GitHub Project Contributor Finder API Skill
browser-act/skills
This skill helps users extract GitHub repository project details and contributor contact information using keywords, stars, and update dates.
For paid lead-gen and participant-recruitment ads, replaces vanity CPA with true CAC per qualified lead by joining ad-platform data with downstream funnel events, surfaces tracking gaps, and…
Install Ad Lead Quality Analyzer
$ npx skills add gooseworks-ai/goose-skills --skill ad-lead-quality-analyzer -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install gooseworks-ai/goose-skills ad-lead-quality-analyzer --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/gooseworks-ai/goose-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/ads/composites/ad-lead-quality-analyzer .claude/skills/ad-lead-quality-analyzer && 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 "ad-lead-quality-analyzer" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/ads/composites/ad-lead-quality-analyzer into .claude/skills/ad-lead-quality-analyzer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ad-lead-quality-analyzer", 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/gooseworks-ai/goose-skills/tree/main/skills/ads/composites/ad-lead-quality-analyzerType 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 gooseworks-ai/goose-skills --skill ad-lead-quality-analyzer -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install gooseworks-ai/goose-skills ad-lead-quality-analyzer --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/ads/composites/ad-lead-quality-analyzer .agents/skills/ad-lead-quality-analyzer && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "ad-lead-quality-analyzer" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/ads/composites/ad-lead-quality-analyzer into .agents/skills/ad-lead-quality-analyzer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ad-lead-quality-analyzer", 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 gooseworks-ai/goose-skills --skill ad-lead-quality-analyzer -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install gooseworks-ai/goose-skills ad-lead-quality-analyzer --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/ads/composites/ad-lead-quality-analyzer .cursor/skills/ad-lead-quality-analyzer && 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 "ad-lead-quality-analyzer" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/ads/composites/ad-lead-quality-analyzer into .cursor/skills/ad-lead-quality-analyzer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ad-lead-quality-analyzer", 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/gooseworks-ai/goose-skills.git --path skills/ads/composites/ad-lead-quality-analyzer--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 gooseworks-ai/goose-skills --skill ad-lead-quality-analyzer -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install gooseworks-ai/goose-skills ad-lead-quality-analyzer --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/ads/composites/ad-lead-quality-analyzer .gemini/skills/ad-lead-quality-analyzer && 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 "ad-lead-quality-analyzer" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/ads/composites/ad-lead-quality-analyzer into .gemini/skills/ad-lead-quality-analyzer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ad-lead-quality-analyzer", 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 gooseworks-ai/goose-skills ad-lead-quality-analyzerInstalls 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 gooseworks-ai/goose-skills --skill ad-lead-quality-analyzer -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/ads/composites/ad-lead-quality-analyzer .github/skills/ad-lead-quality-analyzer && 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 "ad-lead-quality-analyzer" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/ads/composites/ad-lead-quality-analyzer into .github/skills/ad-lead-quality-analyzer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ad-lead-quality-analyzer", 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 gooseworks-ai/goose-skills --skill ad-lead-quality-analyzer -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install gooseworks-ai/goose-skills ad-lead-quality-analyzer --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/ads/composites/ad-lead-quality-analyzer .opencode/skills/ad-lead-quality-analyzer && 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 "ad-lead-quality-analyzer" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/ads/composites/ad-lead-quality-analyzer into .opencode/skills/ad-lead-quality-analyzer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ad-lead-quality-analyzer", 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
ad-lead-quality-analyzer- GitHub stars
- 1.2k
- Used in
- 1 other repo
- Token cost
- ~2.7k tokens
- SKILL.md length
- 1,256 words
- Files
- 2
- Skills in repo
- 273
- Repo updated
- First seen
- Licence
- MIT
At a glance
For paid lead-gen and participant-recruitment ads, replaces vanity CPA with true CAC per qualified lead by joining ad-platform data with downstream funnel events, surfaces tracking gaps, and…
- Works in 7 steps: Discovery Interview → Tracking Validation (Gating Step) → Build the Per-Creative Funnel → …
- Tasks that involve Lead generation
- SKILL.md covers When to Use, Pipeline Pattern Assumptions…, Phase 0: Discovery Interview and Phase 1: Tracking Validation…, plus 9 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Ad Lead Quality Analyzer is an agent skill from gooseworks-ai/goose-skills. For paid lead-gen and participant-recruitment ads, replaces vanity CPA with true CAC per qualified lead by joining ad-platform data with downstream funnel events, surfaces tracking gaps, and classifies every creative into Scale / Keep / Investigate / Cut.
Its SKILL.md is about 2.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `skill.meta.json`).
It sits in Marketing & SEO, covering Lead generation and Recruiting and HR. The repository describes itself as: Library of Growth & GTM skills + data APIs for Claude Code, Codex, Cursor to run ads, social, content, lead gen, seo and data scraping. The licence is MIT.
When your agent uses it
- Tasks that involve Lead generation
- Tasks that involve Recruiting and HR
Example prompts
- “/ad-lead-quality-analyzer”
Workflow steps
7 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 c650c6d. 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
Ad Lead Quality Analyzer loads about 2.7k tokens when it runs. Until then it costs about 70 tokens; SKILL.md has 1,256 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 gooseworks-ai/goose-skills at commit c650c6d, republished under its MIT licence (© gooseworks-ai). 1,256 words, ~2,710 tokens.
Download SKILL.mdSave it as
.claude/skills/ad-lead-quality-analyzer/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.- name
- ad-lead-quality-analyzer
- description
- For paid lead-gen and participant-recruitment ads, replaces vanity CPA with true CAC per qualified lead by joining ad-platform data with downstream funnel events, surfaces tracking gaps, and classifies every creative into Scale / Keep / Investigate / Cut.
- tags
- ads
Ad Lead Quality Analyzer
Meta optimizes for whatever conversion event you fire. For lead-gen and participant-recruitment campaigns that's almost always "signup" — but a signup is worthless if the lead never qualifies, never completes the requested action, or never gets paid out. The lowest-CPA campaign is often the one bringing in the worst leads.
This skill joins what the ad platform knows (spend, signups) with what your own product knows (downstream funnel) and replaces vanity CPA with true CAC per qualified lead. It then classifies every creative into actionable buckets so you stop scaling the wrong winners.
Core principle: The ad platform's CPA is a half-truth. Real optimization needs both halves of the funnel — pre-signup (the platform has it) and post-signup (you have it). Until they're joined, you're flying blind.
When to Use
- "Which ads are bringing in real leads vs. junk?"
- "True CAC per qualified contributor / customer / participant"
- "Why is my lowest-CPA campaign performing worst downstream?"
- "Audit lead quality across creatives / audiences / placements"
- "Should I trust Meta's CPA when scaling?"
- "Find the creatives that look like winners but aren't"
Pipeline Pattern Assumptions (Read First)
This skill is opinionated about what to measure (true CAC per qualified lead, with cohort maturation, with vanity scoring) and agnostic about how the data is sourced.
It assumes one of three standard attribution patterns:
| Pattern | Setup | Join Key |
|---|---|---|
| A. UTM-only (most common) | UTM params captured on signup form, stored on lead/user record. Downstream events joined by user_id inside your DB. | utm_content (typically the ad ID) on both sides, or fbclid |
| B. UTM + CAPI send-back (best) | Same as A, plus your app fires Conversions API events back to Meta when downstream stages hit. Meta then optimizes for quality, not signups. | event_id / external_id |
| C. Meta Lead Ads + CRM sync | Meta-hosted lead form, lead_id syncs to CRM/DB, joined there. | lead_id |
If none of these patterns is wired up, the skill switches to tracking-gap mode — it produces a fix-the-tracking report instead of an analysis.
Phase 0: Discovery Interview
6 short questions. Don't proceed until each is answered (default = "I don't know — let's find out").
- Where do downstream events live? (Postgres / MySQL / Airtable / custom internal admin / spreadsheet / "no idea")
- Can the agent query that source directly? (DB credentials / API endpoint / CSV export / "needs a person to pull it")
- Does the signup form capture
utm_*params orfbclid? ("I don't know" → inspect the signup form's HTML / network requests) - Is the app sending CAPI events back to Meta for any downstream stage? (None / signup-only / signup + qualification / full funnel)
- What is a "qualified lead"? (Default: ≥1 unit of value-producing action completed within 14 days of signup. Examples: first purchase; demo attended; subscription activated; trial converted; first task completed and paid out)
- Cost basis per qualified lead? (Flat payout, variable, tiered by quality, or N/A — needed to compute margin)
Output of Phase 0: a one-paragraph Pipeline Brief stating the assumed pattern (A/B/C), the join key, the qualification definition, and any unknowns.
Phase 1: Tracking Validation (Gating Step)
Pull a sample of 10–20 recent signups from the downstream source. For each, check:
- Is
utm_source/utm_campaign/utm_contentpresent? (Orfbclid? Orlead_id?) - Does the join key resolve back to a specific Meta ad?
- Are there orphan signups (in your DB but no Meta join key)?
- Are there orphan Meta signups (in Meta but no matching DB record)?
Coverage thresholds:
| Coverage | Action |
|---|---|
| ≥80% joinable | Proceed to Phase 2 (analysis mode) |
| 50–80% joinable | Proceed with explicit confidence caveat on every finding |
| <50% joinable | Switch to tracking-gap mode. Skip Phases 2–6. Output the gap report. |
Output of Phase 1: a Data Quality Report with coverage %, sample of orphan records, and exact field-level findings.
Phase 2: Build the Per-Creative Funnel
For every ad / ad set / campaign with statistical volume (default ≥30 signups in the window), construct:
| Stage | Count | Conv. from prev. | What a drop here means |
|---|---|---|---|
| Impressions | n | — | — |
| Link Clicks | n | CTR | Hook / placement issue |
| Signups | n | Click → Signup | LP / form friction (use ad-to-landing-page-auditor) |
| Qualified action started | n | Signup → Started | Vanity signups — wrong promise in the ad |
| Qualified action approved | n | Started → Approved | Wrong audience or fraud |
| Payout / value event | n | Approved → Paid | The "real" conversion |
| Repeat action (configurable window) | n | Retention | One-and-done quality |
The skill should pull Meta-side data via the existing Meta Marketing API connection (MCP, native API, or pasted CSV) and downstream-side data via whichever source Phase 0 identified.
Show full SKILL.md (528 more words)Show less
Phase 3: Compute True CAC
Per creative / ad set / campaign:
- Platform CPA = spend ÷ signups (what Meta reports)
- True CAC = spend ÷ qualified leads (what actually matters)
- Quality Multiplier = True CAC ÷ Platform CPA (how badly the platform is misleading you per ad — higher = worse vanity problem)
- Margin per qualified lead = (cost-basis or LTV-equivalent value) − True CAC
Phase 4: Score and Classify Each Creative
Compute three quality scores per creative with sufficient volume:
- Vanity score = 1 − (Started ÷ Signups). High = clicks but no work
- Audience-fit score = Approved ÷ Started. Low = wrong people getting through
- Retention score = Repeat ÷ Approved. Low = one-and-done
Then classify into action buckets:
| Bucket | Rule | Action |
|---|---|---|
| Scale | Low True CAC + good quality + sufficient volume | Increase budget, watch for diminishing returns |
| Keep | Mid True CAC + acceptable quality | Hold |
| Investigate | High True CAC but high quality (often low volume) | Give it more budget before deciding |
| Cut | Low Platform CPA + high vanity score (the dangerous one — looks like a winner) | Pause and replace |
| Insufficient data | Below volume threshold | Wait, do not act |
Every classification cites the data and gets a confidence flag (sample size + CI on True CAC).
Phase 5: Cohort Maturation Handling
The biggest analysis trap: judging signups before they've had time to complete the funnel.
- Exclude signups newer than the qualification window (default 14 days) from "Cut" decisions
- Show two parallel views in the report:
- Mature cohort (≥14 days old) — the basis for action
- Recent cohort (<14 days) — leading indicator only
- If recent-cohort True CAC is diverging sharply from mature, flag a creative-fatigue or audience-shift hypothesis for investigation in
meta-ads-analyzer
Phase 6: Generate Report
Use this exact structure.
1. PIPELINE BRIEF
- Pattern (A/B/C), join key, qualification definition, unknowns
2. DATA QUALITY
- Coverage %, orphan counts, confidence level
3. HEADLINE
- Overall True CAC vs. Platform CPA
- Overall Quality Multiplier
- Period-over-period delta
4. PER-CREATIVE TABLE
- Ad ID | Spend | Signups | Qualified | Platform CPA | True CAC | Quality Mult. | Vanity | Class
5. ACTION LIST (prioritized)
- Cut (dangerous winners) → Scale (proven quality) → Investigate (low-vol promising) → Keep
- Each action: hypothesis + expected impact + rollback plan
6. AUDIENCE / PLACEMENT PATTERNS
- Which interests / lookalikes / geos / placements correlate with qualified leads
- Which correlate with vanity signups
7. TRACKING GAPS (if any from Phase 1)
- Specific fields, code locations, or events to wire upTracking-Gap Mode (Output if Phase 1 Fails)
If <50% of signups are joinable, the skill stops the analysis and outputs:
1. WHAT'S BROKEN
- Specific symptoms (e.g. "0 signups have utm_content; signup form's hidden fields are empty")
2. WHAT TO ADD
- Code-level recommendations (e.g. "preserve URL params on form submit and POST to /signup as utm_source, utm_campaign, utm_content, fbclid")
- Schema changes (e.g. "add columns to leads table: utm_source, utm_campaign, utm_content, fbclid, signup_timestamp")
- CAPI event setup (recommended, not required)
3. HOW TO VERIFY
- The 5-minute test: drop a tagged URL, complete signup, query DB, confirm fields populated
4. EXPECTED IMPACT
- "Once fixed, re-run this skill in `analysis` mode in N days when you have enough signups for statistical volume"Output Standards (Mandatory)
- Every recommendation is a hypothesis with expected impact and rollback, not a directive
- Never recommend cutting a creative purely on Platform CPA — that's the bug this skill exists to fix
- Always show True CAC alongside Platform CPA in any number reported back to the user
- Cohort-tag every figure as Mature, Recent, or Combined — never let the reader confuse them
- Flag confidence level on every per-creative recommendation (low / medium / high based on sample size + CI)
- Disambiguate "leads" — define "signup", "qualified", "paid" clearly in the Pipeline Brief and use them consistently
What This Skill Will Not Do
- Will not write to ad accounts — pure analysis. Action via Meta Ads Manager or whatever write tool the calling agent has available.
- Will not fix tracking for you — it tells you what's broken and how to fix it; the fix is a code change in your app.
- Will not generate creative or copy variants — use
messaging-ab-testerandad-angle-miner. - Will not diagnose Meta system mechanics (Breakdown Effect, Learning Phase) — pass the output to
meta-ads-analyzerfor that layer. - Will not compute true LTV — uses first-payout / first-value as proxy. Multi-touch LTV modeling is a different skill.
Related Skills
meta-ads-analyzer— Run after this skill to interpret why a creative's quality is low using Meta's system mechanicsad-campaign-analyzer— Use for cross-channel budget reallocation once true CAC is knownad-to-landing-page-auditor— Pair with this when "Click → Signup" drop-off is the leakmessaging-ab-tester— Use to generate replacement creatives for anything in the Cut bucket
© gooseworks-ai, 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 in skills/ads/composites/ad-lead-quality-analyzer of gooseworks-ai/goose-skills.
- SKILL.md
- skill.meta.json
Open the folder on GitHubat commit c650c6d
Used in 1 other repository
We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in gooseworks-ai/goose-skills, which our catalogue first saw on October 9, 2026.
Compare with similar skills
Ad Lead Quality Analyzer 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 |
|---|---|---|---|---|---|---|
| Ad Lead Quality Analyzer this skillgooseworks-ai/goose-skills | 1.2k | 1 repos | ~2.7k | Automated safety check: Pass | MIT | |
| GitHub Project Contributor Finder API Skillbrowser-act/skills | 6.1k | 1 repos | ~1.9k | Automated safety check: Pass | MIT | |
| Industry Key Contact Radar API Skillbrowser-act/skills | 6.1k | 1 repos | ~1.7k | Automated safety check: Pass | MIT | |
| Early Access Designeraaron-he-zhu/aaron-marketing-skills | 2.9k | — | ~3.4k | Automated safety check: Pass | Apache-2.0 | |
| 29 Xuat Khau B2bminhnv0807/ai-business-skills | 609 | — | ~2.9k | Automated safety check: Pass | MIT | |
| Tiktok Shop Affiliate Programnexscope-ai/eCommerce-Skills | 1.1k | — | ~2.4k | Automated safety check: Pass | MIT |
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Questions about Ad Lead Quality Analyzer
What does Ad Lead Quality Analyzer do?
For paid lead-gen and participant-recruitment ads, replaces vanity CPA with true CAC per qualified lead by joining ad-platform data with downstream funnel events, surfaces tracking gaps, and…. Ad Lead Quality Analyzer is an agent skill from gooseworks-ai/goose-skills. For paid lead-gen and participant-recruitment ads, replaces vanity CPA with true CAC per qualified lead by joining ad-platform data with downstream funnel events, surfaces tracking gaps, and classifies every creative into Scale / Keep / Investigate / Cut.
When should I use Ad Lead Quality Analyzer?
Ad Lead Quality Analyzer fits situations like: tasks that involve Lead generation; tasks that involve Recruiting and HR.
How do I install Ad Lead Quality Analyzer in Claude Code?
Run `npx skills add gooseworks-ai/goose-skills --skill ad-lead-quality-analyzer -a claude-code`. Or copy the skill folder (skills/ads/composites/ad-lead-quality-analyzer in gooseworks-ai/goose-skills) into .claude/skills/ad-lead-quality-analyzer in your project. Claude Code loads it when a task matches its description.
How do I install Ad Lead Quality Analyzer in Codex?
Run `npx skills add gooseworks-ai/goose-skills --skill ad-lead-quality-analyzer -a codex`. Or copy the skill folder (skills/ads/composites/ad-lead-quality-analyzer in gooseworks-ai/goose-skills) into .agents/skills/ad-lead-quality-analyzer in your project. Codex loads it when a task matches its description.
Can I use Ad Lead Quality Analyzer 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 gooseworks-ai/goose-skills --skill ad-lead-quality-analyzer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ad-lead-quality-analyzer, .gemini/skills/ad-lead-quality-analyzer, .github/skills/ad-lead-quality-analyzer and .opencode/skills/ad-lead-quality-analyzer in your project.
What does Ad Lead Quality Analyzer need to run?
SKILL.md names no scripts, command-line tools or credentials: Ad Lead Quality Analyzer is instructions for the agent only.
Does Ad Lead Quality Analyzer 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 Ad Lead Quality Analyzer 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 Ad Lead Quality Analyzer use?
Ad Lead Quality Analyzer 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 Ad Lead Quality Analyzer use?
About 2.7k tokens (SKILL.md is roughly 11k 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 Ad Lead Quality Analyzer?
Skills that share tags, products or a category with Ad Lead Quality Analyzer: GitHub Project Contributor Finder API Skill (browser-act/skills, 6.1k stars), Industry Key Contact Radar API Skill (browser-act/skills, 6.1k stars), Early Access Designer (aaron-he-zhu/aaron-marketing-skills, 2.9k stars) and 29 Xuat Khau B2b (minhnv0807/ai-business-skills, 609 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Ad Lead Quality Analyzer?
gooseworks-ai (a GitHub organization) maintains it in gooseworks-ai/goose-skills, which has 1,240 GitHub stars. The repository holds 273 skills in this directory. The repository was last updated on October 8, 2026.
Source: gooseworks-ai/goose-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.