apify-lead-generation
Scrape leads from multiple platforms using Apify Actors.
57
48%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Advisory
Suggest reviewing before use
Optimize this skill with Tessl
npx tessl skill review --optimize ./skills/apify-lead-generation/SKILL.mdQuality
Discovery
32%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
The description is brief and identifies the core domain (lead scraping via Apify) but lacks the depth needed for reliable skill selection. It is missing a 'Use when...' clause, specific platform names, concrete action details, and natural trigger terms users would employ when requesting this capability.
Suggestions
Add an explicit 'Use when...' clause with trigger terms like 'scrape leads', 'lead generation', 'Apify', 'prospect list', 'contact extraction', or specific platform names (e.g., LinkedIn, Google Maps).
List specific concrete actions such as 'extract contact information, export lead lists to CSV, configure Apify Actors for targeted scraping'.
Name the specific platforms supported (e.g., LinkedIn, Google Maps, Yellow Pages) to improve both trigger term quality and distinctiveness.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (lead scraping) and a key tool (Apify Actors), but doesn't list specific concrete actions like extracting contact info, filtering leads, exporting to CSV, etc. 'Scrape leads from multiple platforms' is a single action rather than multiple specific capabilities. | 2 / 3 |
Completeness | Describes what it does (scrape leads using Apify Actors) but completely lacks a 'Use when...' clause or any explicit trigger guidance for when Claude should select this skill. Per the rubric, a missing 'Use when...' clause caps completeness at 2, and the 'what' is also fairly thin, warranting a 1. | 1 / 3 |
Trigger Term Quality | Includes some relevant keywords like 'scrape', 'leads', 'Apify', and 'platforms', but misses common user variations such as 'lead generation', 'prospect list', 'contact scraping', 'LinkedIn', 'web scraping', or specific platform names users might mention. | 2 / 3 |
Distinctiveness Conflict Risk | The mention of 'Apify Actors' provides some distinctiveness, and 'leads' narrows the scraping domain. However, it could overlap with general web scraping skills or other lead generation tools since 'multiple platforms' is vague. | 2 / 3 |
Total | 7 / 12 Passed |
Implementation
64%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a solid, actionable skill with a well-structured workflow and comprehensive actor selection table. Its main weaknesses are the lack of validation/feedback loops after running actors (important for batch scraping operations) and some generic boilerplate in the limitations section. The large inline actor table could benefit from being extracted to a reference file.
Suggestions
Add a validation step after Step 4 (e.g., check result count, verify expected fields are present, handle empty results) to create a feedback loop for the scraping operation.
Move the actor selection table to a separate reference file (e.g., ACTORS.md) and link to it from the main skill to improve progressive disclosure.
Remove the generic limitations section boilerplate ('Do not treat the output as a substitute for environment-specific validation...') which adds no skill-specific value.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The actor table is comprehensive and useful, but the limitations section contains generic boilerplate ('Do not treat the output as a substitute for environment-specific validation...') that adds no value. The workflow checklist is somewhat redundant given the step headers. Overall mostly efficient but could be tightened. | 2 / 3 |
Actionability | Provides fully executable bash commands for every step, a clear actor selection table with specific Actor IDs, concrete command patterns for different output formats, and specific error messages with resolutions. Copy-paste ready with clear placeholder substitution. | 3 / 3 |
Workflow Clarity | Steps are clearly sequenced with a progress checklist, but there are no validation checkpoints between running the script and summarizing results. No feedback loop for handling partial results, verifying data quality, or retrying failed runs beyond checking the Apify console. For a batch scraping operation, this lack of validation caps the score. | 2 / 3 |
Progressive Disclosure | References external scripts (run_actor.js) and the mcpc tool appropriately, but the large actor table could be split into a separate reference file. The skill is moderately long and inline-heavy. No clear links to deeper documentation for individual actors or advanced configuration. | 2 / 3 |
Total | 9 / 12 Passed |
Validation
90%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 10 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 10 / 11 Passed | |
- Repository
- sickn33/antigravity-awesome-skills
- Commit
b938655
- Reviewed
Table of Contents
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