Youtube Search
ZeroPointRepo/youtube-skills
A skill your agent uses when the user wants to find YouTube content on any topic: searching for videos or channels, finding creators who cover a subject, discovering tutorials, talks, or expert…
Identify at-risk customer accounts by analyzing usage patterns, engagement signals, and support history to generate churn risk scores and intervention recommendations.
Install Churn Analysis
$ npx skills add seb1n/awesome-ai-agent-skills --skill churn-analysis -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install seb1n/awesome-ai-agent-skills churn-analysis --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/seb1n/awesome-ai-agent-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/customer-success/churn-analysis .claude/skills/churn-analysis && rm -rf skills-srcUse ~/.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/
Install the "churn-analysis" agent skill from https://github.com/seb1n/awesome-ai-agent-skills/tree/main/customer-success/churn-analysis into .claude/skills/churn-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "churn-analysis", 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 install https://github.com/seb1n/awesome-ai-agent-skills/tree/main/customer-success/churn-analysisType 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.
$ npx skills add seb1n/awesome-ai-agent-skills --skill churn-analysis -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install seb1n/awesome-ai-agent-skills churn-analysis --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/seb1n/awesome-ai-agent-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/customer-success/churn-analysis .agents/skills/churn-analysis && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "churn-analysis" agent skill from https://github.com/seb1n/awesome-ai-agent-skills/tree/main/customer-success/churn-analysis into .agents/skills/churn-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "churn-analysis", 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.
$ npx skills add seb1n/awesome-ai-agent-skills --skill churn-analysis -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install seb1n/awesome-ai-agent-skills churn-analysis --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/seb1n/awesome-ai-agent-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/customer-success/churn-analysis .cursor/skills/churn-analysis && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "churn-analysis" agent skill from https://github.com/seb1n/awesome-ai-agent-skills/tree/main/customer-success/churn-analysis into .cursor/skills/churn-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "churn-analysis", 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 https://github.com/seb1n/awesome-ai-agent-skills.git --path customer-success/churn-analysis--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add seb1n/awesome-ai-agent-skills --skill churn-analysis -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install seb1n/awesome-ai-agent-skills churn-analysis --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/seb1n/awesome-ai-agent-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/customer-success/churn-analysis .gemini/skills/churn-analysis && rm -rf skills-srcUse ~/.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/
Install the "churn-analysis" agent skill from https://github.com/seb1n/awesome-ai-agent-skills/tree/main/customer-success/churn-analysis into .gemini/skills/churn-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "churn-analysis", 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.
$ gh skill install seb1n/awesome-ai-agent-skills churn-analysisInstalls 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).
$ npx skills add seb1n/awesome-ai-agent-skills --skill churn-analysis -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/seb1n/awesome-ai-agent-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/customer-success/churn-analysis .github/skills/churn-analysis && rm -rf skills-srcUse ~/.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/
Install the "churn-analysis" agent skill from https://github.com/seb1n/awesome-ai-agent-skills/tree/main/customer-success/churn-analysis into .github/skills/churn-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "churn-analysis", 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.
$ npx skills add seb1n/awesome-ai-agent-skills --skill churn-analysis -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install seb1n/awesome-ai-agent-skills churn-analysis --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/seb1n/awesome-ai-agent-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/customer-success/churn-analysis .opencode/skills/churn-analysis && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "churn-analysis" agent skill from https://github.com/seb1n/awesome-ai-agent-skills/tree/main/customer-success/churn-analysis into .opencode/skills/churn-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "churn-analysis", 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
churn-analysis- GitHub stars
- 206
- Token cost
- ~2.1k tokens
- SKILL.md length
- 1,106 words
- Files
- 1
- Skills in repo
- 91
- Repo updated
- First seen
- Licence
- MIT
At a glance
Identify at-risk customer accounts by analyzing usage patterns, engagement signals, and support history to generate churn risk scores and intervention recommendations.
- Works in 5 steps: Collect usage and engagement data — Pull… → Define churn signals — Establish the… → Score risk per account — Compute a… → …
- The user requests churn analysis
- SKILL.md covers Workflow, Usage, Examples and Best Practices, plus 1 more section
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Churn Analysis is an agent skill from seb1n/awesome-ai-agent-skills. Identify at-risk customer accounts by analyzing usage patterns, engagement signals, and support history to generate churn risk scores and intervention recommendations. Use when the user requests churn analysis or provides relevant inputs for this workflow.
Its SKILL.md is about 2.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Sales & Support, covering Customer success. The repository describes itself as: 103 ready-to-use AI agent skills for Claude Code, OpenAI Codex, Gemini CLI, Cursor, GitHub Copilot, Windsurf, and other Agent Skills-compatible tools. Complete SKILL.md… The licence is MIT.
When your agent uses it
- The user requests churn analysis
- Provides relevant inputs for this workflow
Example prompts
- “/churn-analysis”
Workflow steps
5 steps, taken from the first numbered list in SKILL.md.
- Collect usage and engagement data — Pull metrics across product analytics (DAU, feature adoption, session duration), support history…
- Define churn signals — Establish the leading indicators that correlate with churn in your specific context. Common signals include: login…
- Score risk per account — Compute a weighted composite score from 0 (healthy) to 100 (imminent churn) for each account. Weight signals by…
- Segment into risk tiers — Bucket accounts into four tiers based on their composite score: Critical (75-100) — immediate intervention…
- Generate intervention recommendations — For each tier, produce specific action plans. Critical: executive sponsor outreach, emergency…
What it can do on your machine
Read from SKILL.md and the folder at commit 75865a5. 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
No scripts in the folder and no shell commands in SKILL.md.
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
Churn Analysis loads about 2.1k tokens when it runs. Until then it costs about 68 tokens; SKILL.md has 1,106 words of instructions outside code blocks.
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); files beside SKILL.md are not scanned.
SKILL.md
The full file from seb1n/awesome-ai-agent-skills at commit 75865a5, republished under its MIT licence (© seb1n). 1,106 words, ~2,132 tokens.
Download SKILL.mdSave it as
.claude/skills/churn-analysis/SKILL.md (or your agent's skills folder).- name
- churn-analysis
- description
- Identify at-risk customer accounts by analyzing usage patterns, engagement signals, and support history to generate churn risk scores and intervention recommendations. Use when the user requests churn analysis or provides relevant inputs for this workflow.
- license
- MIT
- metadata.author
- community
- metadata.version
- 1.0
Churn Analysis
Detect early warning signs of customer churn by aggregating usage telemetry, support interactions, billing history, and engagement metrics into a composite risk score per account. This skill segments accounts into risk tiers and produces actionable intervention playbooks tailored to each tier, enabling CS teams to proactively retain revenue.
Workflow
Collect usage and engagement data — Pull metrics across product analytics (DAU, feature adoption, session duration), support history (ticket volume, CSAT scores, escalations), billing signals (late payments, downgrade requests, contract end dates), and engagement touchpoints (email opens, webinar attendance, QBR participation). Normalize all metrics to a consistent time window (typically 90 days trailing).
Define churn signals — Establish the leading indicators that correlate with churn in your specific context. Common signals include: login frequency dropping below 50% of the account's historical average, a spike in support tickets (3x baseline) within 30 days, missed or late renewal payment, champion contact leaving the company, feature adoption plateau (no new features used in 60 days), and declining NPS scores on consecutive surveys.
Score risk per account — Compute a weighted composite score from 0 (healthy) to 100 (imminent churn) for each account. Weight signals by their predictive power — usage decline typically carries 35% weight, support sentiment 25%, billing signals 20%, and engagement metrics 20%. Adjust weights based on historical churn correlation data if available. Accounts missing data for a signal category receive a neutral score for that dimension with a data-quality flag.
Segment into risk tiers — Bucket accounts into four tiers based on their composite score: Critical (75-100) — immediate intervention required, likely to churn within 30 days. High (50-74) — concerning trends, intervention needed within 2 weeks. Medium (25-49) — early warning signs, monitor and engage proactively. Healthy (0-24) — on track, maintain regular cadence.
Generate intervention recommendations — For each tier, produce specific action plans. Critical: executive sponsor outreach, emergency success plan, potential concessions or credits. High: CSM-led deep dive call, custom training session, product roadmap preview. Medium: automated check-in email sequence, in-app tips targeting underused features, invite to community events. Healthy: upsell/cross-sell opportunity identification, referral program invitation.
Usage
Provide account data or describe the account portfolio you want analyzed. The agent will compute risk scores and return tiered recommendations.
Analyze the churn risk for our Q1 cohort of 200 accounts. Here's the usage
data export. Identify the top 10 at-risk accounts and recommend interventions.Examples
Example 1: Cohort risk analysis with scoring table
Input: Usage and engagement data for 8 accounts over the past 90 days.
Output:
| Account | Plan | Risk Score | Tier | Key Signals | Recommended Action |
|---|---|---|---|---|---|
| Acme Corp | Enterprise | 88 | Critical | Logins down 72%, 14 tickets in 30d, renewal in 18d | Exec sponsor call within 48h, offer dedicated onboarding reset, prepare 2-month extension |
| Bolt Inc | Pro | 71 | High | Feature adoption stalled, champion left org, NPS dropped from 8→4 | CSM deep-dive on use cases, identify new champion, schedule product roadmap session |
| Cedar Ltd | Enterprise | 63 | High | API usage down 45%, 2 escalated tickets, QBR declined | Technical health check, assign SE for integration review, CSM outreach to new stakeholder |
| Dash Co | Pro | 42 | Medium | Session duration declining, stopped attending webinars | Trigger re-engagement email series, in-app walkthrough for new features launched in Q4 |
| Echo LLC | Starter | 38 | Medium | Login frequency down 30%, no support contact in 60d | Automated check-in email, offer free training session |
| Forge Inc | Enterprise | 22 | Healthy | Stable usage, positive CSAT, expanding seat count | Propose enterprise add-on package, invite to advisory board |
| Grid Corp | Pro | 15 | Healthy | Growing feature adoption, 2 referrals made | Send referral program upgrade incentive, case study candidate |
| Haven Ltd | Starter | 8 | Healthy | High engagement, recent plan upgrade | Monitor, include in customer spotlight newsletter |
Summary: 2 accounts critical (25% of ARR at risk), 2 high, 2 medium, 2 healthy. Recommended immediate action on $480K combined ARR in critical tier.
Show full SKILL.md (502 more words)Show less
Example 2: Individual account churn risk deep dive
Input: "Analyze churn risk for Acme Corp — enterprise account, $240K ARR, 18 days to renewal."
Output:
Acme Corp — Risk Score: 88/100 (Critical)
| Signal Category | Weight | Score | Evidence |
|---|---|---|---|
| Usage Decline | 35% | 92 | Daily active users dropped from 145 to 41 over 60 days. Core workflow (report generation) usage down 80%. |
| Support Sentiment | 25% | 85 | 14 tickets in past 30 days (baseline: 3/month). Two P1 escalations unresolved. CSAT on last 3 tickets: 2, 1, 2. |
| Billing Signals | 20% | 78 | Renewal in 18 days, no renewal discussion initiated. Finance team requested contract terms doc (often precedes vendor evaluation). |
| Engagement | 20% | 90 | Declined last two QBR invites. Zero email opens in past 30 days. Primary champion (VP Ops) left the company 6 weeks ago. |
Intervention Plan:
- Day 0-2: VP of CS calls Acme's CTO directly. Acknowledge the degraded experience and unresolved escalations.
- Day 2-5: Assign a dedicated SE to resolve the two open P1 tickets. Provide daily status updates.
- Day 5-10: Schedule an on-site success planning session with the new decision-maker. Present a 90-day recovery roadmap.
- Day 10-15: Offer a concession package: 60-day contract extension at no cost, dedicated Slack channel for real-time support.
- Day 15-18: Secure verbal renewal commitment or escalate to CRO for executive negotiation.
Best Practices
- Recalculate risk scores weekly for critical and high-tier accounts, monthly for medium and healthy tiers to balance signal freshness with noise reduction.
- Validate your signal weights quarterly by back-testing against actual churn events — the default 35/25/20/20 split is a starting point, not a universal truth.
- Combine quantitative signals with qualitative CSM notes — a CSM flagging "bad vibes on the last call" is a legitimate signal that models miss.
- Track intervention outcomes to build a feedback loop: which actions actually prevented churn vs. delayed it vs. had no effect.
- Separate voluntary churn (dissatisfaction) from involuntary churn (payment failure, company closure) in your analysis, as they require completely different interventions.
- Account for seasonality — some industries have natural usage dips (retail post-holiday, education during summer) that should not inflate risk scores.
Edge Cases
- New accounts (under 90 days) — Insufficient historical data to compute meaningful trends. Use onboarding milestone completion rate as a proxy signal instead of usage decline.
- Accounts with a single power user — All usage is concentrated in one person. If that person's activity drops, the risk score should weight this more heavily than a distributed usage decline across many users.
- Free-to-paid conversion cohort — Recently converted accounts may show low engagement compared to established accounts but are actually ramping up. Apply a different baseline for accounts in their first renewal cycle.
- Multi-product accounts — Churn risk should be assessed per product line, not just at the account level. An account may be healthy on Product A but churning on Product B, and a blended score hides this.
- Accounts in active expansion — A temporary dip in per-seat usage metrics might reflect rapid seat additions (denominator growth) rather than actual disengagement. Normalize usage by active seats, not total seats.
© seb1n, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in customer-success/churn-analysis of seb1n/awesome-ai-agent-skills.
Open the folder on GitHubat commit 75865a5
Compare with similar skills
Churn Analysis next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Churn Analysis this skillseb1n/awesome-ai-agent-skills | 206 | — | ~2.1k | Automated safety check: Pass | MIT | |
| Youtube SearchZeroPointRepo/youtube-skills | 1k | 1 repos | ~1.8k | Automated safety check: Pass | MIT | |
| YtZeroPointRepo/youtube-skills | 1k | 1 repos | ~951 | Automated safety check: Pass | MIT | |
| Loki Modedavila7/claude-code-templates | 32k | 7 repos | ~7.1k | Automated safety check: Warn | MIT | |
| Account Researchexplorium-ai/gtm-skills | 163 | — | ~2.8k | Automated safety check: Pass | MIT | |
| Revopssickn33/agentic-awesome-skills | 47k | 2 repos | ~3.8k | Automated safety check: Pass | MIT |
Similar skills
Yt
ZeroPointRepo/youtube-skills
A skill your agent uses when YouTube is relevant: pasted video links or IDs, @handles, quick video lookups, summaries, channel latest uploads, topic search, or any request involving YouTube content…
Loki Mode
davila7/claude-code-templates
Multi-agent autonomous startup system for Claude Code. An agent skill from davila7/claude-code-templates.
Account Research
explorium-ai/gtm-skills
Account research skill for Claude Code and Codex: generate a high-signal company intelligence brief including firmographics, technographics, funding history, hiring signals, business events, recent…
Revops
sickn33/agentic-awesome-skills
Design and improve revenue operations, lead lifecycle rules, scoring, routing, handoffs, and CRM process automation.
Expansion Retention
tech-leads-club/agent-skills
When the user wants to reduce churn, build expansion revenue, automate customer success, or optimize net revenue retention.
More from seb1n/awesome-ai-agent-skills
All 91 skills in this repoAgent Red Teaming
seb1n/awesome-ai-agent-skills
Plan, execute, document, and retest authorized security assessments of AI agents and multi-agent workflows using safe adversarial cases, synthetic identities, canaries, and evidence-based findings.
Eu AI Act Readiness
seb1n/awesome-ai-agent-skills
Build a preliminary, evidence-based EU AI Act readiness assessment across AI-system inventory, territorial scope, operator roles, prohibited-practice screening, risk classification, transparency…
Human In The Loop
seb1n/awesome-ai-agent-skills
Design and verify auditable human oversight, approval gates, escalation paths, and safe state transitions for AI agent workflows.
MCP Server Building
seb1n/awesome-ai-agent-skills
Design, implement, harden, and verify Model Context Protocol (MCP) servers with precise tool contracts, least-privilege authorization, safe transports, structured errors, and interoperability tests.
Skill Supply Chain Audit
seb1n/awesome-ai-agent-skills
Audit agent skills, plugins, prompts, manifests, scripts, dependencies, and bundled assets for provenance, prompt-injection, permission, execution, exfiltration, persistence, and update risk.
Spreadsheet Analysis
seb1n/awesome-ai-agent-skills
Inspect, profile, clean, reconcile, analyze, visualize, and verify spreadsheet data while preserving formulas, formatting, types, and source files.
Related
Categories
Questions about Churn Analysis
What does Churn Analysis do?
Identify at-risk customer accounts by analyzing usage patterns, engagement signals, and support history to generate churn risk scores and intervention recommendations. Churn Analysis is an agent skill from seb1n/awesome-ai-agent-skills. Identify at-risk customer accounts by analyzing usage patterns, engagement signals, and support history to generate churn risk scores and intervention recommendations.
When should I use Churn Analysis?
Churn Analysis fits situations like: the user requests churn analysis; provides relevant inputs for this workflow.
How do I install Churn Analysis in Claude Code?
Run `npx skills add seb1n/awesome-ai-agent-skills --skill churn-analysis -a claude-code`. Or copy the skill folder (customer-success/churn-analysis in seb1n/awesome-ai-agent-skills) into .claude/skills/churn-analysis in your project. Claude Code loads it when a task matches its description.
How do I install Churn Analysis in Codex?
Run `npx skills add seb1n/awesome-ai-agent-skills --skill churn-analysis -a codex`. Or copy the skill folder (customer-success/churn-analysis in seb1n/awesome-ai-agent-skills) into .agents/skills/churn-analysis in your project. Codex loads it when a task matches its description.
Can I use Churn Analysis 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 seb1n/awesome-ai-agent-skills --skill churn-analysis -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/churn-analysis, .gemini/skills/churn-analysis, .github/skills/churn-analysis and .opencode/skills/churn-analysis in your project.
What does Churn Analysis need to run?
SKILL.md names no scripts, command-line tools or credentials: Churn Analysis is instructions for the agent only.
Does Churn Analysis 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 Churn Analysis 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. Review the folder before installing.
What licence does Churn Analysis use?
Churn Analysis 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 Churn Analysis use?
About 2.1k tokens (SKILL.md is roughly 8.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Churn Analysis?
Skills that share tags, products or a category with Churn Analysis: Youtube Search (ZeroPointRepo/youtube-skills, 1k stars), Yt (ZeroPointRepo/youtube-skills, 1k stars), Loki Mode (davila7/claude-code-templates, 32k stars) and Account Research (explorium-ai/gtm-skills, 163 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Churn Analysis?
seb1n (a GitHub user) maintains it in seb1n/awesome-ai-agent-skills, which has 206 GitHub stars. The repository holds 91 skills in this directory. The repository was last updated on August 9, 2026.
Source: seb1n/awesome-ai-agent-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.