comprehensive-analysis

Deep-dive 7-day analysis across all data sources for weekly reviews, board prep, and strategic planning

Quality

32%

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./.claude/skills/comprehensive-analysis/SKILL.md

Quality

Discovery

22%

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 relies heavily on business buzzwords without specifying concrete actions or outputs. It lacks an explicit 'Use when...' clause, making it difficult for Claude to reliably select this skill. The 7-day/weekly framing provides some niche but the overall vagueness significantly undermines its utility as a skill selector.

Suggestions

Specify concrete actions the skill performs, e.g., 'Aggregates metrics from Slack, email, CRM, and analytics platforms to produce a weekly summary report with key trends and recommendations.'

Add an explicit 'Use when...' clause, e.g., 'Use when the user asks for a weekly report, weekly review, board deck preparation, or a summary of the past week's activity.'

Replace vague phrases like 'all data sources' and 'deep-dive analysis' with specific data types, formats, or platforms to reduce conflict risk with other analytics skills.

Dimension	Reasoning	Score
Specificity	The description uses vague, buzzword-heavy language like 'deep-dive', '7-day analysis', and 'strategic planning' without specifying any concrete actions. It doesn't say what it actually does—no verbs like 'extract', 'generate', 'compile', or 'summarize' are present.	1 / 3
Completeness	The 'what' is extremely vague ('analysis across all data sources') and there is no explicit 'Use when...' clause or equivalent trigger guidance. The use cases listed (weekly reviews, board prep) hint at 'when' but are not framed as explicit selection triggers.	1 / 3
Trigger Term Quality	It includes some potentially useful trigger terms like 'weekly reviews', 'board prep', and 'strategic planning' that users might naturally say. However, 'all data sources' is vague, and it lacks specific terms about what kind of analysis or output is produced.	2 / 3
Distinctiveness Conflict Risk	The '7-day' timeframe and 'weekly reviews' / 'board prep' context provide some distinctiveness, but 'analysis across all data sources' and 'strategic planning' are broad enough to overlap with many analytics or reporting skills.	2 / 3
	Total	6 / 12 Passed

Implementation

42%

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

This skill is highly actionable with specific, executable commands and queries across multiple data sources, which is its primary strength. However, it is extremely verbose — inlining massive agent prompts, a large metadata template, and repeating patterns from other skills rather than referencing them. The lack of validation checkpoints in a complex multi-agent workflow and the monolithic structure with no supporting bundle files significantly weaken its overall quality.

Suggestions

Extract the 5 agent prompt templates into separate files (e.g., agents/github-deep-analyst.md) and reference them from SKILL.md to dramatically reduce length and improve progressive disclosure.

Move the metadata template to a separate file (e.g., templates/comprehensive-analysis-metadata.yaml) and link to it.

Add explicit validation checkpoints: verify each agent returned data successfully before synthesis, validate output documents are non-empty before saving, and include a data quality check step.

Remove redundant explanations (e.g., 'Use this when' vs 'Don't use this daily' sections, general purpose descriptions) and trim to essential instructions — Claude can infer when a comprehensive analysis is appropriate.

Dimension	Reasoning	Score
Conciseness	Extremely verbose at ~300+ lines. Extensively explains concepts Claude already knows (what weekly reviews are, when to use vs not use), repeats patterns from a referenced daily-brief skill, and includes massive agent prompt templates that are largely boilerplate. The metadata template alone is ~50 lines. Much of this could be condensed to 1/3 the size.	1 / 3
Actionability	Highly actionable with specific CLI commands (gh pr list, gh api), exact HogQL queries, concrete MCP tool names (mcp__claude_ai_Linear__list_issues), specific file output paths and naming conventions, and detailed agent prompts. Nearly everything is copy-paste ready with clear [CUSTOMIZE] placeholders.	3 / 3
Workflow Clarity	The three-phase structure (Collection → Synthesis → Output) is clear and well-sequenced. However, there are no explicit validation checkpoints — no step to verify agent data quality before synthesis, no check that outputs are well-formed before saving, and error handling is deferred to 'same as daily brief' without specifics. For a multi-agent batch operation producing 3 documents, this lack of validation caps the score.	2 / 3
Progressive Disclosure	Monolithic wall of text with no bundle files to support it. All agent prompts, analysis patterns, metadata templates, and output formats are inlined. References to 'daily-brief.md' for cross-reference patterns and Phase 3.7/Phase 4 patterns are vague and unverifiable. Content like the 5 full agent prompt blocks and the metadata template should be in separate referenced files.	1 / 3
	Total	7 / 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: huytieu/COG-second-brain
Commit: 034af4c

Reviewed: 18 days ago

Table of Contents

Discovery Implementation Validation

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