Run a full Section 508 + WCAG 2.2 Level AA accessibility audit on a federal / .gov / public-sector website or ICT product. Triggers: "Section 508", "508 audit", "WCAG", "VPAT", "ACR".
日本語の概要は準備中です。原文の説明を表示しています。
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.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
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.
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:
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:
Record the files and components involved in each path.
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:
Step 2.2 -- Time to Value
Trace the path from account creation to first meaningful action:
Step 2.3 -- Onboarding Documentation
Search for getting-started docs, quickstart guides, or in-app help:
Score: 0-10 (0 = no onboarding, 10 = guided, progressive, measured)
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:
Step 3.2 -- Search and Discovery
Check if users can search for help within the product:
Step 3.3 -- Error Recovery
Search for error handling patterns across the codebase:
Read error message strings and evaluate their quality:
Score: 0-10 (0 = no self-service, 10 = comprehensive help system)
Evaluate whether the team can detect at-risk customers before they churn.
Step 4.1 -- Usage Tracking
Search for analytics/tracking implementations:
Check for these critical health signal events:
Step 4.2 -- Alerting Infrastructure
Search for monitoring and alerting:
Step 4.3 -- Customer Segmentation
Check if the codebase supports segmenting users by health:
Score: 0-10 (0 = blind, 10 = proactive health monitoring)
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:
Step 5.2 -- Error Message Quality
Audit error messages across the codebase for support-friendliness:
Step 5.3 -- Feedback Mechanisms
Search for user feedback collection:
Score: 0-10 (0 = no support path, 10 = omnichannel, proactive support)
Evaluate whether the product architecture supports growth and retention.
Step 6.1 -- Pricing & Plan Architecture
Search for plan/tier/subscription logic:
Step 6.2 -- Upgrade Triggers
Search for upgrade prompts and upsell logic:
Step 6.3 -- Retention Hooks
Search for engagement and retention mechanisms:
Step 6.4 -- Customer Communication
Search for outbound communication infrastructure:
Score: 0-10 (0 = no expansion path, 10 = data-driven growth engine)
Write the complete analysis to docs/customer-success-audit.md in the project (create the docs/ directory if it does not exist).
After producing output, validate data quality and completeness:
IF VALIDATION FAILS:
IF STILL INCOMPLETE after 2 iterations:
| 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
| # | Improvement | Area | Effort | Impact | Details |
|---|---|---|---|---|---|
| 1 | {description} | {area} | {S/M/L} | {High/Med/Low} | {specifics} |
| 2 | ... | ... | ... | ... | ... |
docs/customer-success-audit.md============================================================
After producing output, record execution metadata for the /evolve pipeline.
Check if a project memory directory exists:
~/.claude/projects/skill-telemetry.md in that memory directoryEntry 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.
NEXT STEPS:
/iterate to implement the top-priority CS improvements."/growth-audit to analyze growth loops alongside CS health."/ux to improve the user experience of onboarding and support flows."/compete to see how competitors handle customer success."まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Run a full Section 508 + WCAG 2.2 Level AA accessibility audit on a federal / .gov / public-sector website or ICT product. Triggers: "Section 508", "508 audit", "WCAG", "VPAT", "ACR".
日本語の概要は準備中です。原文の説明を表示しています。
Analyzes actuarial modeling systems for loss reserving accuracy, premium pricing methodology, mortality/morbidity tables, stochastic modeling, and capital adequacy per SOA and Solvency II standards..
日本語の概要は準備中です。原文の説明を表示しています。
Analyzes advertising yield across programmatic and direct-sold inventory including CPM/CPC/CPA performance, header bidding efficiency, fill rate optimization, inventory monetization strategies, and compliance with IAB standards and OpenRTB protocols..
日本語の概要は準備中です。原文の説明を表示しています。
Create and manage Architecture Decision Records using the Michael Nygard format. Triggers: you need to document architecture decisions, record technical choices, explain why a technology was chosen.
日本語の概要は準備中です。原文の説明を表示しています。
Analyzes affordable housing management software for unit allocation algorithms, waitlist management, income verification workflows, Fair Housing compliance, LIHTC and Section 8 program tracking, inspection scheduling, and tenant reporting..
日本語の概要は準備中です。原文の説明を表示しています。
Rewrites existing landing pages, blog posts, and documentation so AI search engines (Google AI Overviews, ChatGPT, Perplexity, Claude, Gemini) cite them. Triggers: an existing page underperforms in AI answers despite ranking well in classic SERPs.
日本語の概要は準備中です。原文の説明を表示しています。