[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-cdd72a36-5e14-4592-83db-395ebb4b0050":3,"$fwcFhvytigDRchD_w1ErOp4TG9Qi1XRamzioEnv6VbEQ":42},{"id":4,"title":5,"description":6,"categoryId":7,"moduleId":8,"tags":9,"prompt":10,"icon":11,"source":12,"sourceUrl":13,"authorId":14,"authorName":15,"isPublic":16,"stars":17,"runs":18,"createdAt":19,"updatedAt":19,"module":20,"category":27,"packages":33},"cdd72a36-5e14-4592-83db-395ebb4b0050","apify-content-analytics","跟踪参与度指标，衡量活动投资回报率，并分析Instagram、Facebook、YouTube和TikTok上的内容表现。","cat_coding_backend","mod_coding","sickn33,coding","---\nname: apify-content-analytics\ndescription: Track engagement metrics, measure campaign ROI, and analyze content performance across Instagram, Facebook, YouTube, and TikTok.\nrisk: unknown\nsource: community\n---\n\n# Content Analytics\n\nTrack and analyze content performance using Apify Actors to extract engagement metrics from multiple platforms.\n\n## When to Use\n- You need engagement, growth, or ROI metrics for posts, reels, videos, ads, or hashtags.\n- The task is to use Apify Actors to collect cross-platform content performance data.\n- You need exported analytics results and a concise interpretation of what content is performing best.\n\n## Prerequisites\n(No need to check it upfront)\n\n- `.env` file with `APIFY_TOKEN`\n- Node.js 20.6+ (for native `--env-file` support)\n- `mcpc` CLI tool: `npm install -g @apify\u002Fmcpc`\n\n## Workflow\n\nCopy this checklist and track progress:\n\n```\nTask Progress:\n- [ ] Step 1: Identify content analytics type (select Actor)\n- [ ] Step 2: Fetch Actor schema via mcpc\n- [ ] Step 3: Ask user preferences (format, filename)\n- [ ] Step 4: Run the analytics script\n- [ ] Step 5: Summarize findings\n```\n\n### Step 1: Identify Content Analytics Type\n\nSelect the appropriate Actor based on analytics needs:\n\n| User Need | Actor ID | Best For |\n|-----------|----------|----------|\n| Post engagement metrics | `apify\u002Finstagram-post-scraper` | Post performance |\n| Reel performance | `apify\u002Finstagram-reel-scraper` | Reel analytics |\n| Follower growth tracking | `apify\u002Finstagram-followers-count-scraper` | Growth metrics |\n| Comment engagement | `apify\u002Finstagram-comment-scraper` | Comment analysis |\n| Hashtag performance | `apify\u002Finstagram-hashtag-scraper` | Branded hashtags |\n| Mention tracking | `apify\u002Finstagram-tagged-scraper` | Tag tracking |\n| Comprehensive metrics | `apify\u002Finstagram-scraper` | Full data |\n| API-based analytics | `apify\u002Finstagram-api-scraper` | API access |\n| Facebook post performance | `apify\u002Ffacebook-posts-scraper` | Post metrics |\n| Reaction analysis | `apify\u002Ffacebook-likes-scraper` | Engagement types |\n| Facebook Reels metrics | `apify\u002Ffacebook-reels-scraper` | Reels performance |\n| Ad performance tracking | `apify\u002Ffacebook-ads-scraper` | Ad analytics |\n| Facebook comment analysis | `apify\u002Ffacebook-comments-scraper` | Comment engagement |\n| Page performance audit | `apify\u002Ffacebook-pages-scraper` | Page metrics |\n| YouTube video metrics | `streamers\u002Fyoutube-scraper` | Video performance |\n| YouTube Shorts analytics | `streamers\u002Fyoutube-shorts-scraper` | Shorts performance |\n| TikTok content metrics | `clockworks\u002Ftiktok-scraper` | TikTok analytics |\n\n### Step 2: Fetch Actor Schema\n\nFetch the Actor's input schema and details dynamically using mcpc:\n\n```bash\nexport $(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\"\n```\n\nReplace `ACTOR_ID` with the selected Actor (e.g., `apify\u002Finstagram-post-scraper`).\n\nThis returns:\n- Actor description and README\n- Required and optional input parameters\n- Output fields (if available)\n\n### Step 3: Ask User Preferences\n\nBefore running, ask:\n1. **Output format**:\n   - **Quick answer** - Display top few results in chat (no file saved)\n   - **CSV** - Full export with all fields\n   - **JSON** - Full export in JSON format\n2. **Number of results**: Based on character of use case\n\n### Step 4: Run the Script\n\n**Quick answer (display in chat, no file):**\n```bash\nnode --env-file=.env ${CLAUDE_PLUGIN_ROOT}\u002Freference\u002Fscripts\u002Frun_actor.js \\\n  --actor \"ACTOR_ID\" \\\n  --input 'JSON_INPUT'\n```\n\n**CSV:**\n```bash\nnode --env-file=.env ${CLAUDE_PLUGIN_ROOT}\u002Freference\u002Fscripts\u002Frun_actor.js \\\n  --actor \"ACTOR_ID\" \\\n  --input 'JSON_INPUT' \\\n  --output YYYY-MM-DD_OUTPUT_FILE.csv \\\n  --format csv\n```\n\n**JSON:**\n```bash\nnode --env-file=.env ${CLAUDE_PLUGIN_ROOT}\u002Freference\u002Fscripts\u002Frun_actor.js \\\n  --actor \"ACTOR_ID\" \\\n  --input 'JSON_INPUT' \\\n  --output YYYY-MM-DD_OUTPUT_FILE.json \\\n  --format json\n```\n\n### Step 5: Summarize Findings\n\nAfter completion, report:\n- Number of content pieces analyzed\n- File location and name\n- Key performance insights\n- Suggested next steps (deeper analysis, content optimization)\n\n## Error Handling\n\n`APIFY_TOKEN not found` - Ask user to create `.env` with `APIFY_TOKEN=your_token`\n`mcpc not found` - Ask user to install `npm install -g @apify\u002Fmcpc`\n`Actor not found` - Check Actor ID spelling\n`Run FAILED` - Ask user to check Apify console link in error output\n`Timeout` - Reduce input size or increase `--timeout`\n\n## Limitations\n- Use this skill only when the task clearly matches the scope described above.\n- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.\n- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.\n","","imported","https:\u002F\u002Fgithub.com\u002Fsickn33\u002Fantigravity-awesome-skills","user_system_seed","SkillOPIC",true,54,1968,"2026-05-16 13:03:45",{"id":8,"name":21,"slug":22,"icon":23,"description":24,"sort":25,"createdAt":26},"编程开发","coding","mdi-code-braces","代码生成、调试、审查，提升开发效率",2,"2026-05-16 12:53:40",{"id":7,"name":28,"slug":29,"icon":30,"description":31,"moduleId":8,"sort":25,"skillCount":32,"createdAt":26},"后端开发","backend","mdi-server","API、数据库、服务端架构",296,[34],{"id":35,"skillId":4,"version":36,"fileName":37,"fileSize":38,"filePath":39,"fileHash":40,"manifest":41,"createdAt":19},"2e641c4e-b4c9-4eda-a837-0f5de7a37e90","1.0.0","apify-content-analytics.zip",5681,"uploads\u002Fskills\u002Fcdd72a36-5e14-4592-83db-395ebb4b0050\u002Fapify-content-analytics.zip","248f25e923e3e18b079c5356a89c7c16a0202db6d6885e2e155b25948f97f95e","[{\"path\":\"SKILL.md\",\"isDirectory\":false,\"size\":4882},{\"path\":\"reference\u002Fscripts\u002Frun_actor.js\",\"isDirectory\":false,\"size\":11747}]",{"code":43,"message":44,"data":45},200,"success",{"items":46,"stats":47,"page":50},[],{"averageRating":48,"totalRatings":48,"ratingCounts":49},0,[48,48,48,48,48],{"limit":51,"offset":48,"hasMore":52,"nextOffset":51,"ratedOnly":16},15,false]