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ImCa0/just-laws
Convert files and office documents to Markdown. An agent skill from ImCa0/just-laws.
Fit a retention curve to observed cohort data and project LTV — computed, not estimated.
Install Cohort Curve Model
$ npx skills add mohitagw15856/pm-claude-skills --skill cohort-curve-model -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install mohitagw15856/pm-claude-skills cohort-curve-model --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/cohort-curve-model .claude/skills/cohort-curve-model && 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 "cohort-curve-model" agent skill from https://github.com/mohitagw15856/pm-claude-skills/tree/main/skills/cohort-curve-model into .claude/skills/cohort-curve-model/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cohort-curve-model", 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/cohort-curve-modelType 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 cohort-curve-model -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install mohitagw15856/pm-claude-skills cohort-curve-model --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/cohort-curve-model .agents/skills/cohort-curve-model && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "cohort-curve-model" agent skill from https://github.com/mohitagw15856/pm-claude-skills/tree/main/skills/cohort-curve-model into .agents/skills/cohort-curve-model/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cohort-curve-model", 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 cohort-curve-model -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install mohitagw15856/pm-claude-skills cohort-curve-model --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/cohort-curve-model .cursor/skills/cohort-curve-model && 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 "cohort-curve-model" agent skill from https://github.com/mohitagw15856/pm-claude-skills/tree/main/skills/cohort-curve-model into .cursor/skills/cohort-curve-model/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cohort-curve-model", 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/cohort-curve-model--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 cohort-curve-model -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install mohitagw15856/pm-claude-skills cohort-curve-model --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/cohort-curve-model .gemini/skills/cohort-curve-model && 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 "cohort-curve-model" agent skill from https://github.com/mohitagw15856/pm-claude-skills/tree/main/skills/cohort-curve-model into .gemini/skills/cohort-curve-model/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cohort-curve-model", 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 cohort-curve-modelInstalls 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 cohort-curve-model -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/cohort-curve-model .github/skills/cohort-curve-model && 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 "cohort-curve-model" agent skill from https://github.com/mohitagw15856/pm-claude-skills/tree/main/skills/cohort-curve-model into .github/skills/cohort-curve-model/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cohort-curve-model", 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 cohort-curve-model -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 cohort-curve-model --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/cohort-curve-model .opencode/skills/cohort-curve-model && 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 "cohort-curve-model" agent skill from https://github.com/mohitagw15856/pm-claude-skills/tree/main/skills/cohort-curve-model into .opencode/skills/cohort-curve-model/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cohort-curve-model", 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
cohort-curve-model- GitHub stars
- 1.4k
- Token cost
- ~997 tokens
- SKILL.md length
- 507 words
- Files
- 2 (incl. scripts)
- Skills in repo
- 1,348
- Repo updated
- First seen
- Licence
- MIT
At a glance
Fit a retention curve to observed cohort data and project LTV — computed, not estimated.
- Works in 4 steps: The fit — a (scale), b (decay), R² of… → The projection — observed vs fitted by… → The money — lifetime periods (Σ fitted… → …
- Someone has real cohort retention numbers (month 0
- SKILL.md covers Required Inputs, Output Format, Programmatic Helper and Quality Checks, plus 2 more sections
- Runs Python scripts from its folder; calls python3
What it does
Cohort Curve Model is an agent skill from mohitagw15856/pm-claude-skills. Fit a retention curve to observed cohort data and project LTV — computed, not estimated. Use when someone has real cohort retention numbers (month 0, 1, 2…) and asks what lifetime value, lifetime periods, or long-run retention they imply, or whether retention is flattening or leaking. Produces a fitted power curve (parameters, R², retention floor), a 24-36 period projection, and a real .xlsx with live formulas where editing ARPU recalculates LTV — via the bundled zero-dependency script.
Its SKILL.md is about 1000 tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including scripts (for example `scripts/cohort_model.py`).
It sits in Documents & Office, covering Excel spreadsheets. It works with Microsoft Excel. 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
- Someone has real cohort retention numbers (month 0
- 2…) and asks what lifetime value
- Lifetime periods
- Long-run retention they imply
Example prompts
- “/cohort-curve-model”
Requirements
- Python 3
Workflow steps
4 steps, taken from the first numbered list in SKILL.md.
- The fit — a (scale), b (decay), R² of the log-log fit, and the observed tail floor. Interpret b plainly: b < 0.5 = strong flattening, a…
- The projection — observed vs fitted by period, marked where observation ends and projection begins.
- The money — lifetime periods (Σ fitted retention over the horizon) and LTV = ARPU × lifetime periods.
- The caveat that matters most — if R² < 0.9, say the power family fits poorly and the projection should be distrusted beyond the observed…
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
Ships 1 file in scripts/ (Python), which the agent can run.
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
Cohort Curve Model loads about 997 tokens when it runs. Until then it costs about 128 tokens; SKILL.md has 507 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); the scripts in this folder are not scanned.
SKILL.md
The full file from mohitagw15856/pm-claude-skills at commit 1cbf1f0, republished under its MIT licence (© mohitagw15856). 507 words, ~997 tokens.
Download SKILL.mdSave it as
.claude/skills/cohort-curve-model/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.- name
- cohort-curve-model
- description
- Fit a retention curve to observed cohort data and project LTV — computed, not estimated. Use when someone has real cohort retention numbers (month 0, 1, 2…) and asks what lifetime value, lifetime periods, or long-run retention they imply, or whether retention is flattening or leaking. Produces a fitted power curve (parameters, R², retention floor), a 24-36 period projection, and a real .xlsx with live formulas where editing ARPU recalculates LTV — via the bundled zero-dependency script.
Cohort Curve Model
Retention data has a shape, and the shape is the business. This skill fits the standard consumer-retention power curve r(t) = a·t^(−b) to observed cohort data by log-log least squares — actual arithmetic run by the bundled script, not model vibes — then projects it forward and prices it.
Required Inputs
- Observed retention by period — from period 0 (100%) through at least period 3-4. Percent or fraction, either works. More periods = a trustworthy fit; 4 is the floor.
- ARPU per period (optional) — revenue per retained user per period. Without it, LTV is reported in lifetime-period multiples instead of currency.
- Projection horizon (optional, default 24 periods).
If the requester has cohort tables (rows of cohorts × months), take the average by period-age or fit the most recent complete cohort — say which you did.
Output Format
- The fit — a (scale), b (decay), R² of the log-log fit, and the observed tail floor. Interpret b plainly: b < 0.5 = strong flattening, a habit is forming; 0.5–1 = normal decay; b > 1 = leaky bucket, the curve never accumulates a base.
- The projection — observed vs fitted by period, marked where observation ends and projection begins.
- The money — lifetime periods (Σ fitted retention over the horizon) and LTV = ARPU × lifetime periods.
- The caveat that matters most — if R² < 0.9, say the power family fits poorly and the projection should be distrusted beyond the observed tail.
Programmatic Helper
This skill ships scripts/cohort_model.py — zero dependencies (stdlib zip+XML). The math and the workbook both come from the script; run it rather than computing by hand:
python3 scripts/cohort_model.py fit cohorts.xlsx --observed '[100,62,48,41,37,34,32]' --arpu 40 --horizon 24It prints the fit (a=0.619 b=0.371 R²=1.000 lifetime≈7.7 periods LTV≈308) and writes an .xlsx with a Model sheet (parameters + an editable ARPU cell wired to LTV by a live formula) and a Curve sheet (observed vs fitted vs projected). Requires a code-execution environment.
Show full SKILL.md (207 more words)Show less
Quality Checks
- Period 0 is normalised to 100% and the input had at least 4 periods — otherwise the fit was refused, not fudged
- R² is reported next to the projection, and a fit below 0.9 carries an explicit "distrust beyond the tail" warning
- The b-parameter is interpreted in words (flattening / normal / leaky), not left as a naked number
- LTV states its horizon — "LTV over 24 periods", never an unbounded number
- The xlsx was actually generated by the script and the ARPU cell recalculates LTV
Anti-Patterns
- Do not fit fewer than 4 periods — two points always fit a power law and mean nothing
- Do not project a poor fit silently — a beautiful curve through bad residuals is how LTV fictions get funded
- Do not quote LTV without the horizon — "lifetime" hides the assumption that matters
- Do not average incomplete cohorts into the input (young cohorts drag the tail down mechanically — survivorship in reverse)
- Do not present the fitted floor as a promise — it is an extrapolation, and the honest phrasing is "if the current shape holds"
Example Trigger Phrases
- "Here's our cohort retention: what's the LTV?"
- "Fit a retention curve to these cohorts."
- "How long do customers stay, based on this data?"
- "Is our retention improving across cohorts?"
© 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 1 other file (scripts) in skills/cohort-curve-model of mohitagw15856/pm-claude-skills.
- SKILL.md
- scripts/cohort_model.py
Open the folder on GitHubat commit 1cbf1f0
Compare with similar skills
Cohort Curve Model 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 |
|---|---|---|---|---|---|---|
| Cohort Curve Model this skillmohitagw15856/pm-claude-skills | 1.4k | — | ~997 | Automated safety check: Pass | MIT | |
| MarkitdownImCa0/just-laws | 781 | 14 repos | ~3.2k | Automated safety check: Notes | MIT | |
| Data Table Managern8n-io/n8n | 207k | — | ~2.3k | Automated safety check: Pass | Custom licence | |
| Docx4jplutext/docx4j | 2.4k | — | ~2.5k | Automated safety check: Pass | None | |
| Instrument Data To Allotropeaws-samples/amazon-bedrock-agents-healthcare-lifesciences | 274 | 2 repos | ~2.7k | Automated safety check: Pass | Apache-2.0 | |
| Cyber Pptcrazyykhllc-bit/CyberPPT | 1.8k | — | ~10k | Automated safety check: Pass | MIT |
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Questions about Cohort Curve Model
What does Cohort Curve Model do?
Fit a retention curve to observed cohort data and project LTV — computed, not estimated. Cohort Curve Model is an agent skill from mohitagw15856/pm-claude-skills. Fit a retention curve to observed cohort data and project LTV — computed, not estimated.
When should I use Cohort Curve Model?
Cohort Curve Model fits situations like: someone has real cohort retention numbers (month 0; 2…) and asks what lifetime value; lifetime periods; long-run retention they imply.
How do I install Cohort Curve Model in Claude Code?
Run `npx skills add mohitagw15856/pm-claude-skills --skill cohort-curve-model -a claude-code`. Or copy the skill folder (skills/cohort-curve-model in mohitagw15856/pm-claude-skills) into .claude/skills/cohort-curve-model in your project. Claude Code loads it when a task matches its description.
How do I install Cohort Curve Model in Codex?
Run `npx skills add mohitagw15856/pm-claude-skills --skill cohort-curve-model -a codex`. Or copy the skill folder (skills/cohort-curve-model in mohitagw15856/pm-claude-skills) into .agents/skills/cohort-curve-model in your project. Codex loads it when a task matches its description.
Can I use Cohort Curve Model 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 cohort-curve-model -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/cohort-curve-model, .gemini/skills/cohort-curve-model, .github/skills/cohort-curve-model and .opencode/skills/cohort-curve-model in your project.
What does Cohort Curve Model need to run?
Going by SKILL.md and its folder, Cohort Curve Model needs Python for the scripts in its folder and the command-line tools its instructions call (python3). Our summary lists: Python 3.
Does Cohort Curve Model 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 Cohort Curve Model 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 Cohort Curve Model use?
Cohort Curve Model 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 Cohort Curve Model use?
About 997 tokens (SKILL.md is roughly 4k 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 Cohort Curve Model?
Skills that share tags, products or a category with Cohort Curve Model: Markitdown (ImCa0/just-laws, 781 stars), Data Table Manager (n8n-io/n8n, 207k stars), Docx4j (plutext/docx4j, 2.4k stars) and Instrument Data To Allotrope (aws-samples/amazon-bedrock-agents-healthcare-lifesciences, 274 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Cohort Curve Model?
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.