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apify-brand-reputation-monitoring

Scrape reviews, ratings, and brand mentions from multiple platforms using Apify Actors.

57

Quality

66%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

Fix and improve this skill with Tessl

tessl review fix ./plugins/AI-Agents-Safe-Coding-Skills-claude/skills/apify-brand-reputation-monitoring/SKILL.md

The canonical home for this skill is apify-brand-reputation-monitoring in sickn33/antigravity-awesome-skills

SKILL.md
Quality
Evals
Security

Quality

Content

64%Scale 1-3

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

This is a well-structured skill with strong actionability—concrete commands, specific Actor IDs, and clear output format options. Its main weaknesses are the lack of validation/feedback loops in the workflow (e.g., checking if a run succeeded before proceeding to summarize) and the large inline Actor lookup table that could be better handled via progressive disclosure. The error handling section is useful but would be more effective if integrated as checkpoints within the workflow steps.

Suggestions

Add a validation checkpoint after Step 4 (e.g., check exit code or output for 'SUCCEEDED' status) with a retry/fix loop before proceeding to Step 5, to avoid summarizing failed runs.

Move the Actor lookup table to a separate reference file (e.g., ACTORS.md) and link to it from the main skill to improve progressive disclosure and reduce token footprint.

Integrate error handling into the workflow steps rather than listing them separately at the end—e.g., after the mcpc command, note what to do if it fails.

DimensionReasoningScore

Conciseness

The large Actor lookup table is useful reference material but makes the skill quite long. Some sections like Step 3 and Step 5 contain guidance Claude could infer. However, the Actor IDs are genuinely novel information Claude wouldn't know.

2 / 3

Actionability

Provides fully executable bash commands for each step, concrete Actor IDs, specific CLI flags, and copy-paste ready command templates for all three output formats. The mcpc command for fetching schemas is specific and complete.

3 / 3

Workflow Clarity

The workflow has clear sequential steps 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 table could be split into a separate reference file to keep the main skill lean. The skill references external scripts (`run_actor.js`) appropriately, but the inline content is heavy. The checklist and structure are good but the skill would benefit from moving the Actor catalog to a referenced file.

2 / 3

Total

9

/

12

Passed

Description

67%Scale 1-3

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 reasonably specific about what it does, naming concrete data types and the tool used. However, it lacks an explicit 'Use when...' clause, which is critical for Claude to know when to select this skill. The trigger terms could also be expanded to cover more natural user language variations.

Suggestions

Add a 'Use when...' clause such as 'Use when the user wants to scrape or collect reviews, ratings, feedback, or brand mentions from websites, or mentions Apify.'

Include common user-facing trigger terms and platform examples, e.g., 'Google reviews, Yelp ratings, Trustpilot, social media mentions, reputation monitoring, web scraping.'

DimensionReasoningScore

Specificity

Lists multiple specific concrete actions: 'scrape reviews, ratings, and brand mentions from multiple platforms using Apify Actors.' This names the domain (web scraping), specific data types (reviews, ratings, brand mentions), and the tool (Apify Actors).

3 / 3

Completeness

Clearly answers 'what does this do' (scrape reviews, ratings, brand mentions using Apify Actors), but lacks an explicit 'Use when...' clause or equivalent trigger guidance, which caps this at 2 per the rubric guidelines.

2 / 3

Trigger Term Quality

Includes some natural keywords like 'reviews', 'ratings', 'brand mentions', and 'scrape', but misses common variations users might say such as 'sentiment', 'feedback', 'monitor', 'reputation', or specific platform names. 'Apify Actors' is technical jargon that users may not naturally use.

2 / 3

Distinctiveness Conflict Risk

The combination of scraping reviews/ratings/brand mentions specifically via Apify Actors creates a clear niche that is unlikely to conflict with other skills. The mention of Apify Actors as the specific tooling makes it highly distinctive.

3 / 3

Total

10

/

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.

Validation10 / 11 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

10

/

11

Passed

Repository
administrakt0r/AI-Agents-Safe-Coding-Skills
Reviewed

Table of Contents

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