churn-analysis

When the user needs to identify at-risk accounts, understand why customers are leaving, reduce churn rate, build health scores, design save plays, or create win-back campaigns.

Quality

68%

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Passed

No known issues

Fix and improve this skill with Tessl

tessl review fix ./skills/churn-analysis/SKILL.md

Quality

Content

64%

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

This is a thorough, highly actionable churn analysis skill with excellent frameworks (risk scoring model, churn driver taxonomy, cancel flow design) and strong examples showing exact output formats. Its main weaknesses are verbosity — several sections include educational context and industry benchmarks that pad the token count — and the lack of validation checkpoints in the workflow. The inline detail level is appropriate for a standalone reference but would benefit from splitting into sub-files for progressive disclosure.

Suggestions

Add explicit validation checkpoints to the workflow, e.g., 'Verify data completeness before scoring — flag accounts with insufficient signals for manual review' and 'Cross-check top-10 risk scores against CS team intuition before distributing the scorecard.'

Move detailed framework sections (Signal Extraction Categories, Cancel Flow Design, Dunning, Win-Back) into separate referenced files to reduce the main skill's token footprint and improve progressive disclosure.

Trim educational/statistical content that Claude doesn't need to execute the skill, such as industry benchmark percentages ('saves 10-20%', 'recovers 30-50%') and platform name-drops ('Gainsight or ChurnZero').

Dimension	Reasoning	Score
Conciseness	The skill is fairly comprehensive but includes some unnecessary verbosity. Phrases like 'Especially relevant for seed/Series A teams managing customers manually without dedicated CS platforms like Gainsight or ChurnZero' and general industry statistics (e.g., 'saves 10-20% of users') add context Claude doesn't need. The frameworks section is detailed but could be tighter — some explanations are more educational than instructional.	2 / 3
Actionability	The skill provides highly concrete, actionable guidance: specific scoring models with point values, risk tier thresholds with response timelines, a structured cancel flow with numbered steps, dunning retry schedules, and win-back timing windows. The examples show exact output formats with tables and save play briefs that Claude can directly replicate.	3 / 3
Workflow Clarity	The 6-step workflow is clearly sequenced and logical, but it lacks explicit validation checkpoints. There's no verification step to confirm data quality before scoring, no checkpoint to validate risk scores against known outcomes, and no feedback loop for iterating on the scoring model. For a process that drives customer interventions, validation gaps are notable.	2 / 3
Progressive Disclosure	The skill has good section structure with clear headers, but it's quite long (~200+ lines) with detailed frameworks (signal categories, scoring model, cancel flow design, dunning, win-back) all inline. The signal extraction categories, cancel flow design, and dunning sections could be split into referenced files. The 'Related Skills' section is well-done with clear cross-references.	2 / 3
	Total	9 / 12 Passed

Description

72%

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 has strong trigger terms and clear distinctiveness in the customer churn/retention domain, but it lacks an explicit 'what this skill does' statement. It reads entirely as a 'when to use' clause without first establishing the skill's core capabilities or outputs. Adding a concrete capability statement before the trigger conditions would significantly improve it.

Suggestions

Add a leading 'what' statement describing the skill's concrete outputs, e.g., 'Analyzes customer data to predict churn, generates retention strategies, and builds customer health scoring models.'

Restructure to separate capabilities from triggers: start with specific actions/outputs, then follow with the existing 'Use when...' clause.

Dimension	Reasoning	Score
Specificity	The description names the domain (customer churn/retention) and lists several actions like 'identify at-risk accounts', 'build health scores', 'design save plays', and 'create win-back campaigns'. However, these are more like task categories than concrete specific actions—it doesn't describe what the skill actually produces or how it operates.	2 / 3
Completeness	The description provides a 'when' clause ('When the user needs to...') but lacks a clear 'what does this do' statement. It only describes trigger conditions without explaining what the skill actually does or produces. The 'what' is only implied through the trigger terms.	2 / 3
Trigger Term Quality	Includes strong natural keywords users would say: 'at-risk accounts', 'customers are leaving', 'churn rate', 'health scores', 'save plays', 'win-back campaigns'. These cover a good range of terms a user working on customer retention would naturally use.	3 / 3
Distinctiveness Conflict Risk	The description carves out a clear niche around customer churn analysis and retention strategies. Terms like 'churn rate', 'health scores', 'save plays', and 'win-back campaigns' are highly specific to this domain and unlikely to conflict with other skills.	3 / 3
	Total	10 / 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: shawnpang/startup-founder-skills
Commit: 4ad31b4

Reviewed: 3 months ago

Table of Contents

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