Apify Trend Analysis

What it does

When your agent uses it

• Tasks that involve Web scraping
• Tasks that involve Forecasting and time series
• Tasks that involve Content strategy

Example prompts

• “/apify-trend-analysis”

Requirements

• Node.js
• A credential in APIFY_TOKEN

Workflow steps

5 steps, taken from the step headings in SKILL.md.

1. 1Identify Trend Type
2. 2Fetch Actor Schema
3. 3Ask User Preferences
4. 4Run the Script
5. 5Summarize Findings

What it can do on your machine

Read from SKILL.md and the folder at commit 2d14a69. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  Shell commands in SKILL.md call:

  • node
  • npm
  • jq

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  No URLs in SKILL.md. Its commands use npm, which can reach the network depending on how they are called.

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names these keys or tokens, usually read from environment variables:

  • APIFY_TOKEN

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Apify Trend Analysis loads about 1.2k tokens when it runs. Until then it costs about 37 tokens; SKILL.md has 396 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~37

When it runs · the whole SKILL.md, loaded when a task matches

~1.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

• NoteMentions a .env fileSKILL.md:15

  - `.env` file with `APIFY_TOKEN`

• NoteMentions a .env fileSKILL.md:63

  export $(grep APIFY_TOKEN .env | xargs) && mcpc --json mcp.apify.com --header "Authorization: Bearer $APIFY_TOKEN" tools

• NoteMentions a .env fileSKILL.md:86

  node --env-file=.env ${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_actor.js \

• NoteMentions a .env fileSKILL.md:93

  node --env-file=.env ${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_actor.js \

• NoteMentions a .env fileSKILL.md:102

  node --env-file=.env ${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_actor.js \

• NoteMentions a .env fileSKILL.md:120

  Y_TOKEN not found` - Ask user to create `.env` with `APIFY_TOKEN=your_token`

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from majiayu000/claude-skill-registry at commit 2d14a69, republished under its MIT licence (© majiayu000). 396 words, ~1,180 tokens.

name

apify-trend-analysis

description

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

risk

unknown

source

community

Task Progress:
- [ ] Step 1: Identify trend type (select Actor)
- [ ] Step 2: Fetch Actor schema via mcpc
- [ ] Step 3: Ask user preferences (format, filename)
- [ ] Step 4: Run the analysis script
- [ ] Step 5: Summarize findings

export $(grep APIFY_TOKEN .env | xargs) && mcpc --json mcp.apify.com --header "Authorization: Bearer $APIFY_TOKEN" tools-call fetch-actor-details actor:="ACTOR_ID" | jq -r ".content"

node --env-file=.env ${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_actor.js \
  --actor "ACTOR_ID" \
  --input 'JSON_INPUT'

node --env-file=.env ${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_actor.js \
  --actor "ACTOR_ID" \
  --input 'JSON_INPUT' \
  --output YYYY-MM-DD_OUTPUT_FILE.csv \
  --format csv

node --env-file=.env ${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_actor.js \
  --actor "ACTOR_ID" \
  --input 'JSON_INPUT' \
  --output YYYY-MM-DD_OUTPUT_FILE.json \
  --format json

© majiayu000, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in skills/bash/apify-trend-analysis of majiayu000/claude-skill-registry.

• SKILL.md
• metadata.json

Open the folder on GitHubat commit 2d14a69

Used in 3 other repositories

We found 16 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 3 other GitHub owners. This page covers the copy in majiayu000/claude-skill-registry, which our catalogue first saw on October 7, 2026.

• sickn33/agentic-awesome-skillsskills/apify-trend-analysisplugins/agentic-awesome-skills/skills/apify-trend-analysis+1 more
• lingxling/awesome-skills-cnantigravity-awesome-skills/skills/apify-trend-analysisantigravity-awesome-skills/plugins/agentic-awesome-skills/skills/apify-trend-analysis+1 more
• henryalouf/ruflow.agents/skills/apify-trend-analysis.agents/skills/antigravity-awesome-skills/skills/apify-trend-analysis+4 more