Answer Ads Questions
gooseworks-ai/goose-skills
Answer questions about a brand's Meta ads (spend, results, which ad is winning, is it paused, why did Goose do that) only from reads made in the same turn, with the data window and sync time on…
Answers a business question with data without fooling yourself — framing the question so an answer would change something, choosing the right comparison, checking the data before trusting it…
MITAuto-check passed
Install Quantitative Analysis
$ npx skills add cbrock84/headcount --skill quantitative-analysis -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install cbrock84/headcount quantitative-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/cbrock84/headcount.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/data-analytics/skills/quantitative-analysis .claude/skills/quantitative-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 "quantitative-analysis" agent skill from https://github.com/cbrock84/headcount/tree/main/plugins/data-analytics/skills/quantitative-analysis into .claude/skills/quantitative-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "quantitative-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/cbrock84/headcount/tree/main/plugins/data-analytics/skills/quantitative-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 cbrock84/headcount --skill quantitative-analysis -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install cbrock84/headcount quantitative-analysis --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/cbrock84/headcount.git skills-src && mkdir -p .agents/skills && cp -r skills-src/plugins/data-analytics/skills/quantitative-analysis .agents/skills/quantitative-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 "quantitative-analysis" agent skill from https://github.com/cbrock84/headcount/tree/main/plugins/data-analytics/skills/quantitative-analysis into .agents/skills/quantitative-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "quantitative-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 cbrock84/headcount --skill quantitative-analysis -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install cbrock84/headcount quantitative-analysis --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/cbrock84/headcount.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/plugins/data-analytics/skills/quantitative-analysis .cursor/skills/quantitative-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 "quantitative-analysis" agent skill from https://github.com/cbrock84/headcount/tree/main/plugins/data-analytics/skills/quantitative-analysis into .cursor/skills/quantitative-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "quantitative-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/cbrock84/headcount.git --path plugins/data-analytics/skills/quantitative-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 cbrock84/headcount --skill quantitative-analysis -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install cbrock84/headcount quantitative-analysis --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/cbrock84/headcount.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/plugins/data-analytics/skills/quantitative-analysis .gemini/skills/quantitative-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 "quantitative-analysis" agent skill from https://github.com/cbrock84/headcount/tree/main/plugins/data-analytics/skills/quantitative-analysis into .gemini/skills/quantitative-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "quantitative-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 cbrock84/headcount quantitative-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 cbrock84/headcount --skill quantitative-analysis -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/cbrock84/headcount.git skills-src && mkdir -p .github/skills && cp -r skills-src/plugins/data-analytics/skills/quantitative-analysis .github/skills/quantitative-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 "quantitative-analysis" agent skill from https://github.com/cbrock84/headcount/tree/main/plugins/data-analytics/skills/quantitative-analysis into .github/skills/quantitative-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "quantitative-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 cbrock84/headcount --skill quantitative-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 cbrock84/headcount quantitative-analysis --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/cbrock84/headcount.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/plugins/data-analytics/skills/quantitative-analysis .opencode/skills/quantitative-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 "quantitative-analysis" agent skill from https://github.com/cbrock84/headcount/tree/main/plugins/data-analytics/skills/quantitative-analysis into .opencode/skills/quantitative-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "quantitative-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
quantitative-analysis- GitHub stars
- 2k
- Token cost
- ~1.3k tokens
- SKILL.md length
- 711 words
- Files
- 2 (incl. references)
- Skills in repo
- 175
- Repo updated
- First seen
- Licence
- MIT
At a glance
Answers a business question with data without fooling yourself — framing the question so an answer would change something, choosing the right comparison, checking the data before trusting it…
- SKILL.md covers Frame the question so that an…, Choose the comparison before…, Interrogate the data before… and Know the traps that produce…, plus 4 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Quantitative Analysis is an agent skill from cbrock84/headcount. Answers a business question with data without fooling yourself — framing the question so an answer would change something, choosing the right comparison, checking the data before trusting it, recognizing the traps that produce confident wrong answers (aggregation reversals, survivorship, regression to the mean, multiple comparisons), and reporting uncertainty honestly. Use this to run an analysis, review one before acting on it, or work out why two people looking at the same data reached opposite conclusions.
Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/sources.md`).
The repository describes itself as: An agent organization structured as a company — 15+ departments, 125+ skills, each independently installable, citing the standards and regulators that settle the question. Runs… The licence is MIT.
Example prompts
- “Use the quantitative-analysis skill to answer a business question with data without fooling yourself — framing the question so an answer would…”
- “/quantitative-analysis”
What it can do on your machine
Read from SKILL.md and the folder at commit 98d1c17. 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
Quantitative Analysis loads about 1.3k tokens when it runs, and up to ~1.7k if it reads all its reference files. Until then it costs about 134 tokens; SKILL.md has 711 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 cbrock84/headcount at commit 98d1c17, republished under its MIT licence (© cbrock84). 711 words, ~1,253 tokens.
Download SKILL.mdSave it as
.claude/skills/quantitative-analysis/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.- name
- quantitative-analysis
- description
- Answers a business question with data without fooling yourself — framing the question so an answer would change something, choosing the right comparison, checking the data before trusting it, recognizing the traps that produce confident wrong answers (aggregation reversals, survivorship, regression to the mean, multiple comparisons), and reporting uncertainty honestly. Use this to run an analysis, review one before acting on it, or work out why two people looking at the same data reached opposite conclusions.
Quantitative analysis
A wrong answer here is rarely an arithmetic error. It is a correct calculation on the wrong comparison, or on data that does not mean what the field name suggests.
Frame the question so that an answer changes something
Start from the decision. "How is retention doing" has no answer; "is the cohort we changed onboarding for retaining better than the one before it, enough to justify rolling it out" does.
Write down what you expect to find and what you would do in each case before you look. If every possible result leads to the same action, the analysis is not worth running — and knowing that in advance is worth more than the analysis would have been.
Choose the comparison before the metric
Almost every meaningful number is a comparison, and the choice of what to compare against does more work than the calculation.
- Against what it was — needs a period long enough to see through seasonality and noise.
- Against what it would have been — the strongest comparison and the hardest to construct. A holdout group, a matched segment, a pre-trend extended forward.
- Against a peer or a benchmark — only useful if the definitions genuinely match, which they usually do not.
Name the counterfactual explicitly. "Revenue rose after the campaign" is a comparison against nothing, and it is the single most common way credit is claimed for a trend that was already happening.
Interrogate the data before you trust it
Look at the raw rows. Check when collection started and whether the definition changed partway. Check null rates, duplicates, and test or internal accounts still in the set. Check whether recent periods are still filling in — partial data at the tail is what produces the "sudden decline" that resolves itself a week later.
A field's name is not its definition. Find out what actually writes it and under what conditions, especially for anything named status, type, active, or created.
Know the traps that produce confident wrong answers
- Aggregation reversals. A rate can improve in every segment and worsen overall if the mix shifted. Always check whether the segments agree with the total, and where they disagree, the segments are the truth.
- Survivorship. Analyzing only accounts still present answers a question about survivors. The ones that left are usually the ones the question was about.
- Regression to the mean. Anything selected for being extreme moves back toward average on its own. Interventions aimed at the worst performers get credited with this routinely.
- Multiple comparisons. Test twenty segments at the usual threshold and one will look significant by chance. Decide what you are testing before you slice.
- Denominator drift. A ratio moves when either half moves. Show both.
- Correlation with an obvious common cause. Two things driven by the same seasonality will track each other beautifully and explain nothing.
Show full SKILL.md (241 more words)Show less
Segment before concluding, and stop before you overfit
Blended numbers hide the finding almost every time — one segment moving hard while the rest sit still. Split by the two or three dimensions that plausibly matter and check whether the effect is general or local.
Then stop. Slicing until something looks interesting finds noise, reliably, and the result will not replicate.
Report the uncertainty rather than burying it
Give the estimate, the range around it, and what would change the answer. State the sample size and the period. Say plainly what the analysis cannot determine — an analysis honest about its limits gets trusted on the things it can determine.
Distinguish what the data shows from what you infer. Both belong in the report; conflating them is how a plausible interpretation becomes a fact by the third time it is repeated.
Sources
references/sources.md in this skill lists the outside authorities that settle the questions
here — what each one is authoritative for, and what you may do with it. Check them before
answering on anything they cover, and cite what you used. Most are free to read and not free
to reproduce; the use note on each is binding.
Never
- Run an analysis that leads to the same action whatever it finds.
- Report a change without naming what it is being compared against.
- Conclude from a total when the segments disagree with it.
- Slice until something is significant and report the slice that was.
© cbrock84, 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 (references) in plugins/data-analytics/skills/quantitative-analysis of cbrock84/headcount.
- SKILL.md
- references/sources.md
Open the folder on GitHubat commit 98d1c17
Compare with similar skills
Quantitative 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 |
|---|---|---|---|---|---|---|
| Quantitative Analysis this skillcbrock84/headcount | 2k | — | ~1.3k | Automated safety check: Pass | MIT | |
| Answer Ads Questionsgooseworks-ai/goose-skills | 1.2k | — | ~3.1k | Automated safety check: Pass | MIT | |
| Frame Titlethedaviddias/Front-End-Checklist | 74k | — | ~438 | Automated safety check: Pass | MIT | |
| Logseq Answer Machinelogseq/logseq | 45k | — | ~1.2k | Automated safety check: Warn | AGPL-3.0 | |
| Question Framingai-analyst-lab/ai-analyst | 304 | — | ~3k | Automated safety check: Pass | MIT | |
| Frame Logo Outronexu-io/open-design | 100k | — | ~777 | Automated safety check: Pass | Apache-2.0 |
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Questions about Quantitative Analysis
What does Quantitative Analysis do?
Answers a business question with data without fooling yourself — framing the question so an answer would change something, choosing the right comparison, checking the data before trusting it…. Quantitative Analysis is an agent skill from cbrock84/headcount. Answers a business question with data without fooling yourself — framing the question so an answer would change something, choosing the right comparison, checking the data before trusting it, recognizing the traps that produce confident wrong answers (aggregation reversals, survivorship, regression to the mean, multiple comparisons), and reporting uncertainty honestly.
How do I install Quantitative Analysis in Claude Code?
Run `npx skills add cbrock84/headcount --skill quantitative-analysis -a claude-code`. Or copy the skill folder (plugins/data-analytics/skills/quantitative-analysis in cbrock84/headcount) into .claude/skills/quantitative-analysis in your project. Claude Code loads it when a task matches its description.
How do I install Quantitative Analysis in Codex?
Run `npx skills add cbrock84/headcount --skill quantitative-analysis -a codex`. Or copy the skill folder (plugins/data-analytics/skills/quantitative-analysis in cbrock84/headcount) into .agents/skills/quantitative-analysis in your project. Codex loads it when a task matches its description.
Can I use Quantitative 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 cbrock84/headcount --skill quantitative-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/quantitative-analysis, .gemini/skills/quantitative-analysis, .github/skills/quantitative-analysis and .opencode/skills/quantitative-analysis in your project.
What does Quantitative Analysis need to run?
SKILL.md names no scripts, command-line tools or credentials: Quantitative Analysis is instructions for the agent only.
Does Quantitative 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 Quantitative 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 Quantitative Analysis use?
Quantitative 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 Quantitative Analysis use?
About 1.3k tokens (SKILL.md is roughly 5k 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 414 tokens, read only when the agent opens those files.
What are the alternatives to Quantitative Analysis?
Skills that share tags, products or a category with Quantitative Analysis: Answer Ads Questions (gooseworks-ai/goose-skills, 1.2k stars), Frame Title (thedaviddias/Front-End-Checklist, 74k stars), Logseq Answer Machine (logseq/logseq, 45k stars) and Question Framing (ai-analyst-lab/ai-analyst, 304 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Quantitative Analysis?
cbrock84 (a GitHub user) maintains it in cbrock84/headcount, which has 2,007 GitHub stars. The repository holds 175 skills in this directory. The repository was last updated on September 17, 2026.
Source: cbrock84/headcount on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.