Ad Creative
LeoYeAI/openclaw-marketing-skills
When the user wants to generate, iterate, or scale ad creative — headlines, descriptions, primary text, or full ad variations — for any paid advertising platform.
Build CTR prediction models for estimating ad click-through rates from features.
Install Algo Ad Ctr
$ npx skills add asgard-ai-platform/skills --skill algo-ad-ctr -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install asgard-ai-platform/skills algo-ad-ctr --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/asgard-ai-platform/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/algo-ad-ctr .claude/skills/algo-ad-ctr && 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 "algo-ad-ctr" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-ad-ctr into .claude/skills/algo-ad-ctr/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-ad-ctr", 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/asgard-ai-platform/skills/tree/main/algo-ad-ctrType 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 asgard-ai-platform/skills --skill algo-ad-ctr -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install asgard-ai-platform/skills algo-ad-ctr --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/algo-ad-ctr .agents/skills/algo-ad-ctr && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "algo-ad-ctr" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-ad-ctr into .agents/skills/algo-ad-ctr/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-ad-ctr", 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 asgard-ai-platform/skills --skill algo-ad-ctr -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install asgard-ai-platform/skills algo-ad-ctr --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/algo-ad-ctr .cursor/skills/algo-ad-ctr && 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 "algo-ad-ctr" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-ad-ctr into .cursor/skills/algo-ad-ctr/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-ad-ctr", 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/asgard-ai-platform/skills.git --path algo-ad-ctr--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 asgard-ai-platform/skills --skill algo-ad-ctr -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install asgard-ai-platform/skills algo-ad-ctr --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/algo-ad-ctr .gemini/skills/algo-ad-ctr && 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 "algo-ad-ctr" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-ad-ctr into .gemini/skills/algo-ad-ctr/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-ad-ctr", 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 asgard-ai-platform/skills algo-ad-ctrInstalls 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 asgard-ai-platform/skills --skill algo-ad-ctr -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/algo-ad-ctr .github/skills/algo-ad-ctr && 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 "algo-ad-ctr" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-ad-ctr into .github/skills/algo-ad-ctr/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-ad-ctr", 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 asgard-ai-platform/skills --skill algo-ad-ctr -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install asgard-ai-platform/skills algo-ad-ctr --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/algo-ad-ctr .opencode/skills/algo-ad-ctr && 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 "algo-ad-ctr" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-ad-ctr into .opencode/skills/algo-ad-ctr/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-ad-ctr", 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
algo-ad-ctr- GitHub stars
- 242
- Token cost
- ~1.1k tokens
- SKILL.md length
- 427 words
- Files
- 4 (incl. references)
- Skills in repo
- 207
- Repo updated
- First seen
- Licence
- MIT
At a glance
Build CTR prediction models for estimating ad click-through rates from features.
- Works in 4 steps: Input Validation → Core Algorithm → Verification → …
- The user needs to predict click probability
- SKILL.md covers Overview, When to Use, Algorithm and Output Format, plus 3 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Algo Ad Ctr is an agent skill from asgard-ai-platform/skills. Build CTR prediction models for estimating ad click-through rates from features. Use this skill when the user needs to predict click probability, build an ad ranking model, or evaluate ad creative performance — even if they say 'predict click rate', 'ad relevance scoring', or 'which ad will get more clicks'.
Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including reference files (for example `examples/sample_scenario.md`, `references/feature-engineering.md` and `references/position-debiasing.md`).
It sits in Marketing & SEO, covering Paid advertising. The repository describes itself as: 301 open-source coding agent skills across 22 domains — methodology, judgment & gotchas packaged as Claude Agent Skills for the Asgard AI Platform. The licence is MIT.
When your agent uses it
- The user needs to predict click probability
- Build an ad ranking model
- Evaluate ad creative performance — even if they say predict click rate
- Ad relevance scoring
Example prompts
- “predict click rate”
- “ad relevance scoring”
- “which ad will get more clicks”
- “/algo-ad-ctr”
Workflow steps
4 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 4e7f4f8. 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 json).
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
Algo Ad Ctr loads about 1.1k tokens when it runs, and up to ~6.8k if it reads all its reference files. Until then it costs about 80 tokens; SKILL.md has 427 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 asgard-ai-platform/skills at commit 4e7f4f8, republished under its MIT licence (© asgard-ai-platform). 427 words, ~1,121 tokens.
Download SKILL.mdSave it as
.claude/skills/algo-ad-ctr/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.- name
- algo-ad-ctr
- description
- Build CTR prediction models for estimating ad click-through rates from features. Use this skill when the user needs to predict click probability, build an ad ranking model, or evaluate ad creative performance — even if they say 'predict click rate', 'ad relevance scoring', or 'which ad will get more clicks'.
- metadata.category
- WP-37 廣告演算法
- metadata.tags
- advertising, ctr-prediction, machine-learning, ranking
CTR Prediction Model
Overview
CTR prediction estimates the probability that a user clicks on an ad given context (user, query, ad, position). Forms the core of ad ranking: AdRank = Bid × pCTR. Typically uses logistic regression or gradient-boosted trees. Training on billions of impressions.
When to Use
Trigger conditions:
- Building or improving an ad ranking system
- Predicting click probability for bid optimization
- Evaluating ad creative effectiveness from feature analysis
When NOT to use:
- When predicting post-click conversions (use conversion rate model)
- When setting bid amounts (use bidding strategy skill)
Algorithm
IRON LAW: A CTR Model Must Be CALIBRATED
Predicting relative ranking is insufficient. The predicted probability
must MATCH actual click frequency (e.g., predicted 5% → 5 clicks per
100 impressions). Without calibration, bid optimization breaks:
Expected Value = Bid × pCTR × pConversion
If pCTR is off by 2x, bids are wrong by 2x.Phase 1: Input Validation
Collect impression logs with: user features, ad features, query features, position, click label (0/1). Handle class imbalance (CTR typically 1-5%). Gate: Sufficient volume (100K+ impressions), click labels verified, no data leakage from position.
Phase 2: Core Algorithm
- Feature engineering: user demographics, ad category, query-ad match, historical CTR, time/device features
- Train model: logistic regression (interpretable) or GBDT (higher accuracy)
- Calibrate predictions: Platt scaling or isotonic regression on holdout set
- Evaluate: log-loss (calibration) + AUC (ranking quality)
Phase 3: Verification
Check calibration: bucket predictions into deciles, compare predicted vs actual CTR per bucket. Plot reliability diagram. Gate: Calibration curve close to diagonal, AUC > 0.70.
Phase 4: Output
Return predicted CTR with confidence interval and top contributing features.
Output Format
{
"prediction": {"ctr": 0.035, "confidence_interval": [0.028, 0.042]},
"top_features": [{"feature": "query_ad_match", "importance": 0.32}],
"metadata": {"model": "gbdt", "auc": 0.78, "log_loss": 0.21, "calibration_error": 0.008}
}Examples
Sample I/O
Input: Trained logistic regression with 3 features and these coefficients:
intercept: -3.0
position_1: 0.8
query_ad_match: 1.5
user_is_mobile: 0.3Features for current request: position_1=1, query_ad_match=1, user_is_mobile=1
Expected: logit = -3.0 + 0.8 + 1.5 + 0.3 = -0.4 pCTR = sigmoid(-0.4) = 1/(1 + e^0.4) ≈ 0.401 → 40.1%
Verify: for features all 0 (baseline), pCTR = sigmoid(-3.0) ≈ 0.047 (4.7%). Calibration is checked by bucketing predictions and comparing to actual CTR in each bucket.
Show full SKILL.md (157 more words)Show less
Edge Cases
| Input | Expected | Why |
|---|---|---|
| New ad, no history | Use ad category average | Cold start for features |
| Position 1 vs position 4 | Different CTR, same relevance | Position bias inflates top-slot CTR |
| Very rare query | Low confidence | Insufficient training data for that query |
Gotchas
- Position bias: Ads in position 1 get more clicks regardless of relevance. Train on position-debiased data or include position as a feature and normalize at inference.
- Data freshness: CTR patterns change rapidly (seasonality, trends). Retrain daily or use online learning.
- Feature leakage: Including click-derived features (e.g., historical CTR of this exact ad-query pair) creates leakage if not handled carefully with time-based splits.
- Class imbalance: 97% no-click, 3% click. Use proper evaluation metrics (log-loss, AUC), not accuracy. Consider downsampling negatives during training.
- Multi-task learning: CTR and conversion rate are related but different. Joint models can improve both by sharing lower layers.
References
- For feature engineering best practices, see
references/feature-engineering.md - For position debiasing techniques, see
references/position-debiasing.md
© asgard-ai-platform, 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 algo-ad-ctr of asgard-ai-platform/skills.
- SKILL.md
- examples/sample_scenario.md
- references/feature-engineering.md
- references/position-debiasing.md
Open the folder on GitHubat commit 4e7f4f8
Compare with similar skills
Algo Ad Ctr 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 |
|---|---|---|---|---|---|---|
| Algo Ad Ctr this skillasgard-ai-platform/skills | 242 | — | ~1.1k | Automated safety check: Pass | MIT | |
| Ad CreativeLeoYeAI/openclaw-marketing-skills | 1k | 8 repos | ~3.4k | Automated safety check: Pass | Custom licence | |
| Blog GoogleAgriciDaniel/claude-blog | 2.3k | 1 repos | ~3.3k | Automated safety check: Notes | MIT | |
| Ad Account Auditoraaron-he-zhu/aaron-marketing-skills | 2.9k | 2 repos | ~2.2k | Automated safety check: Pass | Apache-2.0 | |
| Ad Creative Builderaaron-he-zhu/aaron-marketing-skills | 2.9k | 2 repos | ~2.2k | Automated safety check: Pass | Apache-2.0 | |
| Ad Test Designeraaron-he-zhu/aaron-marketing-skills | 2.9k | 2 repos | ~2.8k | Automated safety check: Pass | Apache-2.0 |
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Related
Categories
Questions about Algo Ad Ctr
What does Algo Ad Ctr do?
Build CTR prediction models for estimating ad click-through rates from features. Algo Ad Ctr is an agent skill from asgard-ai-platform/skills. Build CTR prediction models for estimating ad click-through rates from features.
When should I use Algo Ad Ctr?
Algo Ad Ctr fits situations like: the user needs to predict click probability; build an ad ranking model; evaluate ad creative performance — even if they say predict click rate; ad relevance scoring.
How do I install Algo Ad Ctr in Claude Code?
Run `npx skills add asgard-ai-platform/skills --skill algo-ad-ctr -a claude-code`. Or copy the skill folder (algo-ad-ctr in asgard-ai-platform/skills) into .claude/skills/algo-ad-ctr in your project. Claude Code loads it when a task matches its description.
How do I install Algo Ad Ctr in Codex?
Run `npx skills add asgard-ai-platform/skills --skill algo-ad-ctr -a codex`. Or copy the skill folder (algo-ad-ctr in asgard-ai-platform/skills) into .agents/skills/algo-ad-ctr in your project. Codex loads it when a task matches its description.
Can I use Algo Ad Ctr 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 asgard-ai-platform/skills --skill algo-ad-ctr -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/algo-ad-ctr, .gemini/skills/algo-ad-ctr, .github/skills/algo-ad-ctr and .opencode/skills/algo-ad-ctr in your project.
What does Algo Ad Ctr need to run?
SKILL.md names no scripts, command-line tools or credentials: Algo Ad Ctr is instructions for the agent only.
Does Algo Ad Ctr 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 Algo Ad Ctr 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 Algo Ad Ctr use?
Algo Ad Ctr 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 Algo Ad Ctr use?
About 1.1k tokens (SKILL.md is roughly 4.5k 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 5.7k tokens, read only when the agent opens those files.
What are the alternatives to Algo Ad Ctr?
Skills that share tags, products or a category with Algo Ad Ctr: Ad Creative (LeoYeAI/openclaw-marketing-skills, 1k stars), Blog Google (AgriciDaniel/claude-blog, 2.3k stars), Ad Account Auditor (aaron-he-zhu/aaron-marketing-skills, 2.9k stars) and Ad Creative Builder (aaron-he-zhu/aaron-marketing-skills, 2.9k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Algo Ad Ctr?
asgard-ai-platform (a GitHub organization) maintains it in asgard-ai-platform/skills, which has 242 GitHub stars. The repository holds 207 skills in this directory. The repository was last updated on June 6, 2026.
Source: asgard-ai-platform/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.