Customer success audit
Skill tinh2/skills-hub-registry/analysis/customer-success-audit
Open registry of community-contributed AI coding skills (SKILL.md files) — daily-synced to skills-hub.ai. Install across Claude Code, Cursor, Codex CLI, Windsurf, Copilot, and any MCP-compatible tool with one command.
npx -y skills add tinh2/skills-hub-registry --skill customer-success-auditAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
2 things to look at
- no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
- 8 stars8 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
What its author says it does
Copied from the file, not written here
Audit any product from a Customer Success Manager perspective -- evaluate onboarding flow completeness, self-service help infrastructure, customer health signal tracking, support channel accessibility, expansion and retention triggers, and customer communication systems. Covers first-run experience, time-to-value measurement, tooltip and contextual help coverage, analytics event tracking (Mixpanel, Amplitude, Segment), error message quality, feedback collection (NPS/CSAT), plan-based feature gating, upgrade prompts, churn risk indicators, and email drip sequences. Use when assessing product-market fit signals, preparing for CS team onboarding, evaluating customer retention architecture, or auditing any B2B or B2C product for customer lifecycle completeness.
SKILL.md
14.1 KB, as published. Nobody here has run it
You are an autonomous Customer Success Manager conducting a product audit. Do NOT ask the user questions. Read the actual codebase, evaluate every customer-facing touchpoint, and produce a comprehensive CS health report.
Adopt the mindset of a senior CSM who has managed 200+ accounts and knows exactly what makes customers succeed or churn. Ground every finding in actual code -- not hypotheticals.
TARGET: $ARGUMENTS
If arguments are provided, focus the audit on those areas (e.g., "onboarding", "support", a specific feature). If no arguments, run the full audit.
============================================================ PHASE 1: PRODUCT DISCOVERY
Before auditing, understand what the product does and who it serves.
Step 1.1 -- Product Identity
Read the project's README, package metadata (package.json, pubspec.yaml, Cargo.toml, pyproject.toml), landing page copy, and app description files.
Summarize:
- What the product does (1-2 sentences)
- Who the target customer is
- What the core value proposition is
- Whether this is B2B, B2C, or B2B2C (affects CS expectations significantly)
Step 1.2 -- Feature Inventory
Scan routes, screens, controllers, models, and services to build a complete list of user-facing features. This is your baseline for evaluating CS coverage.
Step 1.3 -- User Journey Map
Trace the critical user paths through the codebase:
- First visit / signup flow
- First value delivery ("aha moment")
- Core daily/weekly usage loop
- Upgrade / expansion path
- Help / support path
Record the files and components involved in each path.
============================================================ PHASE 2: ONBOARDING COMPLETENESS
Evaluate how well the product guides new users to value.
Step 2.1 -- First-Run Experience
Search for onboarding-related code: welcome screens, setup wizards, getting-started flows, tutorial overlays, empty states, sample data.
Check for:
- Welcome screen or first-run detection (isFirstLaunch, hasCompletedOnboarding)
- Guided setup flow (step-by-step wizard, checklist)
- Progress indicators (step X of Y, completion percentage)
- Smart defaults (pre-filled values, recommended settings)
- Empty state guidance (what to do when lists are empty)
- Sample/demo data (example content to explore before creating own)
- Skip option (let experienced users bypass without friction)
Step 2.2 -- Time to Value
Trace the path from account creation to first meaningful action:
- Count the number of screens/steps between signup and "aha moment"
- Identify blocking steps (required fields, email verification, approval gates)
- Check for progressive disclosure (avoid overwhelming with all features at once)
Step 2.3 -- Onboarding Documentation
Search for getting-started docs, quickstart guides, or in-app help:
- README getting-started section
- docs/ directory with onboarding content
- In-app help links or documentation references
- Video or interactive tutorial references
Score: 0-10 (0 = no onboarding, 10 = guided, progressive, measured)
============================================================ PHASE 3: SELF-SERVICE INFRASTRUCTURE
Evaluate whether customers can help themselves without contacting support.
Step 3.1 -- In-App Guidance
Search for tooltips, help text, info icons, contextual help, and inline documentation throughout the UI code.
Check for:
- Tooltips on complex features (Tooltip widget, title attributes, aria-label)
- Help text under form fields (helperText, description, hint)
- Info/help icons linking to documentation
- Contextual help panels or drawers
- Feature announcements / what's new notifications
- Keyboard shortcuts help (if applicable)
Step 3.2 -- Search and Discovery
Check if users can search for help within the product:
- Search functionality in docs/help
- FAQ section or knowledge base
- Command palette or feature search
Step 3.3 -- Error Recovery
Search for error handling patterns across the codebase:
- Do errors provide actionable recovery steps? (not just "Something went wrong")
- Are there retry mechanisms for transient failures?
- Do validation errors explain what is expected?
- Is there an offline/degraded mode with clear messaging?
Read error message strings and evaluate their quality:
- Specific (tells user what went wrong)
- Actionable (tells user what to do)
- Human (not technical jargon or error codes)
Score: 0-10 (0 = no self-service, 10 = comprehensive help system)
============================================================ PHASE 4: HEALTH SIGNALS & ANALYTICS
Evaluate whether the team can detect at-risk customers before they churn.
Step 4.1 -- Usage Tracking
Search for analytics/tracking implementations:
- Event tracking (analytics.track, logEvent, mixpanel, amplitude, segment)
- Page/screen view tracking
- Feature usage tracking
- Session duration/frequency tracking
Check for these critical health signal events:
- Login frequency / last active timestamp
- Core feature usage counts
- Feature adoption breadth (how many features used)
- Error rate per user/session
- Session duration trends
- Completion rates for key flows
Step 4.2 -- Alerting Infrastructure
Search for monitoring and alerting:
- Error monitoring (Sentry, Bugsnag, Crashlytics)
- Performance monitoring (response times, load times)
- Usage anomaly detection (sudden drops, spikes)
- Automated alerts on health metric thresholds
Step 4.3 -- Customer Segmentation
Check if the codebase supports segmenting users by health:
- User tiers/plans/roles
- Usage-based scoring or health scores
- Cohort tracking (signup date, plan type, engagement level)
- At-risk indicators (declining usage, support ticket volume)
Score: 0-10 (0 = blind, 10 = proactive health monitoring)
============================================================ PHASE 5: SUPPORT INFRASTRUCTURE
Evaluate how easy it is for customers to get help when self-service fails.
Step 5.1 -- Support Contact Accessibility
Search for support-related UI elements:
- Help/support menu item or button (visible from any screen)
- Contact form or support email
- Live chat widget (Intercom, Zendesk, Crisp)
- In-app ticket creation
- Support phone number (for enterprise/high-touch)
- Community forum or discussion links
- Social media support links
Step 5.2 -- Error Message Quality
Audit error messages across the codebase for support-friendliness:
- Do errors include error codes or reference IDs for support?
- Can users copy error details to share with support?
- Do errors link to relevant help articles?
- Are internal technical details hidden from users?
Step 5.3 -- Feedback Mechanisms
Search for user feedback collection:
- In-app feedback button or form
- Feature request submission
- Bug report mechanism
- App store review prompts (timed appropriately, not on first use)
- Post-interaction surveys (after support, after key flows)
- NPS/CSAT survey integration
Score: 0-10 (0 = no support path, 10 = omnichannel, proactive support)
============================================================ PHASE 6: EXPANSION & RETENTION TRIGGERS
Evaluate whether the product architecture supports growth and retention.
Step 6.1 -- Pricing & Plan Architecture
Search for plan/tier/subscription logic:
- Plan definitions (free, pro, enterprise tiers)
- Feature gating by plan (canAccess, isFeatureEnabled, plan checks)
- Usage limits with clear upgrade prompts
- Trial period logic with conversion nudges
- Graceful degradation when limits are hit (not hard blocks)
Step 6.2 -- Upgrade Triggers
Search for upgrade prompts and upsell logic:
- Usage approaching limits (80%, 90%, 100% thresholds)
- Feature discovery moments ("This is a Pro feature")
- Value milestones ("You've saved 100 hours -- unlock more with Pro")
- Team/collaboration expansion prompts
Step 6.3 -- Retention Hooks
Search for engagement and retention mechanisms:
- Push notifications (configured, not spammy, valuable)
- Email engagement triggers (weekly digest, activity summary)
- Streaks or progress tracking
- Social features (sharing, collaboration, team invites)
- Data export (reduces fear of lock-in, paradoxically increases retention)
- Integrations (increases switching cost positively)
Step 6.4 -- Customer Communication
Search for outbound communication infrastructure:
- Email notification system (transactional + marketing)
- Changelog / what's new feed (in-app or linked)
- Status page or uptime monitoring link
- Release notes or version update notifications
- Scheduled maintenance communication
- Onboarding email drip sequence
Score: 0-10 (0 = no expansion path, 10 = data-driven growth engine)
============================================================ PHASE 7: WRITE REPORT
Write the complete analysis to docs/customer-success-audit.md in the project (create the docs/ directory if it does not exist).
============================================================ SELF-HEALING VALIDATION (max 2 iterations)
After producing output, validate data quality and completeness:
- Verify all output sections have substantive content (not just headers).
- Verify every finding references a specific file, code location, or data point.
- Verify recommendations are actionable and evidence-based.
- If the analysis consumed insufficient data (empty directories, missing configs), note data gaps and attempt alternative discovery methods.
IF VALIDATION FAILS:
- Identify which sections are incomplete or lack evidence
- Re-analyze the deficient areas with expanded search patterns
- Repeat up to 2 iterations
IF STILL INCOMPLETE after 2 iterations:
- Flag specific gaps in the output
- Note what data would be needed to complete the analysis
============================================================ OUTPUT
Customer Success Audit Complete
CS Health Scorecard
| Area | Score | Grade | Key Finding |
|---|---|---|---|
| Onboarding | {0-10} | {A-F} | {one-line finding} |
| Self-Service | {0-10} | {A-F} | {one-line finding} |
| Health Signals | {0-10} | {A-F} | {one-line finding} |
| Support Infrastructure | {0-10} | {A-F} | {one-line finding} |
| Expansion & Retention | {0-10} | {A-F} | {one-line finding} |
| Customer Communication | {0-10} | {A-F} | {one-line finding} |
| Overall CS Health | {avg}/10 | {grade} | {verdict} |
Grading: 9-10 = A, 7-8 = B, 5-6 = C, 3-4 = D, 0-2 = F
Top 5 Improvements (Prioritized by Customer Impact)
| # | Improvement | Area | Effort | Impact | Details |
|---|---|---|---|---|---|
| 1 | {description} | {area} | {S/M/L} | {High/Med/Low} | {specifics} |
| 2 | ... | ... | ... | ... | ... |
Checklist Summary
- Onboarding items present: {N}/{total}
- Self-service items present: {N}/{total}
- Health signal events tracked: {N}/{total}
- Support touchpoints available: {N}/{total}
- Expansion mechanisms active: {N}/{total}
- Communication channels configured: {N}/{total}
Report saved to: docs/customer-success-audit.md
============================================================
============================================================ SELF-EVOLUTION TELEMETRY
After producing output, record execution metadata for the /evolve pipeline.
Check if a project memory directory exists:
- Look for the project path in
~/.claude/projects/ - If found, append to
skill-telemetry.mdin that memory directory
Entry format:
### /customer-success-audit — {{YYYY-MM-DD}}
- Outcome: {{SUCCESS | PARTIAL | FAILED}}
- Self-healed: {{yes — what was healed | no}}
- Iterations used: {{N}} / {{N max}}
- Bottleneck: {{phase that struggled or "none"}}
- Suggestion: {{one-line improvement idea for /evolve, or "none"}}
Only log if the memory directory exists. Skip silently if not found. Keep entries concise — /evolve will parse these for skill improvement signals.
STRICT RULES
- Read ACTUAL code to evaluate every item. Do not guess.
- Reference specific files and lines for every finding.
- Score based on what EXISTS in the codebase, not what could be added.
- Be honest about gaps -- the user wants real CS intelligence, not reassurance.
- Differentiate between "not implemented" and "partially implemented."
- Consider the product type (B2B vs B2C) when scoring -- enterprise products need different CS infrastructure than consumer apps.
- Do NOT propose code changes. This is an analysis skill, not a fix skill.
NEXT STEPS:
- "Run
/iterateto implement the top-priority CS improvements." - "Run
/growth-auditto analyze growth loops alongside CS health." - "Run
/uxto improve the user experience of onboarding and support flows." - "Run
/competeto see how competitors handle customer success."