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chief-customer-officer-advisor

Chief Customer Officer advisory for startups: retention decomposition (gross retention vs NRR honesty, churn root-cause taxonomy), customer segmentation strategy (differential investment across tiers + ICP fit scoring), CS team coverage model (pooled vs named CSM thresholds + ratio math), and CS team org evolution (CS vs Support vs AM distinctions). Use when designing retention strategy, segmenting customers for differential investment, sizing CS team, or sequencing CS hires. Strategic only — does not duplicate engineering/business-growth tactical skills.

72

Quality

91%

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SecuritybySnyk

Passed

No findings from the security scan

The canonical home for this skill is chief-customer-officer-advisor in alirezarezvani/claude-skills

SKILL.md
Quality
Evals
Security

Quality

Content

86%Weight 40%Scale 1-5

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

A well-structured strategic skill body: executable commands with embedded samples, benchmark tables that carry genuine non-generic expertise, and clean one-level-deep reference routing verified against the actual bundle. The trimmable padding (Keywords list, Version/Status footer) and the absence of explicit validation checkpoints in the workflows keep it just short of top marks.

Suggestions

Drop or compress the Keywords section (~40 terms) and the Version/Status footer — they add tokens without adding guidance Claude can act on; trigger keywords already live in the description.

Add an explicit checkpoint step in each workflow (e.g., 'Verify cohort data covers 8 full quarters before running the analyzer; if gaps, backfill from closed/won records') so sequences carry validate-then-proceed gates rather than only cross-checks.

DimensionReasoningScore

Conciseness

The body is dense with domain-specific data Claude cannot reliably reproduce (GRR/NRR health thresholds, ARR-per-CSM ratio tables, the six-role owns/does-not-own matrix) with almost no generic explanation, but the ~40-item Keywords dump, the 'Key Questions' section, and the Version/Status footer are trimmable padding — 'minor instances of over-explanation that could be trimmed' fits 4 better than the every-token-earns-its-place bar of 5.

4 / 5

Actionability

Guidance is fully executable: copy-paste-ready commands with a no-arg embedded-sample mode for each tool ('python scripts/retention_decomposition_analyzer.py # embedded B2B SaaS sample'), specific numeric thresholds in every table, and a concrete Output Standards template — matching the 5 anchor's 'copy-paste ready code or commands; specific examples cover the common cases'.

5 / 5

Workflow Clarity

All four workflows have goals, time-boxes, numbered steps, and defined outputs ('Output: top-3 leakage points + 90-day mitigation plan'), with cross-check steps acting as checkpoints — but there are no explicit validate-then-proceed gates or error-recovery loops, so it sits at 4 ('most checkpoints present; minor validation gaps') rather than 5. These are analytical workflows, not destructive/batch operations, so the workflow-clarity cap does not apply.

4 / 5

Progressive Disclosure

The body keeps only decision-level overview tables and defers depth to four real one-level-deep reference files, each verified to exist and signaled both inline ('See references/retention_decomposition.md for the 7-category churn taxonomy') and in a dedicated References section; the three scripts are real and wired into Quick Start — matching the 5 anchor's clear overview with well-signaled references and easy navigation.

5 / 5

Total

18

/

20

Passed

Description

92%Weight 40%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.

A strong description that names concrete capabilities across four decision areas, gives an explicit 'Use when' clause with natural trigger phrases, and proactively fences off adjacent skills. The only weakness is keyword coverage: it leans on the 'CS' abbreviation where users would say 'customer success'.

DimensionReasoningScore

Specificity

The description lists multiple concrete actions across four pillars — 'retention decomposition (gross retention vs NRR honesty, churn root-cause taxonomy)', 'differential investment across tiers + ICP fit scoring', 'pooled vs named CSM thresholds + ratio math', 'CS vs Support vs AM distinctions' — comprehensive coverage of the domain, matching the 5 anchor rather than 4 (no meaningful coverage gaps).

5 / 5

Completeness

It explicitly answers both what (four advisory areas each with concrete sub-capabilities) and when ('Use when designing retention strategy, segmenting customers for differential investment, sizing CS team, or sequencing CS hires'), matching the 5 anchor with concrete trigger phrases rather than the weaker 'when' of the 4 anchor.

5 / 5

Trigger Term Quality

Good natural phrases are present ('Use when designing retention strategy', 'sizing CS team', 'sequencing CS hires', 'gross retention', 'NRR'), but 'customer success' appears only as the abbreviation 'CS' and common variants like 'churn analysis' or 'NRR vs GRR' are missing — good coverage with a few natural terms absent, so 4 rather than 5.

4 / 5

Distinctiveness Conflict Risk

A clear CCO niche with an explicit boundary clause ('Strategic only — does not duplicate engineering/business-growth tactical skills') that actively disambiguates from adjacent tactical and C-level skills, matching the 5 anchor's minimal conflict risk rather than the 'minor overlap risk' of 4.

5 / 5

Total

19

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

alirezarezvani/claude-skills

Path

c-level-advisor/skills/chief-customer-officer-advisor/SKILL.md

Reviewed

Table of Contents

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