startup-metrics-framework

Comprehensive guide to tracking, calculating, and optimizing key performance metrics for different startup business models from seed through Series A.

Quality

32%

Does it follow best practices?

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Passed

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tessl review fix ./skills/startup-metrics-framework/SKILL.md

Quality

Content

28%Scale 1-5

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

This skill is essentially a hollow shell—it has a title and description suggesting comprehensive startup metrics guidance, but the body contains only generic, non-actionable boilerplate instructions with no domain-specific content. It delegates everything to a referenced resource file that doesn't exist in the bundle, making the skill nearly useless on its own.

Suggestions

Add concrete, domain-specific content: list the key metrics (MRR, CAC, LTV, churn, etc.) with formulas and calculation examples for different business models.

Include at least one executable example showing how to calculate a metric (e.g., a Python snippet computing MRR from subscription data or a table showing cohort retention).

Replace generic instructions ('Apply relevant best practices') with specific actionable steps like 'Calculate CAC by dividing total sales+marketing spend by new customers acquired in the period.'

Provide the referenced resources/implementation-playbook.md file or inline the essential content so the skill is functional standalone.

Dimension	Reasoning	Score
Conciseness	The content is relatively short but includes generic boilerplate ('Clarify goals, constraints, and required inputs', 'Apply relevant best practices') that adds no value. The 'Use this skill when' and 'Do not use this skill when' sections are tautological and waste tokens.	3 / 5
Actionability	The skill provides no concrete guidance whatsoever—no formulas for metrics, no specific KPIs, no code, no examples. Instructions like 'Apply relevant best practices and validate outcomes' are entirely vague and non-executable.	1 / 5
Workflow Clarity	There is a rough sequence implied (clarify goals → apply practices → verify), but steps are poorly defined with no specifics about what to validate or how. No checkpoints or concrete workflow for calculating or tracking metrics.	2 / 5
Progressive Disclosure	References a resource file (resources/implementation-playbook.md) for detailed content, which is appropriate structure. However, no bundle files are provided to verify the reference exists, and the SKILL.md itself contains almost no substantive overview content to orient the reader before pointing elsewhere.	3 / 5
	Total	9 / 20 Passed

Description

36%Scale 1-5

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 domain (startup performance metrics) but remains too high-level, relying on generic verbs and lacking concrete examples of what metrics or calculations it covers. It has no 'Use when...' clause, making it harder for Claude to know when to select this skill. The description would benefit significantly from listing specific metrics, mentioning concrete business model types, and adding explicit trigger guidance.

Suggestions

Add a 'Use when...' clause with trigger terms like 'startup metrics', 'KPIs', 'MRR', 'churn', 'burn rate', 'unit economics', 'fundraising readiness'.

List specific concrete capabilities such as 'Calculates MRR, CAC, LTV, churn rate, and burn rate for SaaS, marketplace, and e-commerce startups' instead of generic 'tracking, calculating, and optimizing'.

Include specific business model types (e.g., 'SaaS', 'marketplace', 'D2C') and file/output formats to improve distinctiveness and trigger term coverage.

Dimension	Reasoning	Score
Specificity	Names the domain (startup metrics/KPIs) but actions are generic - 'tracking, calculating, and optimizing' are vague verbs that don't describe concrete capabilities like specific metrics, formulas, or outputs.	2 / 5
Completeness	Has a vague 'what' (tracking/calculating/optimizing metrics) but no explicit 'when' clause or trigger guidance. The absence of a 'Use when...' clause caps this at 3 per the rubric, and the 'what' is itself quite vague, bringing it to 2.	2 / 5
Trigger Term Quality	Includes some relevant keywords like 'startup', 'metrics', 'seed', 'Series A', and 'business models', but misses natural user phrases like 'KPIs', 'MRR', 'churn rate', 'burn rate', 'unit economics', 'fundraising metrics', or specific model types like 'SaaS', 'marketplace'.	3 / 5
Distinctiveness Conflict Risk	Somewhat specific to startup metrics from seed to Series A, which narrows the domain, but could overlap with general business analytics skills, financial modeling skills, or broader startup advisory skills.	3 / 5
	Total	10 / 20 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: administrakt0r/AI-Agents-Safe-Coding-Skills
Path: skills/startup-metrics-framework/SKILL.md
Commit: 57c135b

Reviewed: 16 hours ago

Table of Contents

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