Retention
ericrisco/rsc-harness
A skill your agent uses when revenue is leaking out the bottom of the funnel and you need a program to keep, score and win back customers — a customer health score, NPS, catching churn signals…
Scan support tickets, Slack channels, NPS scores, and usage patterns to flag accounts showing early churn indicators.
Install Churn Risk Detector
$ npx skills add gooseworks-ai/goose-skills --skill churn-risk-detector -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install gooseworks-ai/goose-skills churn-risk-detector --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/gooseworks-ai/goose-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/research/composites/churn-risk-detector .claude/skills/churn-risk-detector && 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-risk-detector" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/research/composites/churn-risk-detector into .claude/skills/churn-risk-detector/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "churn-risk-detector", 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/gooseworks-ai/goose-skills/tree/main/skills/research/composites/churn-risk-detectorType 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 gooseworks-ai/goose-skills --skill churn-risk-detector -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install gooseworks-ai/goose-skills churn-risk-detector --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/research/composites/churn-risk-detector .agents/skills/churn-risk-detector && 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-risk-detector" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/research/composites/churn-risk-detector into .agents/skills/churn-risk-detector/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "churn-risk-detector", 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 gooseworks-ai/goose-skills --skill churn-risk-detector -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install gooseworks-ai/goose-skills churn-risk-detector --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/research/composites/churn-risk-detector .cursor/skills/churn-risk-detector && 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-risk-detector" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/research/composites/churn-risk-detector into .cursor/skills/churn-risk-detector/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "churn-risk-detector", 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/gooseworks-ai/goose-skills.git --path skills/research/composites/churn-risk-detector--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 gooseworks-ai/goose-skills --skill churn-risk-detector -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install gooseworks-ai/goose-skills churn-risk-detector --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/research/composites/churn-risk-detector .gemini/skills/churn-risk-detector && 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-risk-detector" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/research/composites/churn-risk-detector into .gemini/skills/churn-risk-detector/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "churn-risk-detector", 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 gooseworks-ai/goose-skills churn-risk-detectorInstalls 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 gooseworks-ai/goose-skills --skill churn-risk-detector -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/research/composites/churn-risk-detector .github/skills/churn-risk-detector && 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-risk-detector" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/research/composites/churn-risk-detector into .github/skills/churn-risk-detector/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "churn-risk-detector", 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 gooseworks-ai/goose-skills --skill churn-risk-detector -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install gooseworks-ai/goose-skills churn-risk-detector --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/research/composites/churn-risk-detector .opencode/skills/churn-risk-detector && 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-risk-detector" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/research/composites/churn-risk-detector into .opencode/skills/churn-risk-detector/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "churn-risk-detector", 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-risk-detector- GitHub stars
- 1.2k
- Used in
- 1 other repo
- Token cost
- ~2.1k tokens
- SKILL.md length
- 655 words
- Files
- 2
- Skills in repo
- 273
- Repo updated
- First seen
- Licence
- MIT
At a glance
Scan support tickets, Slack channels, NPS scores, and usage patterns to flag accounts showing early churn indicators.
- Works in 5 steps: Intake → Signal Extraction → Risk Scoring → …
- Tasks that involve Customer success
- SKILL.md covers When to Use, Phase 0: Intake, Phase 1: Signal Extraction and Phase 2: Risk Scoring, plus 6 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Churn Risk Detector is an agent skill from gooseworks-ai/goose-skills. Scan support tickets, Slack channels, NPS scores, and usage patterns to flag accounts showing early churn indicators. Produces a weekly risk scorecard with severity tiers, root cause hypotheses, and suggested save plays per account. Designed for seed/Series A teams where the founder or a single CSM manages all accounts manually.
Its SKILL.md is about 2.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `skill.meta.json`).
It sits in Sales & Support, covering Customer success, Customer support and Customer feedback analysis. It works with Slack. The repository describes itself as: Library of Growth & GTM skills + data APIs for Claude Code, Codex, Cursor to run ads, social, content, lead gen, seo and data scraping. The licence is MIT.
When your agent uses it
- Tasks that involve Customer success
- Tasks that involve Customer support
- Tasks that involve Customer feedback analysis
Example prompts
- “/churn-risk-detector”
Requirements
- Python 3
Workflow steps
5 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit c650c6d. 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 (its code samples are markdown and bash).
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 Risk Detector loads about 2.1k tokens when it runs. Until then it costs about 88 tokens; SKILL.md has 655 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 gooseworks-ai/goose-skills at commit c650c6d, republished under its MIT licence (© gooseworks-ai). 655 words, ~2,129 tokens.
Download SKILL.mdSave it as
.claude/skills/churn-risk-detector/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.- name
- churn-risk-detector
- description
- Scan support tickets, Slack channels, NPS scores, and usage patterns to flag accounts showing early churn indicators. Produces a weekly risk scorecard with severity tiers, root cause hypotheses, and suggested save plays per account. Designed for seed/Series A teams where the founder or a single CSM manages all accounts manually.
- tags
- research
Churn Risk Detector
Surface accounts at risk of churning before it's too late. Aggregates signals from support, communication, and usage patterns into a scored risk report with specific save actions.
Built for: Early-stage teams with no CS platform (no Gainsight, no ChurnZero). You have a spreadsheet of customers, a Slack channel, and a support inbox. This skill turns those raw signals into an actionable churn risk list.
When to Use
- "Which customers are at risk of churning?"
- "Run the weekly churn risk scan"
- "Flag accounts I should worry about"
- "Who haven't we heard from in a while?"
- "Produce a customer health report"
Phase 0: Intake
Account Data
- Customer list — CSV or sheet with: company name, primary contact email, contract value (MRR/ARR), contract start date, renewal date (if known)
- Product/service type — What are they paying for? (Helps calibrate expected engagement)
Signal Sources (provide what you have)
- Support tickets — Export from Intercom, Zendesk, or email (CSV with: customer, date, subject, status, resolution time)
- Slack channel history — Customer Slack channel or shared channel messages
- NPS/CSAT scores — Recent survey results with scores and comments
- Usage data — Any metrics you track: logins, API calls, features used, active users (CSV export)
- Email/communication log — Last touchpoints per account (dates + context)
- Billing data — Payment failures, downgrades, discount requests
Calibration
- What does "healthy" look like? — Describe a healthy customer (e.g., "logs in daily, uses 3+ features, responds to emails within 24h")
- Known churn reasons — Why have customers churned in the past? (helps weight signals)
Phase 1: Signal Extraction
1A: Support Signal Analysis
From support ticket data, calculate per account:
| Signal | Calculation | Risk Weight |
|---|---|---|
| Ticket volume spike | >2x their average in last 30 days | High |
| Unresolved tickets | Open tickets older than 7 days | High |
| Escalation language | Keywords: "cancel", "frustrated", "alternative", "not working", "disappointed" | Critical |
| Response time degradation | Your avg response time to this customer trending up | Medium |
| Repeat issues | Same problem reported 2+ times | High |
1B: Communication Signal Analysis
From Slack/email history:
| Signal | Calculation | Risk Weight |
|---|---|---|
| Gone silent | No messages in 30+ days (was previously active) | High |
| Decreasing frequency | Message frequency dropped >50% vs prior 90 days | Medium |
| Negative sentiment shift | Tone changed from positive to neutral/negative | Medium |
| Champion disengagement | Primary contact stopped responding | Critical |
| New stakeholder questions | New person asking basic "what does this do?" questions | Medium (potential reorg) |
Show full SKILL.md (265 more words)Show less
1C: Usage Signal Analysis (if data available)
| Signal | Calculation | Risk Weight |
|---|---|---|
| Login drop | Active users down >30% vs prior month | High |
| Feature abandonment | Stopped using a key feature they previously used regularly | High |
| Shallow usage | Only using 1 feature when they're paying for many | Medium |
| No growth | Same number of seats/users for 6+ months | Low |
| Export spike | Sudden increase in data exports | Critical (may be migrating) |
1D: Commercial Signal Analysis
| Signal | Calculation | Risk Weight |
|---|---|---|
| Discount request | Asked for pricing reduction | High |
| Downgrade inquiry | Asked about lower tier | Critical |
| Payment failure | Failed payment not resolved in 7+ days | High |
| Contract approaching renewal | <60 days to renewal with no renewal discussion | Medium |
| Competitor mention | Mentioned a competitor in any channel | High |
Phase 2: Risk Scoring
Scoring Model
Each account gets a composite risk score (0-100):
Risk Score = Σ (signal_weight × signal_present)
Weights:
Critical signal = 25 points each
High signal = 15 points each
Medium signal = 8 points each
Low signal = 3 points each
Score cap: 100Risk Tiers
| Tier | Score | Label | Action Urgency |
|---|---|---|---|
| Red | 70-100 | Critical risk — likely to churn | This week |
| Orange | 40-69 | Elevated risk — needs attention | Within 2 weeks |
| Yellow | 20-39 | Early warning — monitor closely | Within 30 days |
| Green | 0-19 | Healthy — no action needed | Routine check-in |
Phase 3: Save Play Generation
For each Red and Orange account, generate a specific save play:
Save Play Template
ACCOUNT: [Company Name]
RISK TIER: [Red/Orange]
RISK SCORE: [X/100]
MRR/ARR: $[X]
SIGNALS DETECTED:
- [Signal 1] — [Evidence: specific data point]
- [Signal 2] — [Evidence]
- [Signal 3] — [Evidence]
ROOT CAUSE HYPOTHESIS:
[1-2 sentences: What do you think is actually going wrong?
E.g., "Champion left the company and new stakeholder hasn't been onboarded"
or "They hit a technical limitation with [feature] that's blocking their primary use case"]
RECOMMENDED SAVE PLAY:
1. [Immediate action — e.g., "Schedule a call with [contact] this week"]
2. [Follow-up — e.g., "Send a personalized Loom showing how to solve [specific issue]"]
3. [Structural fix — e.g., "Assign a dedicated onboarding session for new stakeholder"]
TALK TRACK:
"[2-3 sentences the CSM/founder can use to open the conversation naturally,
without saying 'we noticed you might be churning']"
ESCALATION TRIGGER:
If [specific condition] by [date], escalate to [founder/CEO call].Phase 4: Output Format
# Churn Risk Report — Week of [DATE]
Total accounts scanned: [N]
Data sources: [list what was available]
---
## Risk Summary
| Tier | Count | Total MRR at Risk |
|------|-------|-------------------|
| 🔴 Red (Critical) | [N] | $[X] |
| 🟠 Orange (Elevated) | [N] | $[X] |
| 🟡 Yellow (Early Warning) | [N] | $[X] |
| 🟢 Green (Healthy) | [N] | $[X] |
**Total MRR at risk (Red + Orange):** $[X] ([Y]% of total MRR)
---
## 🔴 Critical Risk Accounts
### [Company Name 1] — Score: [X]/100 | MRR: $[X]
**Signals:** [bullet list]
**Root cause:** [hypothesis]
**Save play:** [specific actions]
**Owner:** [who should act]
**Deadline:** [date]
### [Company Name 2] — ...
---
## 🟠 Elevated Risk Accounts
### [Company Name] — Score: [X]/100 | MRR: $[X]
**Signals:** [bullet list]
**Recommended action:** [1-2 sentences]
---
## 🟡 Early Warning Accounts
| Account | Score | Key Signal | Suggested Action |
|---------|-------|------------|-----------------|
| [Name] | [X] | [Signal] | [Action] |
| [Name] | [X] | [Signal] | [Action] |
---
## Trends vs Last Week
- Accounts moved Red → Green: [list — wins!]
- Accounts moved Green → Yellow/Orange: [list — new risks]
- Accounts churned since last report: [list]
---
## Signal Distribution
| Signal Type | Accounts Affected |
|------------|-------------------|
| Support ticket spike | [N] |
| Gone silent | [N] |
| Usage decline | [N] |
| Competitor mention | [N] |
| Payment issue | [N] |
| Champion disengagement | [N] |
---
## Recommended Focus This Week
1. **[Account]** — [Why + what to do]
2. **[Account]** — [Why + what to do]
3. **[Account]** — [Why + what to do]Save to risk-report-[YYYY-MM-DD].md in the current working directory.
Scheduling
Run weekly:
0 8 * * 1 python3 run_skill.py churn-risk-detector --client <client-name>Cost
| Component | Cost |
|---|---|
| All signal analysis | Free (LLM reasoning) |
| Slack/email parsing | Free |
| Total | Free |
Tools Required
- Input data from CSV/sheets (support tickets, usage, NPS)
- Optional: Slack channel reading for communication signals
- No external API costs — pure analysis
Trigger Phrases
- "Which customers are at risk?"
- "Run the churn risk scan"
- "Weekly customer health report"
- "Flag at-risk accounts"
© gooseworks-ai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 1 other file in skills/research/composites/churn-risk-detector of gooseworks-ai/goose-skills.
- SKILL.md
- skill.meta.json
Open the folder on GitHubat commit c650c6d
Used in 1 other repository
We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in gooseworks-ai/goose-skills, which our catalogue first saw on October 9, 2026.
Compare with similar skills
Churn Risk Detector 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 Risk Detector this skillgooseworks-ai/goose-skills | 1.2k | 1 repos | ~2.1k | Automated safety check: Pass | MIT | |
| Retentionericrisco/rsc-harness | 180 | — | ~2.8k | Automated safety check: Pass | MIT | |
| Loki Modedavila7/claude-code-templates | 33k | 7 repos | ~7.1k | Automated safety check: Warn | MIT | |
| Msp QbrRTFM-IT-Services-LLC/msp-claude-skills | 115 | — | ~1.7k | Automated safety check: Pass | Custom licence | |
| Customer Supportaiskillstore/marketplace | 433 | 7 repos | ~2.2k | Automated safety check: Pass | None | |
| Afa Cxafadtc/afa-dtc-skills | 168 | — | ~2.4k | Automated safety check: Pass | Custom licence |
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Questions about Churn Risk Detector
What does Churn Risk Detector do?
Scan support tickets, Slack channels, NPS scores, and usage patterns to flag accounts showing early churn indicators. Churn Risk Detector is an agent skill from gooseworks-ai/goose-skills. Scan support tickets, Slack channels, NPS scores, and usage patterns to flag accounts showing early churn indicators.
When should I use Churn Risk Detector?
Churn Risk Detector fits situations like: tasks that involve Customer success; tasks that involve Customer support; tasks that involve Customer feedback analysis.
How do I install Churn Risk Detector in Claude Code?
Run `npx skills add gooseworks-ai/goose-skills --skill churn-risk-detector -a claude-code`. Or copy the skill folder (skills/research/composites/churn-risk-detector in gooseworks-ai/goose-skills) into .claude/skills/churn-risk-detector in your project. Claude Code loads it when a task matches its description.
How do I install Churn Risk Detector in Codex?
Run `npx skills add gooseworks-ai/goose-skills --skill churn-risk-detector -a codex`. Or copy the skill folder (skills/research/composites/churn-risk-detector in gooseworks-ai/goose-skills) into .agents/skills/churn-risk-detector in your project. Codex loads it when a task matches its description.
Can I use Churn Risk Detector 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 gooseworks-ai/goose-skills --skill churn-risk-detector -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-risk-detector, .gemini/skills/churn-risk-detector, .github/skills/churn-risk-detector and .opencode/skills/churn-risk-detector in your project.
What does Churn Risk Detector need to run?
SKILL.md names no scripts, command-line tools or credentials: Churn Risk Detector is instructions for the agent only. Our summary lists: Python 3.
Does Churn Risk Detector 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 Risk Detector 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 Risk Detector use?
Churn Risk Detector is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Churn Risk Detector 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 Risk Detector?
Skills that share tags, products or a category with Churn Risk Detector: Retention (ericrisco/rsc-harness, 180 stars), Loki Mode (davila7/claude-code-templates, 33k stars), Msp Qbr (RTFM-IT-Services-LLC/msp-claude-skills, 115 stars) and Customer Support (aiskillstore/marketplace, 433 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Churn Risk Detector?
gooseworks-ai (a GitHub organization) maintains it in gooseworks-ai/goose-skills, which has 1,240 GitHub stars. The repository holds 273 skills in this directory. The repository was last updated on October 8, 2026.
Source: gooseworks-ai/goose-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.