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apify-trend-analysis

Discover and track emerging trends across Google Trends, Instagram, Facebook, YouTube, and TikTok to inform content strategy.

46

Quality

48%

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

Fix and improve this skill with Tessl

tessl review fix ./skills/apify-trend-analysis/SKILL.md
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 skill provides actionable, executable commands for trend analysis across multiple platforms with a clear multi-step workflow. Its main weaknesses are the lack of validation checkpoints between steps (especially after schema fetch and before actor execution), the verbose Actor lookup table that could be externalized, and boilerplate sections that waste tokens. The error handling section is useful but presented as a flat list without recovery workflows.

Suggestions

Add a validation step after fetching the Actor schema (Step 2) to verify the response contains expected fields before proceeding, and add output verification after Step 4.

Move the large Actor ID lookup table to a separate reference file (e.g., ACTORS.md) and keep only a brief summary or top 5 most common actors inline.

Remove the generic 'When to Use' and 'Limitations' boilerplate sections — they add no skill-specific value and waste tokens.

Convert the error handling section into a structured troubleshooting flow with explicit recovery steps rather than a flat list.

DimensionReasoningScore

Conciseness

The large Actor table is useful reference but could be more compact. The boilerplate 'When to Use' and 'Limitations' sections add no value. The workflow is mostly efficient but has some padding (e.g., 'Based on character of use case' is vague filler).

2 / 3

Actionability

Provides fully executable bash commands for fetching schemas and running actors, with concrete examples for all three output formats. The mcpc command, run_actor.js invocations, and error handling are all copy-paste ready with clear placeholder 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 schema was fetched correctly, no validation of the JSON input before running, and no feedback loop for handling partial failures or verifying output quality.

2 / 3

Progressive Disclosure

References scripts at ${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_actor.js but no bundle files are provided to verify. The Actor table is a large inline block that could be a separate reference file. The skill has reasonable section structure but the monolithic Actor table hurts organization.

2 / 3

Total

9

/

12

Passed

Description

32%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 identifies a clear domain (social media trend tracking) and lists specific platforms, which is helpful for differentiation. However, it lacks a 'Use when...' clause, uses vague action verbs ('discover and track'), and misses common user trigger terms like 'trending', 'viral', or 'social media analytics'. The description would benefit significantly from explicit trigger guidance and more concrete capability descriptions.

Suggestions

Add an explicit 'Use when...' clause, e.g., 'Use when the user asks about trending topics, what's viral, social media trends, or wants to research popular content across platforms.'

Replace vague actions with specific capabilities, e.g., 'Analyzes trending keywords, compares search volume over time, identifies viral content patterns, and generates trend reports across Google Trends, Instagram, Facebook, YouTube, and TikTok.'

Include natural user trigger terms like 'what's trending', 'viral content', 'social media analytics', 'hashtag trends', 'popular topics', and 'trend analysis'.

DimensionReasoningScore

Specificity

Names the domain (trend tracking) and lists specific platforms (Google Trends, Instagram, Facebook, YouTube, TikTok), but the actions are vague — 'discover and track' and 'inform content strategy' don't describe concrete operations like 'generate trend reports', 'compare keyword volumes', or 'export analytics data'.

2 / 3

Completeness

Describes what the skill does (discover and track trends across platforms) 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' portion is also somewhat vague, warranting a score of 1.

1 / 3

Trigger Term Quality

Includes good platform-specific keywords (Google Trends, Instagram, Facebook, YouTube, TikTok) and relevant terms like 'trends' and 'content strategy', but misses common user phrasings like 'what's trending', 'viral', 'social media analytics', 'hashtag research', or 'trending topics'.

2 / 3

Distinctiveness Conflict Risk

The combination of multiple specific social platforms and 'emerging trends' provides some distinctiveness, but 'content strategy' is broad enough to overlap with general social media management or content planning skills.

2 / 3

Total

7

/

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

CriteriaDescriptionResult

frontmatter_unknown_keys

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

Warning

Total

10

/

11

Passed

Repository
popey/claude-code-skills
Reviewed

Table of Contents

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