

Add a brief workflow section showing how to combine analytics steps (e.g., 'Start with /analytics for overview → drill into attribution → check edge decay for underperformers').

Dimension	Reasoning	Score
Conciseness	The content is fairly well-organized but includes verbose console.log patterns that repeat a similar structure across many sections. The extensive API reference with every method's full output logging is somewhat redundant — Claude could infer output patterns from one or two examples. The chat commands section is lean, but the TypeScript section could be significantly tightened.	2 / 3
Actionability	The skill provides fully executable TypeScript code with concrete imports, method calls, and parameter examples. Chat commands are specific and copy-paste ready. The API examples include realistic field names and usage patterns that Claude can directly apply.	3 / 3
Workflow Clarity	This is primarily a reference/API skill rather than a multi-step workflow, so explicit sequencing is less critical. However, there's no guidance on when to use which analysis, no error handling, and no validation steps for report generation or data export operations. The 'Best Practices' section hints at a workflow but doesn't sequence it.	2 / 3
Progressive Disclosure	This is a monolithic wall of API reference content that would benefit greatly from being split into separate files (e.g., chat commands overview in SKILL.md, detailed API reference in a separate REFERENCE.md, metrics/best practices in another file). Everything is inlined with no references to external files.	1 / 3
	Total	8 / 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: alsk1992/CloddsBot
Commit: e71a5f6

Reviewed: 11 days ago

Table of Contents

Discovery Implementation Validation

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