apify-lead-generation
Lead Generation
Overview
This public intake copy packages plugins/antigravity-awesome-skills-claude/skills/apify-lead-generation from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.
Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.
This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.
Lead Generation Scrape leads from multiple platforms using Apify Actors.
Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Prerequisites, Error Handling, Limitations.
When to Use This Skill
Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.
- You need business, creator, or contact leads from maps, search, social, or video platforms.
- The task involves selecting an Apify Actor to discover prospects and extract outreach data.
- You need exported lead data plus a concise summary of lead quality or segmentation.
- Use when the request clearly matches the imported source intent: Scrape leads from multiple platforms using Apify Actors.
- Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.
- Use when provenance needs to stay visible in the answer, PR, or review packet.
Operating Table
| Situation | Start here | Why it matters |
|---|---|---|
| First-time use | metadata.json |
Confirms repository, branch, commit, and imported path through the external_source block before touching the copied workflow |
| Provenance review | ORIGIN.md |
Gives reviewers a plain-language audit trail for the imported source |
| Workflow execution | reference/scripts/run_actor.js |
Starts with the smallest copied file that materially changes execution |
| Supporting context | reference/scripts/run_actor.js |
Adds the next most relevant copied source file without loading the entire package |
| Handoff decision | ## Related Skills |
Helps the operator switch to a stronger native skill when the task drifts |
Workflow
This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.
- Step 1: Determine lead source (select Actor)
- Step 2: Fetch Actor schema via mcpc
- Step 3: Ask user preferences (format, filename)
- Step 4: Run the lead finder script
- Step 5: Summarize results
- User Need - Actor ID - Best For
- Local businesses - compass/crawler-google-places - Restaurants, gyms, shops
Imported Workflow Notes
Imported: Workflow
Copy this checklist and track progress:
Task Progress:
- [ ] Step 1: Determine lead source (select Actor)
- [ ] Step 2: Fetch Actor schema via mcpc
- [ ] Step 3: Ask user preferences (format, filename)
- [ ] Step 4: Run the lead finder script
- [ ] Step 5: Summarize results
Step 1: Determine Lead Source
Select the appropriate Actor based on user needs:
| User Need | Actor ID | Best For |
|---|---|---|
| Local businesses | compass/crawler-google-places |
Restaurants, gyms, shops |
| Contact enrichment | vdrmota/contact-info-scraper |
Emails, phones from URLs |
| Instagram profiles | apify/instagram-profile-scraper |
Influencer discovery |
| Instagram posts/comments | apify/instagram-scraper |
Posts, comments, hashtags, places |
| Instagram search | apify/instagram-search-scraper |
Places, users, hashtags discovery |
| TikTok videos/hashtags | clockworks/tiktok-scraper |
Comprehensive TikTok data extraction |
| TikTok hashtags/profiles | clockworks/free-tiktok-scraper |
Free TikTok data extractor |
| TikTok user search | clockworks/tiktok-user-search-scraper |
Find users by keywords |
| TikTok profiles | clockworks/tiktok-profile-scraper |
Creator outreach |
| TikTok followers/following | clockworks/tiktok-followers-scraper |
Audience analysis, segmentation |
| Facebook pages | apify/facebook-pages-scraper |
Business contacts |
| Facebook page contacts | apify/facebook-page-contact-information |
Extract emails, phones, addresses |
| Facebook groups | apify/facebook-groups-scraper |
Buying intent signals |
| Facebook events | apify/facebook-events-scraper |
Event networking, partnerships |
| Google Search | apify/google-search-scraper |
Broad lead discovery |
| YouTube channels | streamers/youtube-scraper |
Creator partnerships |
| Google Maps emails | poidata/google-maps-email-extractor |
Direct email extraction |
Step 2: Fetch Actor Schema
Fetch the Actor's input schema and details dynamically using mcpc:
export $(grep APIFY_TOKEN .env | xargs) && mcpc --json mcp.apify.com --header "Authorization: Bearer $APIFY_TOKEN" tools-call fetch-actor-details actor:="ACTOR_ID" | jq -r ".content"
Replace ACTOR_ID with the selected Actor (e.g., compass/crawler-google-places).
This returns:
- Actor description and README
- Required and optional input parameters
- Output fields (if available)
Step 3: Ask User Preferences
Before running, ask:
- Output format:
- Quick answer - Display top few results in chat (no file saved)
- CSV - Full export with all fields
- JSON - Full export in JSON format
- Number of results: Based on character of use case
Step 4: Run the Script
Quick answer (display in chat, no file):
node --env-file=.env ${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_actor.js \
--actor "ACTOR_ID" \
--input 'JSON_INPUT'
CSV:
node --env-file=.env ${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_actor.js \
--actor "ACTOR_ID" \
--input 'JSON_INPUT' \
--output YYYY-MM-DD_OUTPUT_FILE.csv \
--format csv
JSON:
node --env-file=.env ${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_actor.js \
--actor "ACTOR_ID" \
--input 'JSON_INPUT' \
--output YYYY-MM-DD_OUTPUT_FILE.json \
--format json
Step 5: Summarize Results
After completion, report:
- Number of leads found
- File location and name
- Key fields available
- Suggested next steps (filtering, enrichment)
Imported: Prerequisites
(No need to check it upfront)
.envfile withAPIFY_TOKEN- Node.js 20.6+ (for native
--env-filesupport) mcpcCLI tool:npm install -g @apify/mcpc
Examples
Example 1: Ask for the upstream workflow directly
Use @apify-lead-generation to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.
Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.
Example 2: Ask for a provenance-grounded review
Review @apify-lead-generation against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.
Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.
Example 3: Narrow the copied support files before execution
Use @apify-lead-generation for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.
Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.
Example 4: Build a reviewer packet
Review @apify-lead-generation using the copied upstream files plus provenance, then summarize any gaps before merge.
Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.
Best Practices
Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.
- Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
- Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
- Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
- Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.
- Treat generated examples as scaffolding; adapt them to the concrete task before execution.
- Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant.
Troubleshooting
Problem: The operator skipped the imported context and answered too generically
Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills-claude/skills/apify-lead-generation, fails to mention provenance, or does not use any copied source files at all.
Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.
Problem: The imported workflow feels incomplete during review
Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task.
Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.
Problem: The task drifted into a different specialization
Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.
Related Skills
@00-andruia-consultant- Use when the work is better handled by that native specialization after this imported skill establishes context.@00-andruia-consultant-v2- Use when the work is better handled by that native specialization after this imported skill establishes context.@10-andruia-skill-smith- Use when the work is better handled by that native specialization after this imported skill establishes context.@10-andruia-skill-smith-v2- Use when the work is better handled by that native specialization after this imported skill establishes context.
Additional Resources
Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.
| Resource family | What it gives the reviewer | Example path |
|---|---|---|
references |
copied reference notes, guides, or background material from upstream | references/n/a |
examples |
worked examples or reusable prompts copied from upstream | examples/n/a |
scripts |
upstream helper scripts that change execution or validation | scripts/n/a |
agents |
routing or delegation notes that are genuinely part of the imported package | agents/n/a |
assets |
supporting assets or schemas copied from the source package | assets/n/a |
Imported Reference Notes
Imported: Error Handling
APIFY_TOKEN not found - Ask user to create .env with APIFY_TOKEN=your_token
mcpc not found - Ask user to install npm install -g @apify/mcpc
Actor not found - Check Actor ID spelling
Run FAILED - Ask user to check Apify console link in error output
Timeout - Reduce input size or increase --timeout
Imported: Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
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