apify-competitor-intelligence
Analyze competitor strategies, content, pricing, ads, and market positioning across Google Maps, Booking.com, Facebook, Instagram, YouTube, and TikTok.
65
57%
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-competitor-intelligence/SKILL.mdQuality
Discovery
50%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 excels at listing specific capabilities and platforms, creating a distinctive niche. However, it critically lacks any 'Use when...' guidance, which means Claude may not know when to select this skill from a large pool. Adding explicit trigger conditions and a few more natural user phrases would significantly improve its effectiveness.
Suggestions
Add a 'Use when...' clause such as 'Use when the user asks about competitor analysis, competitive research, market positioning, or wants to analyze rivals on platforms like Google Maps, Booking.com, or social media.'
Include natural trigger terms users would say, such as 'competitive analysis', 'competitor research', 'market intelligence', 'what are my competitors doing', or 'analyze the competition'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'analyze competitor strategies, content, pricing, ads, and market positioning' across named platforms. These are clear, actionable capabilities. | 3 / 3 |
Completeness | Describes what the skill does but completely lacks a 'Use when...' clause or any explicit trigger guidance for when Claude should select this skill. Per rubric guidelines, a missing 'Use when...' clause caps completeness at 2, and since the 'when' is entirely absent, this scores at 1. | 1 / 3 |
Trigger Term Quality | Includes good platform names (Google Maps, Booking.com, Facebook, Instagram, YouTube, TikTok) and domain terms (competitor, pricing, ads), but misses common user phrases like 'competitive analysis', 'competitor research', 'market research', or 'spy on competitors'. | 2 / 3 |
Distinctiveness Conflict Risk | The combination of competitor analysis with specific platforms (Google Maps, Booking.com, social media channels) creates a clear niche that is unlikely to conflict with other skills. The platform specificity makes it highly distinguishable. | 3 / 3 |
Total | 9 / 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.
The skill provides highly actionable, executable commands and a clear multi-step workflow for competitor intelligence gathering across multiple platforms. Its main weakness is the oversized Actor lookup table that should be extracted to a reference file, and the lack of validation/feedback loops between running the Actor and summarizing results. Error handling exists but is disconnected from the workflow steps.
Suggestions
Move the 30+ row Actor lookup table to a separate reference file (e.g., ACTORS.md) and keep only 3-5 common examples inline with a link to the full list.
Add a validation step between Step 4 and Step 5: check that the Actor run succeeded (e.g., verify output file exists and is non-empty) before summarizing, with a retry path if it failed.
Integrate error handling into the workflow steps rather than listing it separately at the end, so Claude knows what to check at each step.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The massive Actor lookup table (30+ rows) is verbose and could be condensed or moved to a reference file. The workflow steps and commands are reasonably efficient, but the sheer size of the table dominates the skill and much of the 'Best For' column is redundant with the 'User Need' column. | 2 / 3 |
Actionability | Provides fully executable bash commands with clear argument patterns, concrete examples for each output format, and specific Actor IDs. The mcpc command for fetching schemas is copy-paste ready with only the Actor ID needing substitution. | 3 / 3 |
Workflow Clarity | The 5-step workflow is clearly sequenced with a progress checklist, but lacks validation checkpoints. There's no step to verify the Actor run succeeded before summarizing, no feedback loop for retrying failed runs, and the error handling section is separate rather than integrated into the workflow. | 2 / 3 |
Progressive Disclosure | The Actor lookup table (30+ entries) should be in a separate reference file rather than inline. The skill references external scripts (`run_actor.js`) appropriately, but the monolithic table makes the main skill harder to scan. The structure would benefit from moving the table to a REFERENCE.md and keeping only a few common examples inline. | 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
b739683
- Reviewed
Table of Contents
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