Autofocus Avoidance
thedaviddias/Front-End-Checklist
A skill your agent uses when reviewing rendered HTML, interactive components, or design-system patterns related to Avoid autofocus on form fields.
Produce a structured churn analysis that separates avoidable from unavoidable churn.
MITAuto-check passed
Install Churn Analysis
$ npx skills add mohitagw15856/pm-claude-skills --skill churn-analysis -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install mohitagw15856/pm-claude-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/mohitagw15856/pm-claude-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/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/mohitagw15856/pm-claude-skills/tree/main/skills/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/mohitagw15856/pm-claude-skills/tree/main/skills/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 mohitagw15856/pm-claude-skills --skill churn-analysis -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install mohitagw15856/pm-claude-skills churn-analysis --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/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/mohitagw15856/pm-claude-skills/tree/main/skills/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 mohitagw15856/pm-claude-skills --skill churn-analysis -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install mohitagw15856/pm-claude-skills churn-analysis --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/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/mohitagw15856/pm-claude-skills/tree/main/skills/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/mohitagw15856/pm-claude-skills.git --path skills/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 mohitagw15856/pm-claude-skills --skill churn-analysis -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install mohitagw15856/pm-claude-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/mohitagw15856/pm-claude-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/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/mohitagw15856/pm-claude-skills/tree/main/skills/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 mohitagw15856/pm-claude-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 mohitagw15856/pm-claude-skills --skill churn-analysis -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/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/mohitagw15856/pm-claude-skills/tree/main/skills/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 mohitagw15856/pm-claude-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 mohitagw15856/pm-claude-skills churn-analysis --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/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/mohitagw15856/pm-claude-skills/tree/main/skills/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
- 1.4k
- Token cost
- ~2.5k tokens
- SKILL.md length
- 1,210 words
- Files
- 4 (incl. references)
- Skills in repo
- 1,348
- Repo updated
- First seen
- Licence
- MIT
At a glance
Produce a structured churn analysis that separates avoidable from unavoidable churn.
- Investigating why customers are leaving
- SKILL.md covers Reads from / Writes to the Brain, Required Inputs, Churn Categories and Output Format, plus 13 more sections
- Calls python3
- Identifying at-risk segments
What it does
Churn Analysis is an agent skill from mohitagw15856/pm-claude-skills. Produce a structured churn analysis that separates avoidable from unavoidable churn. Use when investigating why customers are leaving, identifying at-risk segments, calculating net revenue retention, or building a retention intervention plan. Produces a churn report with rate calculations, categorised reasons by avoidability, segment breakdown, timing analysis, early warning signals, and prioritised interventions ranked by estimated impact.
Its SKILL.md is about 2.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including reference files (for example `references/avoidability-calls.md`, `references/worked-example.md` and `templates/churn-report.md`).
The repository describes itself as: 1255 professional Agent Skills for Claude, ChatGPT, Gemini, Cursor & Codex — PRDs, postmortems, leases, medical bills, layoffs, go-bags, new countries. Plain markdown, MIT, in… The licence is MIT.
When your agent uses it
- Investigating why customers are leaving
- Identifying at-risk segments
- Calculating net revenue retention
- Building a retention intervention plan
Example prompts
- “/churn-analysis”
Requirements
- Python 3
What it can do on your machine
Read from SKILL.md and the folder at commit 1cbf1f0. 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
Shell commands in SKILL.md call:
python3
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 2.5k tokens when it runs, and up to ~4.8k if it reads all its reference files. Until then it costs about 115 tokens; SKILL.md has 1,210 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 mohitagw15856/pm-claude-skills at commit 1cbf1f0, republished under its MIT licence (© mohitagw15856). 1,210 words, ~2,496 tokens.
Download SKILL.mdSave it as
.claude/skills/churn-analysis/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.- name
- churn-analysis
- description
- Produce a structured churn analysis that separates avoidable from unavoidable churn. Use when investigating why customers are leaving, identifying at-risk segments, calculating net revenue retention, or building a retention intervention plan. Produces a churn report with rate calculations, categorised reasons by avoidability, segment breakdown, timing analysis, early warning signals, and prioritised interventions ranked by estimated impact.
Churn Analysis Skill
Produce a structured churn analysis that goes beyond the headline rate — identifying why customers leave, which segments are most at risk, and what interventions will have the highest impact on retention.
Reads from / Writes to the Brain
If a professional-brain (brain/) exists, ground in it instead of re-asking for what you already know:
- Read first:
context.md(metric definitions — what "churn" means here),knowledge/, and related segmententities/. Runpython3 ../professional-brain/scripts/brain_query.py ./brain "churn"and carry each fact's provenance tag through. - 📥 Propose to the Brain: after producing, propose recording the headline retention finding to
knowledge/([data]), any retention decision todecisions/, and at-risk drivers ashypotheses/. Show them, get a yes, then write with../professional-brain/scripts/brain_write.py … --commit(append-only, dry-run by default).
Required Inputs
Ask for these if not already provided:
- Time period being analysed (e.g. Q1, last 12 months)
- Total customers at start of period and customers churned
- ARR or revenue lost to churn
- Churn reasons data — exit survey results, CSM notes, support data, or sales loss reasons
- Customer segments — by tier, industry, cohort, or product line
- Current retention rate if known
- Any recent changes — pricing, product, support model — that may have affected churn
Churn Categories
Always classify churn before analysing it:
| Category | Definition |
|---|---|
| Voluntary — avoidable | Customer left due to a problem we could have addressed (product gaps, poor onboarding, relationship failures) |
| Voluntary — unavoidable | Customer left for reasons outside our control (budget cuts, acquisition, company shutdown) |
| Involuntary | Payment failure, contract non-renewal by mistake, admin error |
The interventions for each category are different. Conflating them leads to wrong conclusions.
Output Format
Churn Analysis: [Product / Segment / Company]
Period: [Start date] — [End date] Prepared by: [Name] | Date: [Date]
Headline Numbers
| Metric | Value |
|---|---|
| Customers at start of period | [N] |
| Customers churned | [N] |
| Customer churn rate | [X]% |
| ARR at start of period | £/$/€[X] |
| ARR lost to churn | £/$/€[X] |
| Revenue churn rate (gross) | [X]% |
| ARR from expansions (same period) | £/$/€[X] |
| Net revenue retention (NRR) | [X]% |
Benchmark context:
- Customer churn rate: [X]% vs. industry benchmark [Y]% — [above / below / in line]
- NRR: [X]% — [What this means: above 100% = expansion offsets churn; below 100% = shrinking base]
Churn Breakdown by Category
| Category | Customers | % of churn | ARR lost |
|---|---|---|---|
| Voluntary — avoidable | [N] | [X]% | £/$/€[X] |
| Voluntary — unavoidable | [N] | [X]% | £/$/€[X] |
| Involuntary | [N] | [X]% | £/$/€[X] |
| Total | [N] | 100% | £/$/€[X] |
Avoidable churn as % of total churn: [X]% — this is the number we can actually influence.
Churn Reasons — Avoidable Churn Only
Rank by frequency. Include ARR weight where data allows.
| Reason | Count | % of avoidable churn | ARR lost | Representative quote |
|---|---|---|---|---|
| [Reason 1 — e.g. "Product missing key feature"] | [N] | [X]% | £/$/€[X] | "[Quote]" |
| [Reason 2] | [N] | [X]% | £/$/€[X] | "[Quote]" |
| [Reason 3] | [N] | [X]% | £/$/€[X] | "[Quote]" |
| [Reason 4] | [N] | [X]% | £/$/€[X] | "[Quote]" |
| Other | [N] | [X]% | £/$/€[X] | — |
Theme synthesis: [2–3 sentences grouping the top reasons into 2–3 themes. E.g. "The top three reasons cluster around two themes: product gaps in [area] (affecting X% of avoidable churn) and onboarding failures where customers never achieved value (Y%)."]
Churn by Segment
Identify which segments over- or under-index for churn.
By Tier
| Tier | Churn rate | vs. Overall | Notes |
|---|---|---|---|
| Enterprise | [X]% | +/-[X]pp | |
| Mid-Market | [X]% | +/-[X]pp | |
| SMB | [X]% | +/-[X]pp |
By Cohort (Acquisition Year)
| Cohort | Churn rate | Notes |
|---|---|---|
| [Year 1] | [X]% | |
| [Year 2] | [X]% | |
| [Year 3] | [X]% |
By Industry / Use Case (if data available)
| Segment | Churn rate | Notes |
|---|---|---|
| [Segment 1] | [X]% | |
| [Segment 2] | [X]% |
Key pattern: [Which segment has the highest churn rate and what likely explains it]
Timing Analysis
- Average contract length before churn: [X months]
- Highest-risk moment: [e.g. "Month 3 — when trial value has worn off but full adoption hasn't happened"]
- Churn timing distribution:
| When churn occurred | % of churned accounts |
|---|---|
| 0–3 months | [X]% |
| 3–6 months | [X]% |
| 6–12 months | [X]% |
| 12+ months | [X]% |
Early Warning Signals
Based on the churned accounts, identify the signals that preceded churn (and could have triggered earlier intervention):
| Signal | Lead time before churn | How to detect |
|---|---|---|
| [Signal 1 — e.g. "DAU/MAU dropped below 15%"] | [~X weeks] | [Usage dashboard / alert] |
| [Signal 2 — e.g. "No QBR in 90+ days"] | [~X weeks] | [CRM flag] |
| [Signal 3 — e.g. "Champion left the account"] | [~X weeks] | [LinkedIn alert / CSM tracking] |
| [Signal 4] | [~X weeks] | [Detection method] |
Intervention Recommendations
Ranked by estimated impact × feasibility.
| Intervention | Addresses | Est. churn reduction | Effort | Owner |
|---|---|---|---|---|
| [Intervention 1 — e.g. "Improve onboarding for [segment] with dedicated 30-day check-in"] | [Reason 1] | [X accounts / £X ARR] | Low / Med / High | [Team] |
| [Intervention 2] | [Reason 2] | [X accounts / £X ARR] | Low / Med / High | [Team] |
| [Intervention 3] | [Reason 3] | [X accounts / £X ARR] | Low / Med / High | [Team] |
Priority call: [Which one intervention, if implemented this quarter, would have the biggest impact and why]
Show full SKILL.md (458 more words)Show less
What We Don't Know (Data Gaps)
- [Data gap 1 — e.g. "Exit survey response rate is only 30% — the reasons data may not be representative"]
- [Data gap 2 — e.g. "No product usage data for SMB tier — can't confirm usage signal correlation"]
- [Data gap 3]
Anti-Patterns
- Do not mix avoidable and unavoidable churn in intervention plans — recommending product fixes for customers who churned due to company shutdown wastes resources
- Do not calculate churn rate using end-of-period customer count as the denominator — this understates churn; always divide churned customers by the starting cohort
- Do not rely solely on exit survey data for churn reasons — response rates are typically low and self-selection biases the sample toward customers who are engaged enough to complete a survey
- Do not recommend interventions without linking them to a specific churn reason — interventions disconnected from root causes will not move retention
- Do not report only gross revenue churn — without net revenue retention (NRR), a healthy-looking retention number can hide a shrinking revenue base
Deeper Materials
This skill ships with support files — use them when they are available:
references/avoidability-calls.md— Avoidable or Not? The Judgment Calls in Churn Classification. Apply it while producing the output; it carries the calibration and judgment calls the method summary above compresses.templates/churn-report.md— a fill-in version of the deliverable with the quality gates inline. Offer it when the user wants to work the document themselves rather than have it generated.
Scoring Rubric (0–40)
Score any output of this skill before handing it over; 32+ is ship-quality.
| Dimension | 0 | 5 | 10 |
|---|---|---|---|
| Rate math integrity | Churn computed on end-of-period count; no NRR | Correct denominator but gross churn only | Correct denominator, gross and net side by side, benchmark context that interprets rather than decorates |
| Avoidability separation | All churn treated as one pool | Categories tabulated but interventions still address the full pool | Avoidable/unavoidable/involuntary split carried through every downstream section; interventions touch only the avoidable share |
| Segment & timing insight | Averages only | Segment table present but no over-index reading | Names the specific over-indexing cell (tier × cohort) and the highest-risk moment, with the "why" |
| Intervention linkage | Recommendations float free of causes | Each intervention names a reason but impact is unsized | Every intervention maps to a ranked reason with estimated accounts/ARR recovered, and the priority call justifies its sequencing |
Quality Checks
- Churn rate is correctly calculated (churned ÷ starting cohort, not end-of-period total)
- Avoidable and unavoidable churn are separated — interventions target avoidable churn only
- Churn reasons are customer-reported, not internally assumed
- Segment analysis identifies which segments over-index — not just averages
- Early warning signals are specific and detectable, not generic ("low engagement")
- Interventions link directly to the top churn reasons — no recommendations without a root cause match
Example Trigger Phrases
- "Investigate why customers are leaving."
- "Calculate net revenue retention."
- "Build a retention intervention plan."
© mohitagw15856, 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 3 other files (references) in skills/churn-analysis of mohitagw15856/pm-claude-skills.
- SKILL.md
- references/avoidability-calls.md
- references/worked-example.md
- templates/churn-report.md
Open the folder on GitHubat commit 1cbf1f0
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 skillmohitagw15856/pm-claude-skills | 1.4k | — | ~2.5k | Automated safety check: Pass | MIT | |
| Autofocus Avoidancethedaviddias/Front-End-Checklist | 74k | — | ~508 | Automated safety check: Pass | MIT | |
| Developer Churnsickn33/agentic-awesome-skills | 47k | 1 repos | ~457 | Automated safety check: Pass | MIT | |
| Avoid Evalthedaviddias/Front-End-Checklist | 74k | — | ~554 | Automated safety check: Pass | MIT | |
| Keep Churnjeremylongshore/tons-of-skills-marketplace | 2.8k | — | ~1.6k | Automated safety check: Notes | MIT | |
| Avoid AI Writingsickn33/agentic-awesome-skills | 47k | 2 repos | ~684 | Automated safety check: Pass | MIT |
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Questions about Churn Analysis
What does Churn Analysis do?
Produce a structured churn analysis that separates avoidable from unavoidable churn. Churn Analysis is an agent skill from mohitagw15856/pm-claude-skills. Produce a structured churn analysis that separates avoidable from unavoidable churn.
When should I use Churn Analysis?
Churn Analysis fits situations like: investigating why customers are leaving; identifying at-risk segments; calculating net revenue retention; building a retention intervention plan.
How do I install Churn Analysis in Claude Code?
Run `npx skills add mohitagw15856/pm-claude-skills --skill churn-analysis -a claude-code`. Or copy the skill folder (skills/churn-analysis in mohitagw15856/pm-claude-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 mohitagw15856/pm-claude-skills --skill churn-analysis -a codex`. Or copy the skill folder (skills/churn-analysis in mohitagw15856/pm-claude-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 mohitagw15856/pm-claude-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?
Going by SKILL.md and its folder, Churn Analysis needs the command-line tools its instructions call (python3). Our summary lists: Python 3.
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 (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Churn Analysis use?
About 2.5k tokens (SKILL.md is roughly 10k 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 2.3k tokens, read only when the agent opens those files.
What are the alternatives to Churn Analysis?
Skills that share tags, products or a category with Churn Analysis: Autofocus Avoidance (thedaviddias/Front-End-Checklist, 74k stars), Developer Churn (sickn33/agentic-awesome-skills, 47k stars), Avoid Eval (thedaviddias/Front-End-Checklist, 74k stars) and Keep Churn (jeremylongshore/tons-of-skills-marketplace, 2.8k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Churn Analysis?
mohitagw15856 (a GitHub user) maintains it in mohitagw15856/pm-claude-skills, which has 1,434 GitHub stars. The repository holds 1,348 skills in this directory. The repository was last updated on October 9, 2026.
Source: mohitagw15856/pm-claude-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.