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apify-brand-reputation-monitoring

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

58

Quality

67%

Does it follow best practices?

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Adds up to 20 points to the overall score

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SecuritybySnyk

Low

Low-risk findings worth noting

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tessl review fix ./skills/apify-brand-reputation-monitoring/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

68%Weight 40%Scale 1-5

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

The skill body is well-structured, actionable, and concise, giving Claude concrete commands and a clear actor-selection table. Its main weakness is the absence of an explicit validation checkpoint in a batch-scraping workflow, which caps workflow clarity.

Suggestions

Insert an explicit validation checkpoint between Step 4 (run) and Step 5 (summarize), e.g. verify the output row count and required fields before reporting, with a retry-on-failure loop for 'Run FAILED'.

Tighten vague phrasing such as 'Number of results: Based on character of use case' into a concrete default or prompt, and remove the redundant description line repeated under the H1.

Confirm the referenced `${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_actor.js` exists in the bundle and document its expected flags, since actionability depends on that external script.

DimensionReasoningScore

Conciseness

The body is efficient and avoids explaining concepts Claude already knows, but includes minor padding such as the repeated description line under the H1 and the vague 'Number of results: Based on character of use case'.

4 / 5

Actionability

Provides concrete, mostly copy-paste-ready commands (mcpc schema fetch, three run_actor.js variants with explicit flags) plus a specific Actor-ID table, with minor gaps from placeholders and reliance on an external unverified script.

4 / 5

Workflow Clarity

The five steps are clearly sequenced with a progress checklist, but this is a batch scraping operation with no explicit validation/verification checkpoint before summarizing results, which caps workflow clarity at 3 per the rubric.

3 / 5

Progressive Disclosure

Content is well-organized into sections with a clearly signaled one-level-deep reference to run_actor.js; the inline 20-row Actor catalog is reasonable but could optionally live in a separate reference file.

4 / 5

Total

15

/

20

Passed

Description

66%Weight 40%Scale 1-5

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 clearly conveys what the skill does and targets a distinct niche, but it omits any explicit 'Use when...' trigger guidance, which caps completeness. Adding a trigger clause with natural synonyms like 'reputation' or 'sentiment' would lift both completeness and trigger term quality.

Suggestions

Append a 'Use when...' clause naming concrete trigger phrases, e.g. 'Use when the user wants to monitor brand reputation, sentiment, or reviews across platforms like Google Maps, Booking, TripAdvisor, or social media.'

Add natural synonyms ('reputation', 'sentiment') and a couple of platform names users would actually mention to broaden trigger term coverage.

Reflect the export/summary actions from the body (CSV/JSON export, summary of signals) in the description to improve specificity and completeness.

DimensionReasoningScore

Specificity

Names several concrete data targets — 'reviews, ratings, and brand mentions' scraped 'from multiple platforms using Apify Actors' — with only minor coverage gaps (no mention of export or summarization).

4 / 5

Completeness

The 'what' is clearly stated (scrape reviews/ratings/mentions via Apify Actors) but there is no 'Use when...' or equivalent explicit trigger guidance, capping completeness at 3 per the rubric.

3 / 5

Trigger Term Quality

Includes natural terms users would say ('reviews', 'ratings', 'brand mentions') with good coverage, though common synonyms like 'sentiment' and 'reputation' and platform names are absent.

4 / 5

Distinctiveness Conflict Risk

The combination of 'Apify Actors' with 'brand mentions' across 'multiple platforms' carves a mostly distinct niche, with only minor overlap risk against other Apify scraping skills.

4 / 5

Total

15

/

20

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

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

Warning

Total

15

/

16

Passed

Repository
sickn33/agentic-awesome-skills
Reviewed

Table of Contents

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