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Use when checking current marketing performance, monitoring KPI health, comparing to benchmarks, or getting a quick status update across analytics platforms.","LongLeo287\u002FSEOSONA-OS","LongLeo287","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F141803367?v=4","01M6JK75C62VCNZKR5BRZ386YV","LongLeo","01M75CK60XQGYJY3YDGGFJQDYT",2,"MIT","https:\u002F\u002Fgithub.com\u002FLongLeo287\u002FSEOSONA-OS\u002Fblob\u002Fmain\u002F2_KNOWLEDGE\u002Fframeworks\u002Fdigital-marketing-pro\u002Fskills\u002Fperformance-check\u002FSKILL.md",[],[],"2026-11-12 13:55:43",false,"---\nname: performance-check\ndescription: \"Pull live marketing metrics for a performance snapshot: KPIs vs targets, trend comparison, and cross-platform overview. Use when checking current marketing performance, monitoring KPI health, comparing to benchmarks, or getting a quick status update across analytics platforms.\"\nuser-invocable: true\ntriggers:\n  - check marketing performance\n  - pull current KPIs\n  - performance snapshot\n  - how are our marketing metrics\n  - compare performance to targets\n  - marketing performance check\n  - quick KPI health check\n  - check campaign performance\n---\n\n# \u002Fdigital-marketing-pro:performance-check\n\n## Purpose\n\nPull live metrics from all connected analytics MCPs and produce a comprehensive performance snapshot. Compares current performance to KPI targets defined in the brand profile, previous-period benchmarks, and industry averages. Designed for quick health checks — run it daily, weekly, or on-demand to stay on top of marketing performance without switching between platforms.\n\n**Scope (vs `\u002Fdigital-marketing-pro:performance-report`):** this skill is the **live-pull + snapshot-persistence** layer — it fetches current metrics from the platforms and saves a snapshot for trend history. When you need a formatted, narrative deliverable for stakeholders (executive summary, channel commentary, prioritized recommendations, branded formatting), run `\u002Fdigital-marketing-pro:performance-report`, which consumes the snapshots this skill persists rather than re-pulling. Use `performance-check` to *see the numbers now*; use `performance-report` to *tell the story*.\n\n## Input Required\n\nThe user must provide (or will be prompted for):\n\n- **Time period**: Today, this week, this month, this quarter, or a custom date range (e.g., \"last 14 days\", \"Jan 1 - Jan 31\")\n- **Channel focus** (optional): Specific channels or platforms to prioritize (e.g., \"paid search only\", \"email and social\").\n  If omitted, all connected platforms are included\n- **Comparison period** (optional): Period to compare against — previous period, same period last year, or custom range.\n  Defaults to the equivalent previous period\n- **KPI targets** (optional): Override targets for this check.\n  If omitted, targets are pulled from profile.json goals and KPI settings\n- **Granularity** (optional): Daily, weekly, or aggregate view. Defaults to aggregate for the selected period\n\n## Process\n\n1. **Load brand context**: Read `~\u002F.claude-marketing\u002Fbrands\u002F_active-brand.json` for the active slug, then load `~\u002F.claude-marketing\u002Fbrands\u002F{slug}\u002Fprofile.json`. Apply brand voice, compliance rules for target markets (`skills\u002Fcontext-engine\u002Fcompliance-rules.md`), and industry context. Also check for guidelines at `~\u002F.claude-marketing\u002Fbrands\u002F{slug}\u002Fguidelines\u002F_manifest.json` — if present, load restrictions. Check for agency SOPs at `~\u002F.claude-marketing\u002Fsops\u002F`. If no brand exists, ask: \"Set up a brand first (\u002Fdigital-marketing-pro:brand-setup)?\" — or proceed with defaults.\n2. **Detect connected analytics MCPs**: Check `.mcp.json` and active MCP connections to identify which platforms are available\n   (google-analytics, google-ads, meta-marketing, linkedin-marketing, tiktok-ads, mailchimp, stripe, mixpanel, amplitude, shopify, etc.).\n   Log any expected platforms that are not connected so the user knows about gaps in coverage.\n3. **Pull metrics from each connected platform**: Request key metrics for the specified time period:\n   - Traffic: sessions, users, pageviews, new vs returning (break out GA4's **\"AI Assistant\"** default channel — referrals from ChatGPT, Gemini, Copilot, Perplexity, etc. — so AI-sourced traffic isn't buried under Referral\u002FDirect)\n   - Ads: impressions, clicks, spend, CPC, CPM\n   - Conversions: leads, purchases, sign-ups, goal completions\n   - Revenue: total revenue, average order value, transaction count\n   - Engagement: open rate, click rate, bounce rate, time on site\n   - Platform-specific: email deliverability, social reach, video views, app installs\n4. **Aggregate into unified dashboard**: Normalize metrics across platforms into a single cross-channel view with consistent\n   naming, currency conversion if multi-currency, and de-duplicated conversion counts where platforms overlap\n5. **Calculate KPIs vs targets**: Compare actuals to targets from `profile.json` goals — flag green (on track or exceeding),\n   yellow (within 10% of target), or red (missing by >10%). Include absolute and percentage variance for each KPI.\n6. **Compare to previous period**: Calculate period-over-period change for every metric and attach trend direction\n   (up\u002Fdown\u002Fflat) with percentage change. If year-over-year data is available, include as a secondary reference point.\n7. **Benchmark against industry**: Reference `skills\u002Fcontext-engine\u002Findustry-profiles.md` for the brand's industry to\n   contextualize performance relative to category averages. Flag metrics significantly above or below industry norms.\n8. **Identify notable findings**: Surface the top 3 wins (best-performing metrics or biggest improvements), top 3 concerns\n   (underperforming or declining metrics), and any material changes that warrant deeper investigation. Before labelling a\n   conversion-rate change \"statistically significant,\" confirm it with `python \"${CLAUDE_PLUGIN_ROOT}\u002Fscripts\u002Fsignificance-tester.py\" --control-visitors {n} --control-conversions {n} --variant-visitors {n} --variant-conversions {n} --confidence 0.95` — do not call a movement significant off a raw percentage delta.\n9. **Generate recommended actions**: Based on the data, produce 3-5 specific, actionable next steps — e.g., \"Pause\n   underperforming ad set X\", \"Increase budget on high-ROAS channel Y\", \"Investigate traffic drop on Z\",\n   \"Scale winning creative variant\", \"Run \u002Fdigital-marketing-pro:anomaly-scan for deeper diagnosis\".\n10. **Save performance snapshot**: Execute `python \"${CLAUDE_PLUGIN_ROOT}\u002Fscripts\u002Fperformance-monitor.py\" --brand {slug} --action save-snapshot --data '{...current metrics...}'`\n    to persist the snapshot for historical comparison and trend tracking across future runs.\n11. **Log significant insights**: For any metric with a notable deviation, save via\n    `python \"${CLAUDE_PLUGIN_ROOT}\u002Fscripts\u002Fcampaign-tracker.py\" --brand {slug} --action save-insight --data '{\"type\":\"anomaly\",\"insight\":\"...\",\"context\":\"...\"}'`\n    so findings surface in future reports and campaign planning.\n\n## Output\n\nA structured performance snapshot containing:\n\n- **Executive summary**: 2-3 sentence overview of overall marketing health with the single most important finding highlighted\n- **Channel-by-channel metrics table**: Traffic, impressions, clicks, conversions, revenue, spend, CPA, ROAS, and engagement\n  rate per platform — sortable by any column\n- **KPI scoreboard**: Each tracked KPI with actual value, target value, percentage to target, variance (absolute and %),\n  trend arrow (vs previous period), and RAG status (red\u002Famber\u002Fgreen)\n- **Cross-channel summary**: Total spend, total conversions, blended CPA, blended ROAS, total revenue, marketing efficiency\n  ratio, and overall health assessment\n- **Period-over-period comparison**: Percentage change for all key metrics vs the comparison period with directional\n  indicators and sparkline-style trend data\n- **Industry benchmark context**: How key metrics compare to industry averages from industry-profiles.md, with percentile\n  ranking where data is available\n- **Notable findings**: Top 3 wins, top 3 concerns, and any anomalies worth investigating further — each with supporting\n  data points and severity indicator\n- **Recommended actions**: 3-5 specific next steps with priority ranking, expected impact, and the platform or campaign\n  each action applies to\n- **Data gaps**: Any platforms that were expected but not connected, metrics that could not be retrieved, or time periods\n  with incomplete data — so the user knows what is missing from the picture\n\n## Agents Used\n\n- **analytics-analyst** — Metrics interpretation, KPI analysis, cross-channel normalization, trend identification, industry benchmarking, insight generation, and action recommendation\n- **performance-monitor-agent** — Data aggregation from connected MCPs, baseline comparison, snapshot persistence, historical trend analysis, and gap detection\n","# \u002Fdigital-marketing-pro:performance-check\n\n## Purpose\n\nPull live metrics from all connected analytics MCPs and produce a comprehensive performance snapshot. Compares current performance to KPI targets defined in the brand profile, previous-period benchmarks, and industry averages. Designed for quick health checks — run it daily, weekly, or on-demand to stay on top of marketing performance without switching between platforms.\n\n**Scope (vs `\u002Fdigital-marketing-pro:performance-report`):** this skill is the **live-pull + snapshot-persistence** layer — it fetches current metrics from the platforms and saves a snapshot for trend history. When you need a formatted, narrative deliverable for stakeholders (executive summary, channel commentary, prioritized recommendations, branded formatting), run `\u002Fdigital-marketing-pro:performance-report`, which consumes the snapshots this skill persists rather than re-pulling. Use `performance-check` to *see the numbers now*; use `performance-report` to *tell the story*.\n\n## Input Required\n\nThe user must provide (or will be prompted for):\n\n- **Time period**: Today, this week, this month, this quarter, or a custom date range (e.g., \"last 14 days\", \"Jan 1 - Jan 31\")\n- **Channel focus** (optional): Specific channels or platforms to prioritize (e.g., \"paid search only\", \"email and social\").\n  If omitted, all connected platforms are included\n- **Comparison period** (optional): Period to compare against — previous period, same period last year, or custom range.\n  Defaults to the equivalent previous period\n- **KPI targets** (optional): Override targets for this check.\n  If omitted, targets are pulled from profile.json goals and KPI settings\n- **Granularity** (optional): Daily, weekly, or aggregate view. Defaults to aggregate for the selected period\n\n## Process\n\n1. **Load brand context**: Read `~\u002F.claude-marketing\u002Fbrands\u002F_active-brand.json` for the active slug, then load `~\u002F.claude-marketing\u002Fbrands\u002F{slug}\u002Fprofile.json`. Apply brand voice, compliance rules for target markets (`skills\u002Fcontext-engine\u002Fcompliance-rules.md`), and industry context. Also check for guidelines at `~\u002F.claude-marketing\u002Fbrands\u002F{slug}\u002Fguidelines\u002F_manifest.json` — if present, load restrictions. Check for agency SOPs at `~\u002F.claude-marketing\u002Fsops\u002F`. If no brand exists, ask: \"Set up a brand first (\u002Fdigital-marketing-pro:brand-setup)?\" — or proceed with defaults.\n2. **Detect connected analytics MCPs**: Check `.mcp.json` and active MCP connections to identify which platforms are available\n   (google-analytics, google-ads, meta-marketing, linkedin-marketing, tiktok-ads, mailchimp, stripe, mixpanel, amplitude, shopify, etc.).\n   Log any expected platforms that are not connected so the user knows about gaps in coverage.\n3. **Pull metrics from each connected platform**: Request key metrics for the specified time period:\n   - Traffic: sessions, users, pageviews, new vs returning (break out GA4's **\"AI Assistant\"** default channel — referrals from ChatGPT, Gemini, Copilot, Perplexity, etc. — so AI-sourced traffic isn't buried under Referral\u002FDirect)\n   - Ads: impressions, clicks, spend, CPC, CPM\n   - Conversions: leads, purchases, sign-ups, goal completions\n   - Revenue: total revenue, average order value, transaction count\n   - Engagement: open rate, click rate, bounce rate, time on site\n   - Platform-specific: email deliverability, social reach, video views, app installs\n4. **Aggregate into unified dashboard**: Normalize metrics across platforms into a single cross-channel view with consistent\n   naming, currency conversion if multi-currency, and de-duplicated conversion counts where platforms overlap\n5. **Calculate KPIs vs targets**: Compare actuals to targets from `profile.json` goals — flag green (on track or exceeding),\n   yellow (within 10% of target), or red (missing by >10%). Include absolute and percentage variance for each KPI.\n6. **Compare to previous period**: Calculate period-over-period change for every metric and attach trend direction\n   (up\u002Fdown\u002Fflat) with percentage change. If year-over-year data is available, include as a secondary reference point.\n7. **Benchmark against industry**: Reference `skills\u002Fcontext-engine\u002Findustry-profiles.md` for the brand's industry to\n   contextualize performance relative to category averages. Flag metrics significantly above or below industry norms.\n8. **Identify notable findings**: Surface the top 3 wins (best-performing metrics or biggest improvements), top 3 concerns\n   (underperforming or declining metrics), and any material changes that warrant deeper investigation. Before labelling a\n   conversion-rate change \"statistically significant,\" confirm it with `python \"${CLAUDE_PLUGIN_ROOT}\u002Fscripts\u002Fsignificance-tester.py\" --control-visitors {n} --control-conversions {n} --variant-visitors {n} --variant-conversions {n} --confidence 0.95` — do not call a movement significant off a raw percentage delta.\n9. **Generate recommended actions**: Based on the data, produce 3-5 specific, actionable next steps — e.g., \"Pause\n   underperforming ad set X\", \"Increase budget on high-ROAS channel Y\", \"Investigate traffic drop on Z\",\n   \"Scale winning creative variant\", \"Run \u002Fdigital-marketing-pro:anomaly-scan for deeper diagnosis\".\n10. **Save performance snapshot**: Execute `python \"${CLAUDE_PLUGIN_ROOT}\u002Fscripts\u002Fperformance-monitor.py\" --brand {slug} --action save-snapshot --data '{...current metrics...}'`\n    to persist the snapshot for historical comparison and trend tracking across future runs.\n11. **Log significant insights**: For any metric with a notable deviation, save via\n    `python \"${CLAUDE_PLUGIN_ROOT}\u002Fscripts\u002Fcampaign-tracker.py\" --brand {slug} --action save-insight --data '{\"type\":\"anomaly\",\"insight\":\"...\",\"context\":\"...\"}'`\n    so findings surface in future reports and campaign planning.\n\n## Output\n\nA structured performance snapshot containing:\n\n- **Executive summary**: 2-3 sentence overview of overall marketing health with the single most important finding highlighted\n- **Channel-by-channel metrics table**: Traffic, impressions, clicks, conversions, revenue, spend, CPA, ROAS, and engagement\n  rate per platform — sortable by any column\n- **KPI scoreboard**: Each tracked KPI with actual value, target value, percentage to target, variance (absolute and %),\n  trend arrow (vs previous period), and RAG status (red\u002Famber\u002Fgreen)\n- **Cross-channel summary**: Total spend, total conversions, blended CPA, blended ROAS, total revenue, marketing efficiency\n  ratio, and overall health assessment\n- **Period-over-period comparison**: Percentage change for all key metrics vs the comparison period with directional\n  indicators and sparkline-style trend data\n- **Industry benchmark context**: How key metrics compare to industry averages from industry-profiles.md, with percentile\n  ranking where data is available\n- **Notable findings**: Top 3 wins, top 3 concerns, and any anomalies worth investigating further — each with supporting\n  data points and severity indicator\n- **Recommended actions**: 3-5 specific next steps with priority ranking, expected impact, and the platform or campaign\n  each action applies to\n- **Data gaps**: Any platforms that were expected but not connected, metrics that could not be retrieved, or time periods\n  with incomplete data — so the user knows what is missing from the picture\n\n## Agents Used\n\n- **analytics-analyst** — Metrics interpretation, KPI analysis, cross-channel normalization, trend identification, industry benchmarking, insight generation, and action recommendation\n- **performance-monitor-agent** — Data aggregation from connected MCPs, baseline comparison, snapshot persistence, historical trend analysis, and gap detection\n",{"name":5,"triggers":24,"description":7,"user-invocable":33},[25,26,27,28,29,30,31,32],"check marketing performance","pull current KPIs","performance snapshot","how are our marketing metrics","compare performance to targets","marketing performance check","quick KPI health check","check campaign performance",true,"main","4fd50b1281e8cd978866b6b4556242605cabf708","2_KNOWLEDGE\u002Fframeworks\u002Fdigital-marketing-pro\u002Fskills\u002Fperformance-check",1,"https:\u002F\u002Fgithub.com\u002FLongLeo287\u002FSEOSONA-OS","https:\u002F\u002Fseosona-os.vercel.app","https:\u002F\u002Fgithub.com\u002FLongLeo287","https:\u002F\u002Fwww.facebook.com\u002FResourceRookie2023","2026-10-09 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