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apify-trend-analysis

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

55

Quality

63%

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 ./plugins/AI-Agents-Safe-Coding-Skills/skills/apify-trend-analysis/SKILL.md

The canonical home for this skill is apify-trend-analysis in sickn33/agentic-awesome-skills

SKILL.md
Quality
Evals
Security

Quality

Content

65%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 content is well-structured and actionable with concrete commands and a helpful actor table, but it lacks validation checkpoints for its batch operations and references a script that is absent from the bundle.

Suggestions

Add an explicit validation/verification checkpoint after Step 4 (e.g., confirm the run succeeded and the output file is non-empty before summarizing) to lift workflow clarity above the batch-operation cap.

Ship the referenced run_actor.js in scripts/ or remove the dangling reference so progressive disclosure resolves to real files.

Replace the generic 'When to Use' filler with concrete trigger guidance or remove it to tighten conciseness.

DimensionReasoningScore

Conciseness

The body is largely lean with a useful actor-selection table and concrete commands, but the generic 'When to Use' line ('Use this skill when tackling tasks related to its primary domain or functionality as described above') is filler that could be trimmed.

4 / 5

Actionability

Concrete, executable bash commands with real flags and Actor IDs cover the common quick/CSV/JSON cases, though placeholders (ACTOR_ID, JSON_INPUT) must be substituted and the referenced run_actor.js script is not present in the bundle.

4 / 5

Workflow Clarity

Steps 1-5 are clearly sequenced with a progress checklist and an error-handling section, but Apify actor runs are batch operations with no explicit validation/verification checkpoint, which caps workflow clarity at 3 per the rubric.

3 / 5

Progressive Disclosure

Sections are organized per step, but the body references ${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_actor.js while the scripts/ bundle directory is empty, so the navigation target is missing and references are not actually resolvable.

3 / 5

Total

14

/

20

Passed

Description

61%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 is specific and distinct, listing concrete platforms and actions, but it omits an explicit "Use when..." trigger clause, which caps completeness and limits its value as a trigger.

Suggestions

Add an explicit 'Use when...' clause listing natural trigger phrases (e.g., 'Use when researching trending topics, hashtags, or viral content across social platforms').

Broaden the action vocabulary beyond 'discover and track' to cover scraping, exporting, and summarizing trends.

Include common synonyms like 'social media', 'hashtags', and 'viral' to improve trigger term coverage.

DimensionReasoningScore

Specificity

"Discover and track emerging trends" names the domain with two concrete actions and enumerates five specific platforms, but the action set is not comprehensive (no scraping/export/analysis verbs).

3 / 5

Completeness

A clear "what" is present (discover/track trends across platforms), but there is no "Use when..." trigger clause, so per the rubric completeness is capped at 3.

3 / 5

Trigger Term Quality

Natural terms like "trends", "Google Trends", "Instagram", "Facebook", "YouTube", "TikTok", and "content strategy" are terms users would say, though common variations such as "social media", "hashtags", and "viral" are absent.

4 / 5

Distinctiveness Conflict Risk

Trend analysis across named social platforms is a distinct niche with only minor overlap risk against general social-media scraping skills.

4 / 5

Total

14

/

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

Table of Contents

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