Agent skill

Customer Success Manager

Monitors customer health, predicts churn risk, and identifies expansion opportunities using weighted scoring models for SaaS customer success

MITAuto-check passedSales & Support

Install Customer Success Manager

skills CLI

$ npx skills add aAAaqwq/AGI-Super-Team --skill customer-success-manager -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install aAAaqwq/AGI-Super-Team customer-success-manager --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/aAAaqwq/AGI-Super-Team.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/customer-success-manager .claude/skills/customer-success-manager && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "customer-success-manager" agent skill from https://github.com/aAAaqwq/AGI-Super-Team/tree/main/skills/customer-success-manager into .claude/skills/customer-success-manager/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "customer-success-manager", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/aAAaqwq/AGI-Super-Team/tree/main/skills/customer-success-manager

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add aAAaqwq/AGI-Super-Team --skill customer-success-manager -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install aAAaqwq/AGI-Super-Team customer-success-manager --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/aAAaqwq/AGI-Super-Team.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/customer-success-manager .agents/skills/customer-success-manager && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "customer-success-manager" agent skill from https://github.com/aAAaqwq/AGI-Super-Team/tree/main/skills/customer-success-manager into .agents/skills/customer-success-manager/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "customer-success-manager", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add aAAaqwq/AGI-Super-Team --skill customer-success-manager -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install aAAaqwq/AGI-Super-Team customer-success-manager --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/aAAaqwq/AGI-Super-Team.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/customer-success-manager .cursor/skills/customer-success-manager && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "customer-success-manager" agent skill from https://github.com/aAAaqwq/AGI-Super-Team/tree/main/skills/customer-success-manager into .cursor/skills/customer-success-manager/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "customer-success-manager", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/aAAaqwq/AGI-Super-Team.git --path skills/customer-success-manager

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add aAAaqwq/AGI-Super-Team --skill customer-success-manager -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install aAAaqwq/AGI-Super-Team customer-success-manager --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/aAAaqwq/AGI-Super-Team.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/customer-success-manager .gemini/skills/customer-success-manager && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "customer-success-manager" agent skill from https://github.com/aAAaqwq/AGI-Super-Team/tree/main/skills/customer-success-manager into .gemini/skills/customer-success-manager/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "customer-success-manager", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install aAAaqwq/AGI-Super-Team customer-success-manager

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add aAAaqwq/AGI-Super-Team --skill customer-success-manager -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/aAAaqwq/AGI-Super-Team.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/customer-success-manager .github/skills/customer-success-manager && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "customer-success-manager" agent skill from https://github.com/aAAaqwq/AGI-Super-Team/tree/main/skills/customer-success-manager into .github/skills/customer-success-manager/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "customer-success-manager", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add aAAaqwq/AGI-Super-Team --skill customer-success-manager -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install aAAaqwq/AGI-Super-Team customer-success-manager --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/aAAaqwq/AGI-Super-Team.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/customer-success-manager .opencode/skills/customer-success-manager && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "customer-success-manager" agent skill from https://github.com/aAAaqwq/AGI-Super-Team/tree/main/skills/customer-success-manager into .opencode/skills/customer-success-manager/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "customer-success-manager", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: customer-success-manager
GitHub stars: 105
Used in: 2 other repos
Token cost: ~2.7k tokens
SKILL.md length: 631 words
Files: 13 (incl. scripts, references, assets)
Skills in repo: 167
Repo updated: Oct 8, 2026
First seen: Oct 9, 2026
Licence: MIT

At a glance

Monitors customer health, predicts churn risk, and identifies expansion opportunities using weighted scoring models for SaaS customer success

Works in 3 steps: health_score_calculator.py → churn_risk_analyzer.py → expansion_opportunity_scorer.py
Tasks that involve Customer success
SKILL.md covers Table of Contents, Capabilities, Input Requirements and Output Formats, plus 6 more sections
Runs Python scripts from its folder; calls python

What it does

Customer Success Manager is an agent skill from aAAaqwq/AGI-Super-Team. Monitors customer health, predicts churn risk, and identifies expansion opportunities using weighted scoring models for SaaS customer success

Its SKILL.md is about 2.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 15 other files, including scripts, reference files and assets (for example `assets/executive_business_review_template.md`, `assets/expected_output.json` and `assets/onboarding_checklist_template.md`).

It sits in Sales & Support, covering Customer success. The repository describes itself as: An installable, cross-framework AI organization: C-suite agents, expert subagents, curated skills, independent review, and one-command setup across 18 AI client/runtime adapters. The licence is MIT.

When your agent uses it

Tasks that involve Customer success

Example prompts

“Use the customer-success-manager skill to monitor customer health, predicts churn risk, and identifies expansion opportunities using weighted…”
“/customer-success-manager”

Requirements

Python 3

Workflow steps

3 steps, taken from the step headings in SKILL.md.

health_score_calculator.py
churn_risk_analyzer.py
expansion_opportunity_scorer.py

What it can do on your machine

Read from SKILL.md and the folder at commit 7cefd81. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 3 files in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
- python
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Customer Success Manager loads about 2.7k tokens when it runs, and up to ~9.8k if it reads all its reference files. Until then it costs about 42 tokens; SKILL.md has 631 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~42
When it runs · the whole SKILL.md, loaded when a task matches: ~2.7k
With references · SKILL.md plus every file in references/, read only if the agent opens them: ~9.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from aAAaqwq/AGI-Super-Team at commit 7cefd81, republished under its MIT licence (© aAAaqwq). 631 words, ~2,684 tokens.

Download SKILL.mdSave it as .claude/skills/customer-success-manager/SKILL.md (or your agent's skills folder). This skill also uses 12 other files; get the full folder from GitHub.

name: customer-success-manager
description: Monitors customer health, predicts churn risk, and identifies expansion opportunities using weighted scoring models for SaaS customer success
license: MIT
metadata.version: 1.0.0
metadata.author: Alireza Rezvani
metadata.category: business-growth
metadata.domain: customer-success
metadata.updated: 2026-02-06
metadata.python-tools: health_score_calculator.py, churn_risk_analyzer.py, expansion_opportunity_scorer.py
metadata.tech-stack: customer-success, saas-metrics, health-scoring

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.

Capabilities
Input Requirements
Output Formats
How to Use
Scripts
Reference Guides
Templates
Best Practices
Limitations

Capabilities

Customer Health Scoring: Multi-dimensional weighted scoring across usage, engagement, support, and relationship dimensions with Red/Yellow/Green classification
Churn Risk Analysis: Behavioral signal detection with tier-based intervention playbooks and time-to-renewal urgency multipliers
Expansion Opportunity Scoring: Adoption depth analysis, whitespace mapping, and revenue opportunity estimation with effort-vs-impact prioritization
Segment-Aware Benchmarking: Configurable thresholds for Enterprise, Mid-Market, and SMB customer segments
Trend Analysis: Period-over-period comparison to detect improving or declining trajectories
Executive Reporting: QBR templates, success plans, and executive business review templates

Input Requirements

All scripts accept a JSON file as positional input argument. See assets/sample_customer_data.json for complete examples.

Health Score Calculator

json

{
  "customers": [
    {
      "customer_id": "CUST-001",
      "name": "Acme Corp",
      "segment": "enterprise",
      "arr": 120000,
      "usage": {
        "login_frequency": 85,
        "feature_adoption": 72,
        "dau_mau_ratio": 0.45
      },
      "engagement": {
        "support_ticket_volume": 3,
        "meeting_attendance": 90,
        "nps_score": 8,
        "csat_score": 4.2
      },
      "support": {
        "open_tickets": 2,
        "escalation_rate": 0.05,
        "avg_resolution_hours": 18
      },
      "relationship": {
        "executive_sponsor_engagement": 80,
        "multi_threading_depth": 4,
        "renewal_sentiment": "positive"
      },
      "previous_period": {
        "usage_score": 70,
        "engagement_score": 65,
        "support_score": 75,
        "relationship_score": 60
      }
    }
  ]
}

Churn Risk Analyzer

json

{
  "customers": [
    {
      "customer_id": "CUST-001",
      "name": "Acme Corp",
      "segment": "enterprise",
      "arr": 120000,
      "contract_end_date": "2026-06-30",
      "usage_decline": {
        "login_trend": -15,
        "feature_adoption_change": -10,
        "dau_mau_change": -0.08
      },
      "engagement_drop": {
        "meeting_cancellations": 2,
        "response_time_days": 5,
        "nps_change": -3
      },
      "support_issues": {
        "open_escalations": 1,
        "unresolved_critical": 0,
        "satisfaction_trend": "declining"
      },
      "relationship_signals": {
        "champion_left": false,
        "sponsor_change": false,
        "competitor_mentions": 1
      },
      "commercial_factors": {
        "contract_type": "annual",
        "pricing_complaints": false,
        "budget_cuts_mentioned": false
      }
    }
  ]
}

Expansion Opportunity Scorer

json

{
  "customers": [
    {
      "customer_id": "CUST-001",
      "name": "Acme Corp",
      "segment": "enterprise",
      "arr": 120000,
      "contract": {
        "licensed_seats": 100,
        "active_seats": 95,
        "plan_tier": "professional",
        "available_tiers": ["professional", "enterprise", "enterprise_plus"]
      },
      "product_usage": {
        "core_platform": {"adopted": true, "usage_pct": 85},
        "analytics_module": {"adopted": true, "usage_pct": 60},
        "integrations_module": {"adopted": false, "usage_pct": 0},
        "api_access": {"adopted": true, "usage_pct": 40},
        "advanced_reporting": {"adopted": false, "usage_pct": 0}
      },
      "departments": {
        "current": ["engineering", "product"],
        "potential": ["marketing", "sales", "support"]
      }
    }
  ]
}

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

How to Use

Quick Start

bash

# 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

bash

# 1. Score customer health across portfolio
python scripts/health_score_calculator.py customer_portfolio.json --format json > health_results.json

# 2. Identify at-risk accounts
python scripts/churn_risk_analyzer.py customer_portfolio.json --format json > risk_results.json

# 3. Find expansion opportunities in healthy accounts
python scripts/expansion_opportunity_scorer.py customer_portfolio.json --format json > expansion_results.json

# 4. Prepare QBR using templates
# Reference: assets/qbr_template.md

Scripts

1. health_score_calculator.py

Purpose: Multi-dimensional customer health scoring with trend analysis and segment-aware benchmarking.

Dimensions and Weights:

Dimension	Weight	Metrics
Usage	30%	Login frequency, feature adoption, DAU/MAU ratio
Engagement	25%	Support ticket volume, meeting attendance, NPS/CSAT
Support	20%	Open tickets, escalation rate, avg resolution time
Relationship	25%	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:

bash

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 Category	Weight	Indicators
Usage Decline	30%	Login trend, feature adoption change, DAU/MAU change
Engagement Drop	25%	Meeting cancellations, response time, NPS change
Support Issues	20%	Open escalations, unresolved critical, satisfaction trend
Relationship Signals	15%	Champion left, sponsor change, competitor mentions
Commercial Factors	10%	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:

bash

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:

bash

python scripts/expansion_opportunity_scorer.py customer_data.json
python scripts/expansion_opportunity_scorer.py customer_data.json --format json

Show full SKILL.md (234 more words)Show less

Reference Guides

Reference	Description
`references/health-scoring-framework.md`	Complete health scoring methodology, dimension definitions, weighting rationale, threshold calibration
`references/cs-playbooks.md`	Intervention playbooks for each risk tier, onboarding, renewal, expansion, and escalation procedures
`references/cs-metrics-benchmarks.md`	Industry benchmarks for NRR, GRR, churn rates, health scores, expansion rates by segment and industry

Templates

Template	Purpose
`assets/qbr_template.md`	Quarterly Business Review presentation structure
`assets/success_plan_template.md`	Customer success plan with goals, milestones, and metrics
`assets/onboarding_checklist_template.md`	90-day onboarding checklist with phase gates
`assets/executive_business_review_template.md`	Executive stakeholder review for strategic accounts

Best Practices

Score regularly: Run health scoring weekly for Enterprise, bi-weekly for Mid-Market, monthly for SMB
Act on trends, not snapshots: A declining Green is more urgent than a stable Yellow
Combine signals: Use all three scripts together for a complete customer picture
Calibrate thresholds: Adjust segment benchmarks based on your product and industry
Document interventions: Track what actions you took and outcomes for playbook refinement
Prepare with data: Run scripts before every QBR and executive meeting

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

Files

SKILL.md and 12 other files (scripts, references, assets) in skills/customer-success-manager of aAAaqwq/AGI-Super-Team.

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
assets/success_plan_template.md
references/cs-metrics-benchmarks.md
references/cs-playbooks.md
references/health-scoring-framework.md
scripts/churn_risk_analyzer.py
scripts/expansion_opportunity_scorer.py
scripts/health_score_calculator.py

Open the folder on GitHubat commit 7cefd81

Used in 2 other repositories

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in aAAaqwq/AGI-Super-Team, which our catalogue first saw on October 9, 2026.

Compare with similar skills

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Questions about Customer Success Manager

What does Customer Success Manager do?

Monitors customer health, predicts churn risk, and identifies expansion opportunities using weighted scoring models for SaaS customer success. Customer Success Manager is an agent skill from aAAaqwq/AGI-Super-Team.

When should I use Customer Success Manager?

Customer Success Manager fits situations like: tasks that involve Customer success.

How do I install Customer Success Manager in Claude Code?

Run `npx skills add aAAaqwq/AGI-Super-Team --skill customer-success-manager -a claude-code`. Or copy the skill folder (skills/customer-success-manager in aAAaqwq/AGI-Super-Team) into .claude/skills/customer-success-manager in your project. Claude Code loads it when a task matches its description.

How do I install Customer Success Manager in Codex?

Run `npx skills add aAAaqwq/AGI-Super-Team --skill customer-success-manager -a codex`. Or copy the skill folder (skills/customer-success-manager in aAAaqwq/AGI-Super-Team) into .agents/skills/customer-success-manager in your project. Codex loads it when a task matches its description.

Can I use Customer Success Manager in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add aAAaqwq/AGI-Super-Team --skill customer-success-manager -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/customer-success-manager, .gemini/skills/customer-success-manager, .github/skills/customer-success-manager and .opencode/skills/customer-success-manager in your project.

What does Customer Success Manager need to run?

Going by SKILL.md and its folder, Customer Success Manager needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Customer Success Manager access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Customer Success Manager safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Customer Success Manager use?

Customer Success Manager is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Customer Success Manager use?

About 2.7k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 7.1k tokens, read only when the agent opens those files.

What are the alternatives to Customer Success Manager?

Skills that share tags, products or a category with Customer Success Manager: Cs Health Scorecard (mohitagw15856/pm-claude-skills, 1.4k stars), Youtube Search (ZeroPointRepo/youtube-skills, 1k stars), Yt (ZeroPointRepo/youtube-skills, 1k stars) and Account Plan (Bevel-Software/Hexis, 100 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Customer Success Manager?

aAAaqwq (a GitHub user) maintains it in aAAaqwq/AGI-Super-Team, which has 105 GitHub stars. The repository holds 167 skills in this directory. The repository was last updated on October 8, 2026.

Source: aAAaqwq/AGI-Super-Team on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Customer Success Manager

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Customer Success Manager

Table of Contents

Capabilities

Input Requirements

Health Score Calculator

Churn Risk Analyzer

Expansion Opportunity Scorer

Output Formats

How to Use

Quick Start

Workflow Integration

Scripts

1. health_score_calculator.py

2. churn_risk_analyzer.py

3. expansion_opportunity_scorer.py

Reference Guides

Templates

Best Practices

Limitations

Files

Used in 2 other repositories

Compare with similar skills

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Related

Questions about Customer Success Manager