# Send Customer Success Manager to your agent Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually. ## Fast path - Download the package from Yavira. - Extract it into a folder your agent can access. - Paste one of the prompts below and point your agent at the extracted folder. ## Suggested prompts ### New install ```text I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete. ``` ### Upgrade existing ```text I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run. ``` ## Machine-readable fields ```json { "schemaVersion": "1.0", "item": { "slug": "customer-success-manager", "name": "Customer Success Manager", "source": "tencent", "type": "skill", "category": "AI 智能", "sourceUrl": "https://clawhub.ai/alirezarezvani/customer-success-manager", "canonicalUrl": "https://clawhub.ai/alirezarezvani/customer-success-manager", "targetPlatform": "OpenClaw" }, "install": { "downloadUrl": "/downloads/customer-success-manager", "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=customer-success-manager", "sourcePlatform": "tencent", "targetPlatform": "OpenClaw", "packageFormat": "ZIP package", "primaryDoc": "SKILL.md", "includedAssets": [ "SKILL.md", "assets/executive_business_review_template.md", "assets/expected_output.json", "assets/onboarding_checklist_template.md", "assets/qbr_template.md", "assets/sample_customer_data.json" ], "downloadMode": "redirect", "sourceHealth": { "source": "tencent", "status": "healthy", "reason": "direct_download_ok", "recommendedAction": "download", "checkedAt": "2026-04-30T16:55:25.780Z", "expiresAt": "2026-05-07T16:55:25.780Z", "httpStatus": 200, "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=network", "contentType": "application/zip", "probeMethod": "head", "details": { "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=network", "contentDisposition": "attachment; filename=\"network-1.0.0.zip\"", "redirectLocation": null, "bodySnippet": null }, "scope": "source", "summary": "Source download looks usable.", "detail": "Yavira can redirect you to the upstream package for this source.", "primaryActionLabel": "Download for OpenClaw", "primaryActionHref": "/downloads/customer-success-manager" }, "validation": { "installChecklist": [ "Use the Yavira download entry.", "Review SKILL.md after the package is downloaded.", "Confirm the extracted package contains the expected setup assets." ], "postInstallChecks": [ "Confirm the extracted package includes the expected docs or setup files.", "Validate the skill or prompts are available in your target agent workspace.", "Capture any manual follow-up steps the agent could not complete." ] } }, "links": { "detailUrl": "https://openagent3.xyz/skills/customer-success-manager", "downloadUrl": "https://openagent3.xyz/downloads/customer-success-manager", "agentUrl": "https://openagent3.xyz/skills/customer-success-manager/agent", "manifestUrl": "https://openagent3.xyz/skills/customer-success-manager/agent.json", "briefUrl": "https://openagent3.xyz/skills/customer-success-manager/agent.md" } } ``` ## Documentation ### Customer Success Manager Production-grade customer success analytics with multi-dimensional health scoring, churn risk prediction, and expansion opportunity identification. Three Python CLI tools provide deterministic, repeatable analysis using standard library only -- no external dependencies, no API calls, no ML models. ### Table of Contents Input Requirements Output Formats How to Use Scripts Reference Guides Templates Best Practices Limitations ### Input Requirements All scripts accept a JSON file as positional input argument. See assets/sample_customer_data.json for complete schema examples and sample data. ### Health Score Calculator Required fields per customer object: customer_id, name, segment, arr, and nested objects usage (login_frequency, feature_adoption, dau_mau_ratio), engagement (support_ticket_volume, meeting_attendance, nps_score, csat_score), support (open_tickets, escalation_rate, avg_resolution_hours), relationship (executive_sponsor_engagement, multi_threading_depth, renewal_sentiment), and previous_period scores for trend analysis. ### Churn Risk Analyzer Required fields per customer object: customer_id, name, segment, arr, contract_end_date, and nested objects usage_decline, engagement_drop, support_issues, relationship_signals, and commercial_factors. ### Expansion Opportunity Scorer Required fields per customer object: customer_id, name, segment, arr, and nested objects contract (licensed_seats, active_seats, plan_tier, available_tiers), product_usage (per-module adoption flags and usage percentages), and departments (current and potential). ### Output Formats All scripts support two output formats via the --format flag: text (default): Human-readable formatted output for terminal viewing json: Machine-readable JSON output for integrations and pipelines ### Quick Start # Health scoring python scripts/health_score_calculator.py assets/sample_customer_data.json python scripts/health_score_calculator.py assets/sample_customer_data.json --format json # Churn risk analysis python scripts/churn_risk_analyzer.py assets/sample_customer_data.json python scripts/churn_risk_analyzer.py assets/sample_customer_data.json --format json # Expansion opportunity scoring python scripts/expansion_opportunity_scorer.py assets/sample_customer_data.json python scripts/expansion_opportunity_scorer.py assets/sample_customer_data.json --format json ### Workflow Integration # 1. Score customer health across portfolio python scripts/health_score_calculator.py customer_portfolio.json --format json > health_results.json # Verify: confirm health_results.json contains the expected number of customer records before continuing # 2. Identify at-risk accounts python scripts/churn_risk_analyzer.py customer_portfolio.json --format json > risk_results.json # Verify: confirm risk_results.json is non-empty and risk tiers are present for each customer # 3. Find expansion opportunities in healthy accounts python scripts/expansion_opportunity_scorer.py customer_portfolio.json --format json > expansion_results.json # Verify: confirm expansion_results.json lists opportunities ranked by priority # 4. Prepare QBR using templates # Reference: assets/qbr_template.md Error handling: If a script exits with an error, check that: The input JSON matches the required schema for that script (see Input Requirements above) All required fields are present and correctly typed Python 3.7+ is being used (python --version) Output files from prior steps are non-empty before piping into subsequent steps ### 1. health_score_calculator.py Purpose: Multi-dimensional customer health scoring with trend analysis and segment-aware benchmarking. Dimensions and Weights: DimensionWeightMetricsUsage30%Login frequency, feature adoption, DAU/MAU ratioEngagement25%Support ticket volume, meeting attendance, NPS/CSATSupport20%Open tickets, escalation rate, avg resolution timeRelationship25%Executive sponsor engagement, multi-threading depth, renewal sentiment Classification: Green (75-100): Healthy -- customer achieving value Yellow (50-74): Needs attention -- monitor closely Red (0-49): At risk -- immediate intervention required Usage: python scripts/health_score_calculator.py customer_data.json python scripts/health_score_calculator.py customer_data.json --format json ### 2. churn_risk_analyzer.py Purpose: Identify at-risk accounts with behavioral signal detection and tier-based intervention recommendations. Risk Signal Weights: Signal CategoryWeightIndicatorsUsage Decline30%Login trend, feature adoption change, DAU/MAU changeEngagement Drop25%Meeting cancellations, response time, NPS changeSupport Issues20%Open escalations, unresolved critical, satisfaction trendRelationship Signals15%Champion left, sponsor change, competitor mentionsCommercial Factors10%Contract type, pricing complaints, budget cuts Risk Tiers: Critical (80-100): Immediate executive escalation High (60-79): Urgent CSM intervention Medium (40-59): Proactive outreach Low (0-39): Standard monitoring Usage: python scripts/churn_risk_analyzer.py customer_data.json python scripts/churn_risk_analyzer.py customer_data.json --format json ### 3. expansion_opportunity_scorer.py Purpose: Identify upsell, cross-sell, and expansion opportunities with revenue estimation and priority ranking. Expansion Types: Upsell: Upgrade to higher tier or more of existing product Cross-sell: Add new product modules Expansion: Additional seats or departments Usage: python scripts/expansion_opportunity_scorer.py customer_data.json python scripts/expansion_opportunity_scorer.py customer_data.json --format json ### Reference Guides ReferenceDescriptionreferences/health-scoring-framework.mdComplete health scoring methodology, dimension definitions, weighting rationale, threshold calibrationreferences/cs-playbooks.mdIntervention playbooks for each risk tier, onboarding, renewal, expansion, and escalation proceduresreferences/cs-metrics-benchmarks.mdIndustry benchmarks for NRR, GRR, churn rates, health scores, expansion rates by segment and industry ### Templates TemplatePurposeassets/qbr_template.mdQuarterly Business Review presentation structureassets/success_plan_template.mdCustomer success plan with goals, milestones, and metricsassets/onboarding_checklist_template.md90-day onboarding checklist with phase gatesassets/executive_business_review_template.mdExecutive stakeholder review for strategic accounts ### Best Practices Combine signals: Use all three scripts together for a complete customer picture Act on trends, not snapshots: A declining Green is more urgent than a stable Yellow Calibrate thresholds: Adjust segment benchmarks based on your product and industry per references/health-scoring-framework.md Prepare with data: Run scripts before every QBR and executive meeting; reference references/cs-playbooks.md for intervention guidance ### Limitations No real-time data: Scripts analyze point-in-time snapshots from JSON input files No CRM integration: Data must be exported manually from your CRM/CS platform Deterministic only: No predictive ML -- scoring is algorithmic based on weighted signals Threshold tuning: Default thresholds are industry-standard but may need calibration for your business Revenue estimates: Expansion revenue estimates are approximations based on usage patterns Last Updated: February 2026 Tools: 3 Python CLI tools Dependencies: Python 3.7+ standard library only ## Trust - Source: tencent - Verification: Indexed source record - Publisher: alirezarezvani - Version: 2.1.1 ## Source health - Status: healthy - Source download looks usable. - Yavira can redirect you to the upstream package for this source. - Health scope: source - Reason: direct_download_ok - Checked at: 2026-04-30T16:55:25.780Z - Expires at: 2026-05-07T16:55:25.780Z - Recommended action: Download for OpenClaw ## Links - [Detail page](https://openagent3.xyz/skills/customer-success-manager) - [Send to Agent page](https://openagent3.xyz/skills/customer-success-manager/agent) - [JSON manifest](https://openagent3.xyz/skills/customer-success-manager/agent.json) - [Markdown brief](https://openagent3.xyz/skills/customer-success-manager/agent.md) - [Download page](https://openagent3.xyz/downloads/customer-success-manager)