Ab Testing
coreyhaines31/marketingskills
When the user wants to plan, design, or implement an A/B test or experiment, or build a growth experimentation program.
A skill your agent uses when designing or auditing ACM RecSys experiments centered on offline-versus-online evaluation — temporal splits, equal-budget baseline tuning, full-ranking versus sampled…
Install Recsys Experiments
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill recsys-experiments -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills recsys-experiments --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/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/RecSys-Skills/skills/recsys-experiments .claude/skills/recsys-experiments && 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 "recsys-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/RecSys-Skills/skills/recsys-experiments into .claude/skills/recsys-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "recsys-experiments", 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/brycewang-stanford/Awesome-Journal-Skills/tree/main/RecSys-Skills/skills/recsys-experimentsType 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 brycewang-stanford/Awesome-Journal-Skills --skill recsys-experiments -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills recsys-experiments --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/RecSys-Skills/skills/recsys-experiments .agents/skills/recsys-experiments && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "recsys-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/RecSys-Skills/skills/recsys-experiments into .agents/skills/recsys-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "recsys-experiments", 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 brycewang-stanford/Awesome-Journal-Skills --skill recsys-experiments -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills recsys-experiments --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/RecSys-Skills/skills/recsys-experiments .cursor/skills/recsys-experiments && 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 "recsys-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/RecSys-Skills/skills/recsys-experiments into .cursor/skills/recsys-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "recsys-experiments", 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/brycewang-stanford/Awesome-Journal-Skills.git --path RecSys-Skills/skills/recsys-experiments--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 brycewang-stanford/Awesome-Journal-Skills --skill recsys-experiments -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills recsys-experiments --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/RecSys-Skills/skills/recsys-experiments .gemini/skills/recsys-experiments && 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 "recsys-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/RecSys-Skills/skills/recsys-experiments into .gemini/skills/recsys-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "recsys-experiments", 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 brycewang-stanford/Awesome-Journal-Skills recsys-experimentsInstalls 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 brycewang-stanford/Awesome-Journal-Skills --skill recsys-experiments -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/RecSys-Skills/skills/recsys-experiments .github/skills/recsys-experiments && 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 "recsys-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/RecSys-Skills/skills/recsys-experiments into .github/skills/recsys-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "recsys-experiments", 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 brycewang-stanford/Awesome-Journal-Skills --skill recsys-experiments -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills recsys-experiments --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/RecSys-Skills/skills/recsys-experiments .opencode/skills/recsys-experiments && 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 "recsys-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/RecSys-Skills/skills/recsys-experiments into .opencode/skills/recsys-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "recsys-experiments", 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
recsys-experiments- GitHub stars
- 1.2k
- Token cost
- ~1.1k tokens
- SKILL.md length
- 428 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when designing or auditing ACM RecSys experiments centered on offline-versus-online evaluation — temporal splits, equal-budget baseline tuning, full-ranking versus sampled…
- Auditing ACM RecSys experiments centered on offline-versus-online evaluation — temporal splits
- SKILL.md covers Experiment audit, Offline vs online: the RecSys…, Claim-to-evidence design table and Vignette: an off-policy…, plus 2 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Equal-budget baseline tuning
What it does
Recsys Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing ACM RecSys experiments centered on offline-versus-online evaluation — temporal splits, equal-budget baseline tuning, full-ranking versus sampled metrics, off-policy estimators (IPS, SNIPS, doubly robust), A/B tests, exposure and popularity bias, seeds and variance, and matching each recommendation claim to its evidence.
Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Marketing & SEO, covering A/B testing. The repository describes itself as: Journal-specific Claude Code/Codex skill packs covering mainstream journals — AER, QJE, Nature, Cell, 管理世界, 经济研究 & 200+ more — your fast track to getting published. ｜ 覆盖主流期刊的… The licence is MIT.
When your agent uses it
- Auditing ACM RecSys experiments centered on offline-versus-online evaluation — temporal splits
- Equal-budget baseline tuning
- Full-ranking versus sampled metrics
- Off-policy estimators (IPS
Example prompts
- “/recsys-experiments”
What it can do on your machine
Read from SKILL.md and the folder at commit 932eb23. 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
Recsys Experiments loads about 1.1k tokens when it runs. Until then it costs about 93 tokens; SKILL.md has 428 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 brycewang-stanford/Awesome-Journal-Skills at commit 932eb23, republished under its MIT licence (© brycewang-stanford). 428 words, ~1,052 tokens.
Download SKILL.mdSave it as
.claude/skills/recsys-experiments/SKILL.md (or your agent's skills folder).- name
- recsys-experiments
- description
- Use when designing or auditing ACM RecSys experiments centered on offline-versus-online evaluation — temporal splits, equal-budget baseline tuning, full-ranking versus sampled metrics, off-policy estimators (IPS, SNIPS, doubly robust), A/B tests, exposure and popularity bias, seeds and variance, and matching each recommendation claim to its evidence.
RecSys Experiments
Use this before submission when the empirical story is not yet locked. At RecSys the experiment section is where papers are won or lost, because the community's reproducibility culture makes reviewers read evaluation choices as a proxy for whether the gains are real.
Experiment audit
- Map each recommendation claim to a table, figure, ablation, off-policy estimate, or A/B result.
- Tune every baseline under an equal search budget; an under-tuned baseline is the fastest route to a low score at this venue.
- Use a leakage-aware split: temporal or leave-one-last for sequential/session data, never a random split that lets future interactions into training.
- Rank over the full item catalog or state clearly that a sampled candidate set was used and why — sampled metrics can reorder methods.
- Report uncertainty: multiple seeds, mean ± sd, and a significance test on close results.
- Report the split, filtering, metrics and cutoffs, tuning grid and selection metric, seeds, hardware, and runtime.
- Add ablations that isolate the mechanism, not cosmetic variants.
Offline vs online: the RecSys distinctive
Offline metrics are cheap but only a proxy for deployed behavior. RecSys rewards papers that are honest about the gap and, where possible, bridge it.
| Evaluation mode | What it establishes | What it cannot establish |
|---|---|---|
| Offline top-N on logged data | Ranking quality against past behavior | That live users engage more |
| Off-policy estimate (IPS / SNIPS / DR) | Estimated online reward under exposure correction | Anything, if propensities are missing or positivity fails |
| Simulator / semi-synthetic | Behavior under a controlled, known reward | Real-world generalization |
| A/B test | Actual deployed effect | Reproducibility without the platform |
The strongest design triad: a tuned offline study, an off-policy or simulator bridge showing the offline gain tracks a deployment quantity, and — where available — an A/B result.
Show full SKILL.md (144 more words)Show less
Claim-to-evidence design table
| Recommendation claim | Matching experiment | Reject pattern avoided |
|---|---|---|
| "Ranks better than baselines" | Full-ranking metrics, equal-budget tuning, variance | "Beat only untuned defaults" |
| "Gain transfers to deployment" | Off-policy estimate or A/B result | "Offline nDCG assumed to imply engagement" |
| "Handles the exposure/popularity bias" | Debiased metric or propensity-corrected estimate | "Popularity bias reported but not corrected" |
| "Mechanism M drives the gain" | Ablation removing only M | "Improvement unattributed to any component" |
Vignette: an off-policy ranking study
Suppose the paper claims an exposure-corrected ranker improves engagement. The matching plan: a temporal split with full-ranking metrics and equal-budget baselines; a self-normalized IPS estimate of reward with the positivity assumption stated; a semi-synthetic simulator sweeping exposure strength to show the offline estimate and the known online reward move together; and an ablation removing the exposure correction to isolate it — every panel tied to a numbered claim.
Statistical reporting floor
- Seeds and replication count for every stochastic table; captions name what the bars are.
- Split protocol and metric cutoff stated once, in the body.
- Tuning grid + selection metric per system, symmetric across baselines.
- Compute actually consumed, not vague feasibility language.Output format
[Experiment readiness] strong / adequate / weak
[Claim -> evidence map] <claim: table / off-policy / A-B / simulator>
[Evaluation-validity risks] <baseline tuning / split leakage / sampled metrics>
[Offline-online bridge] present / missing / scoped-to-offline
[Decision-critical next run] <one experiment>© brycewang-stanford, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in RecSys-Skills/skills/recsys-experiments of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Recsys Experiments 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 |
|---|---|---|---|---|---|---|
| Recsys Experiments this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.1k | Automated safety check: Pass | MIT | |
| Ab Testingcoreyhaines31/marketingskills | 54k | 3 repos | ~3.1k | Automated safety check: Pass | MIT | |
| AnalyticsNexus-JPF/note-companion | 870 | 7 repos | ~2.2k | Automated safety check: Pass | MIT | |
| Ad Test Designeraaron-he-zhu/aaron-marketing-skills | 2.9k | 2 repos | ~2.8k | Automated safety check: Pass | Apache-2.0 | |
| Ab Test Analyzeririnabuht12-oss/marketing-skills | 4k | — | ~1.4k | Automated safety check: Pass | None | |
| Ab Test Store Listingappeeky/aso-skills | 2.2k | — | ~1.8k | Automated safety check: Pass | MIT |
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Categories
Questions about Recsys Experiments
What does Recsys Experiments do?
A skill your agent uses when designing or auditing ACM RecSys experiments centered on offline-versus-online evaluation — temporal splits, equal-budget baseline tuning, full-ranking versus sampled…. Recsys Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing ACM RecSys experiments centered on offline-versus-online evaluation — temporal splits, equal-budget baseline tuning, full-ranking versus sampled metrics, off-policy estimators (IPS, SNIPS, doubly robust), A/B tests, exposure and popularity bias, seeds and variance, and matching each recommendation claim to its evidence.
When should I use Recsys Experiments?
Recsys Experiments fits situations like: auditing ACM RecSys experiments centered on offline-versus-online evaluation — temporal splits; equal-budget baseline tuning; full-ranking versus sampled metrics; off-policy estimators (IPS.
How do I install Recsys Experiments in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill recsys-experiments -a claude-code`. Or copy the skill folder (RecSys-Skills/skills/recsys-experiments in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/recsys-experiments in your project. Claude Code loads it when a task matches its description.
How do I install Recsys Experiments in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill recsys-experiments -a codex`. Or copy the skill folder (RecSys-Skills/skills/recsys-experiments in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/recsys-experiments in your project. Codex loads it when a task matches its description.
Can I use Recsys Experiments 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 brycewang-stanford/Awesome-Journal-Skills --skill recsys-experiments -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/recsys-experiments, .gemini/skills/recsys-experiments, .github/skills/recsys-experiments and .opencode/skills/recsys-experiments in your project.
What does Recsys Experiments need to run?
SKILL.md names no scripts, command-line tools or credentials: Recsys Experiments is instructions for the agent only.
Does Recsys Experiments 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 Recsys Experiments 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 Recsys Experiments use?
Recsys Experiments 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 Recsys Experiments use?
About 1.1k tokens (SKILL.md is roughly 4.2k 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 Recsys Experiments?
Skills that share tags, products or a category with Recsys Experiments: Ab Testing (coreyhaines31/marketingskills, 54k stars), Analytics (Nexus-JPF/note-companion, 870 stars), Ad Test Designer (aaron-he-zhu/aaron-marketing-skills, 2.9k stars) and Ab Test Analyzer (irinabuht12-oss/marketing-skills, 4k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Recsys Experiments?
brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,228 GitHub stars. The repository holds 2,387 skills in this directory. The repository was last updated on September 27, 2026.
Source: brycewang-stanford/Awesome-Journal-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.