Analytics
Nexus-JPF/note-companion
When the user wants to set up, improve, or audit analytics tracking and measurement.
Generate comprehensive marketing analytics reports by collecting KPIs, analyzing trends, and delivering actionable insights with attribution modeling and funnel analysis.
Install Analytics Reporting
$ npx skills add seb1n/awesome-ai-agent-skills --skill analytics-reporting -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install seb1n/awesome-ai-agent-skills analytics-reporting --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/seb1n/awesome-ai-agent-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/marketing-and-seo/analytics-reporting .claude/skills/analytics-reporting && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "analytics-reporting" agent skill from https://github.com/seb1n/awesome-ai-agent-skills/tree/main/marketing-and-seo/analytics-reporting into .claude/skills/analytics-reporting/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analytics-reporting", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/seb1n/awesome-ai-agent-skills/tree/main/marketing-and-seo/analytics-reportingType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add seb1n/awesome-ai-agent-skills --skill analytics-reporting -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install seb1n/awesome-ai-agent-skills analytics-reporting --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/seb1n/awesome-ai-agent-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/marketing-and-seo/analytics-reporting .agents/skills/analytics-reporting && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "analytics-reporting" agent skill from https://github.com/seb1n/awesome-ai-agent-skills/tree/main/marketing-and-seo/analytics-reporting into .agents/skills/analytics-reporting/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analytics-reporting", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add seb1n/awesome-ai-agent-skills --skill analytics-reporting -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install seb1n/awesome-ai-agent-skills analytics-reporting --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/seb1n/awesome-ai-agent-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/marketing-and-seo/analytics-reporting .cursor/skills/analytics-reporting && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "analytics-reporting" agent skill from https://github.com/seb1n/awesome-ai-agent-skills/tree/main/marketing-and-seo/analytics-reporting into .cursor/skills/analytics-reporting/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analytics-reporting", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/seb1n/awesome-ai-agent-skills.git --path marketing-and-seo/analytics-reporting--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add seb1n/awesome-ai-agent-skills --skill analytics-reporting -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install seb1n/awesome-ai-agent-skills analytics-reporting --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/seb1n/awesome-ai-agent-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/marketing-and-seo/analytics-reporting .gemini/skills/analytics-reporting && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "analytics-reporting" agent skill from https://github.com/seb1n/awesome-ai-agent-skills/tree/main/marketing-and-seo/analytics-reporting into .gemini/skills/analytics-reporting/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analytics-reporting", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install seb1n/awesome-ai-agent-skills analytics-reportingInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add seb1n/awesome-ai-agent-skills --skill analytics-reporting -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/seb1n/awesome-ai-agent-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/marketing-and-seo/analytics-reporting .github/skills/analytics-reporting && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "analytics-reporting" agent skill from https://github.com/seb1n/awesome-ai-agent-skills/tree/main/marketing-and-seo/analytics-reporting into .github/skills/analytics-reporting/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analytics-reporting", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add seb1n/awesome-ai-agent-skills --skill analytics-reporting -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install seb1n/awesome-ai-agent-skills analytics-reporting --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/seb1n/awesome-ai-agent-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/marketing-and-seo/analytics-reporting .opencode/skills/analytics-reporting && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "analytics-reporting" agent skill from https://github.com/seb1n/awesome-ai-agent-skills/tree/main/marketing-and-seo/analytics-reporting into .opencode/skills/analytics-reporting/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analytics-reporting", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
analytics-reporting- GitHub stars
- 206
- Token cost
- ~2.7k tokens
- SKILL.md length
- 1,348 words
- Files
- 1
- Skills in repo
- 92
- Repo updated
- First seen
- Licence
- MIT
At a glance
Generate comprehensive marketing analytics reports by collecting KPIs, analyzing trends, and delivering actionable insights with attribution modeling and funnel analysis.
- Works in 6 steps: Define reporting scope and KPIs. Clarify… → Collect data from all sources. Pull data… → Analyze trends and identify patterns.… → …
- The user requests analytics reporting
- SKILL.md covers Workflow, Usage, Examples and Best Practices, plus 1 more section
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Analytics Reporting is an agent skill from seb1n/awesome-ai-agent-skills. Generate comprehensive marketing analytics reports by collecting KPIs, analyzing trends, and delivering actionable insights with attribution modeling and funnel analysis. Use when the user requests analytics reporting or provides relevant inputs for this workflow.
Its SKILL.md is about 2.7k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Marketing & SEO, covering Marketing analytics, OKRs and executive reporting and Product analytics. The repository describes itself as: 103 ready-to-use AI agent skills for Claude Code, OpenAI Codex, Gemini CLI, Cursor, GitHub Copilot, Windsurf, and other Agent Skills-compatible tools. Complete SKILL.md… The licence is MIT.
When your agent uses it
- The user requests analytics reporting
- Provides relevant inputs for this workflow
Example prompts
- “/analytics-reporting”
Workflow steps
6 steps, taken from the first numbered list in SKILL.md.
- Define reporting scope and KPIs. Clarify the report type (monthly overview, campaign-specific, channel deep-dive) and time period…
- Collect data from all sources. Pull data from web analytics (Google Analytics, Plausible), search console (impressions, clicks, average…
- Analyze trends and identify patterns. Compare current period metrics against previous period and year-over-year baselines. Calculate…
- Apply attribution modeling. Move beyond last-click attribution to understand the full customer journey. Apply multi-touch models — linear…
- Perform funnel and cohort analysis. Map the conversion funnel from first visit to purchase or signup. Calculate drop-off rates at each…
- Generate the report with visualizations and recommendations. Structure the report with an executive summary, channel-by-channel breakdown…
What it can do on your machine
Read from SKILL.md and the folder at commit 75865a5. 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
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Analytics Reporting loads about 2.7k tokens when it runs. Until then it costs about 71 tokens; SKILL.md has 1,348 words of instructions outside code blocks.
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
Auto-check passed
The automated check found no risky patterns in SKILL.md.
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 seb1n/awesome-ai-agent-skills at commit 75865a5, republished under its MIT licence (© seb1n). 1,348 words, ~2,656 tokens.
Download SKILL.mdSave it as
.claude/skills/analytics-reporting/SKILL.md (or your agent's skills folder).- name
- analytics-reporting
- description
- Generate comprehensive marketing analytics reports by collecting KPIs, analyzing trends, and delivering actionable insights with attribution modeling and funnel analysis. Use when the user requests analytics reporting or provides relevant inputs for this workflow.
- license
- MIT
- metadata.author
- awesome-ai-agent-skills
- metadata.version
- 1.0.0
Analytics Reporting
This skill enables an AI agent to generate detailed marketing analytics reports that go beyond raw numbers to deliver actionable insights. The agent collects data across traffic, engagement, conversion, and revenue metrics, applies attribution models to understand channel contribution, performs funnel and cohort analysis, and produces executive-ready reports with clear recommendations. The output helps marketing teams make data-driven decisions about budget allocation, campaign optimization, and strategy shifts.
Workflow
Define reporting scope and KPIs. Clarify the report type (monthly overview, campaign-specific, channel deep-dive) and time period. Establish the primary KPIs to track: traffic metrics (sessions, unique visitors, pageviews), engagement metrics (bounce rate, time on page, pages per session), conversion metrics (conversion rate, leads generated, cost per acquisition), and revenue metrics (customer lifetime value, return on ad spend, marketing-attributed revenue).
Collect data from all sources. Pull data from web analytics (Google Analytics, Plausible), search console (impressions, clicks, average position), advertising platforms (Google Ads, Meta Ads, LinkedIn Ads), email marketing (Mailchimp, SendGrid), CRM (HubSpot, Salesforce), and social media analytics (native platform insights). Normalize date ranges and metric definitions across sources to ensure comparability.
Analyze trends and identify patterns. Compare current period metrics against previous period and year-over-year baselines. Calculate growth rates, identify statistically significant changes, and flag anomalies (traffic spikes from viral content, drops from algorithm updates or site outages). Segment data by channel, device, geography, and user cohort to uncover hidden patterns.
Apply attribution modeling. Move beyond last-click attribution to understand the full customer journey. Apply multi-touch models — linear (equal credit), time-decay (more credit to recent touchpoints), or data-driven (algorithmic) — to evaluate how each channel contributes to conversions. This prevents over-investing in bottom-funnel channels while starving the awareness channels that feed the pipeline.
Perform funnel and cohort analysis. Map the conversion funnel from first visit to purchase or signup. Calculate drop-off rates at each stage: landing page → lead form → MQL → SQL → customer. Identify the highest-friction stages and recommend tests to improve them. Run cohort analysis to understand retention — do users acquired from organic search retain better than those from paid ads after 30, 60, and 90 days?
Generate the report with visualizations and recommendations. Structure the report with an executive summary, channel-by-channel breakdown, top-performing content, funnel analysis, and a prioritized recommendation section. Include tables, trend charts, and comparison visualizations. End every section with a "So what?" — the specific action the team should take based on the data.
Usage
Provide the agent with the reporting period, data sources or raw data exports, and which KPIs matter most to your team. The agent returns a structured analytics report with insights and recommendations.
Prompt: Generate a January 2025 marketing analytics report for our e-commerce site. Data sources: Google Analytics, Google Ads, and Shopify. Focus on traffic trends, conversion rate by channel, and ROAS.
Examples
Example 1: Monthly Marketing Analytics Report
Request: Generate the February 2025 monthly report for acmesaas.com.
Executive Summary:
| Metric | Feb 2025 | Jan 2025 | MoM Change | YoY Change |
|---|---|---|---|---|
| Total Sessions | 84,200 | 78,500 | +7.3% | +22.1% |
| Unique Visitors | 61,400 | 57,800 | +6.2% | +18.9% |
| Bounce Rate | 42.1% | 45.3% | -3.2 pts | -5.8 pts |
| Avg Session Duration | 3:42 | 3:18 | +12.1% | +8.4% |
| Lead Form Submissions | 1,240 | 1,080 | +14.8% | +31.2% |
| Trial Signups | 386 | 342 | +12.9% | +28.7% |
| CAC (blended) | $127 | $143 | -11.2% | -19.1% |
| Marketing-Attributed Revenue | $94,200 | $81,600 | +15.4% | +41.3% |
Channel Breakdown:
| Channel | Sessions | Conv. Rate | Leads | Cost | CAC |
|---|---|---|---|---|---|
| Organic Search | 38,100 | 2.1% | 800 | $0 | $0 |
| Paid Search (Google) | 18,400 | 1.8% | 331 | $24,800 | $74.92 |
| LinkedIn Ads | 8,200 | 1.4% | 115 | $18,600 | $161.74 |
| Email Marketing | 12,600 | 3.2% | 403 | $1,200 | $2.98 |
| Direct / Referral | 6,900 | 0.9% | 62 | $0 | $0 |
Key Insights:
- Organic search is the efficiency leader. It delivered 65% of all leads at zero marginal cost. The 22% YoY session growth reflects SEO investments from Q3–Q4 2024 compounding.
- Email marketing has the highest conversion rate at 3.2%. The new onboarding drip sequence launched in January drove a 40% lift in email-sourced trial signups.
- LinkedIn Ads CAC is elevated at $161.74. However, LinkedIn leads convert to paid customers at 2.4x the rate of Google Ads leads. When measured by LTV:CAC ratio, LinkedIn is more efficient.
- Bounce rate improved 3.2 points MoM. This correlates with the homepage redesign deployed February 3rd, which added social proof and clearer CTAs above the fold.
Recommendations:
- Increase organic content investment: publish 2 additional SEO-targeted posts per week focused on high-intent commercial keywords identified in the January keyword gap analysis.
- Scale LinkedIn Ads budget by 20% but narrow targeting to Director+ titles at companies with 200–2,000 employees to reduce CAC.
- A/B test the trial signup page — current 12.9% growth is strong but the 1.8% paid search conversion rate suggests friction on the landing page.
Show full SKILL.md (575 more words)Show less
Example 2: Campaign Performance Analysis
Request: Analyze the "Q1 Product Launch" paid campaign running Jan 15–Feb 28 across Google Ads and Meta Ads.
Campaign Summary:
| Metric | Google Ads | Meta Ads | Combined |
|---|---|---|---|
| Impressions | 1,240,000 | 2,860,000 | 4,100,000 |
| Clicks | 31,000 | 22,880 | 53,880 |
| CTR | 2.5% | 0.8% | 1.3% |
| Cost | $18,600 | $14,200 | $32,800 |
| CPC | $0.60 | $0.62 | $0.61 |
| Conversions (signups) | 620 | 274 | 894 |
| Conv. Rate | 2.0% | 1.2% | 1.7% |
| Cost per Conversion | $30.00 | $51.82 | $36.69 |
Attribution Analysis (Multi-Touch, Time-Decay Model):
| Touchpoint Path | Conversions | Avg Days to Convert |
|---|---|---|
| Google Ad → Direct → Signup | 312 | 1.4 |
| Meta Ad → Google Ad → Signup | 186 | 4.2 |
| Meta Ad → Organic → Email → Signup | 142 | 8.6 |
| Organic → Meta Ad (retarget) → Signup | 98 | 6.1 |
| Other multi-touch paths | 156 | 5.8 |
Findings:
- Google Ads drives direct, fast conversions (1.4-day avg) — ideal for capturing high-intent demand.
- Meta Ads plays a crucial assist role: 186 conversions touched Meta first before converting via Google. Under last-click attribution, Meta would receive zero credit for these.
- The Meta → Organic → Email path shows that awareness campaigns create a delayed pipeline that converts through nurture. Cutting Meta spend would reduce email conversions within 2–4 weeks.
Recommendations:
- Maintain Google Ads at current spend; test increasing bids on top 10 converting keywords by 15%.
- Shift 30% of Meta budget from prospecting to retargeting — the retargeting path converts at 2.1x the rate of prospecting at half the cost.
- Extend the campaign 2 weeks: the time-decay model shows conversions are still accelerating as the multi-touch paths mature.
Best Practices
- Always compare against a baseline. Raw numbers are meaningless without context. Show month-over-month, year-over-year, and target-vs-actual comparisons for every metric.
- Segment before you summarize. Aggregate numbers hide critical patterns. Always break data down by channel, device, geography, and user segment before drawing conclusions.
- Use multi-touch attribution for any campaign with more than one channel. Last-click attribution systematically undervalues awareness and mid-funnel channels, leading to misallocated budgets.
- Lead with insights, not data. Executives don't need 50 metrics — they need 3–5 actionable findings. Structure reports as "what happened → why it matters → what to do next."
- Automate data collection, not interpretation. Scripts and dashboards should pull data automatically, but the narrative — the "so what" — requires human or AI judgment applied to the specific business context.
- Include confidence indicators. For small sample sizes or short time windows, note that trends may not be statistically significant. Avoid making major budget decisions on fewer than 100 conversions per variant.
Edge Cases
- Data discrepancies between platforms. Google Analytics and ad platforms often disagree on conversion counts due to different attribution windows, cookie handling, and tracking methodologies. Document the source of truth for each metric and note known discrepancies in the report.
- Cookie consent and tracking gaps. In regions with GDPR or CCPA enforcement, 20–40% of users may decline tracking cookies. Reported traffic and conversions are understated. Use server-side tracking or consent-mode modeling to estimate true values, and note the adjustment methodology.
- Seasonality masking real trends. A 15% traffic drop in December may be seasonal, not a problem. Always include year-over-year comparisons for seasonal businesses and flag whether changes are within normal seasonal variance.
- Attribution for offline conversions. B2B sales with long cycles often close offline via sales calls. Implement UTM-to-CRM mapping and closed-loop reporting so marketing can claim attribution for pipeline it influenced.
- Zero-conversion campaigns in early stages. New awareness campaigns may show zero direct conversions for 4–8 weeks. Measure leading indicators — impressions, reach, video view rate, landing page visits — to assess whether the campaign is building pipeline before judging ROI.
© seb1n, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in marketing-and-seo/analytics-reporting of seb1n/awesome-ai-agent-skills.
Open the folder on GitHubat commit 75865a5
Compare with similar skills
Analytics Reporting next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Analytics Reporting this skillseb1n/awesome-ai-agent-skills | 206 | — | ~2.7k | Automated safety check: Pass | MIT | |
| AnalyticsNexus-JPF/note-companion | 869 | 6 repos | ~2.2k | Automated safety check: Pass | MIT | |
| Marketing Analyticscbrock84/headcount | 2k | — | ~942 | Automated safety check: Pass | MIT | |
| Analytics Trackingborghei/Claude-Skills | 874 | — | ~5.7k | Automated safety check: Pass | MIT | |
| Analytics Strategyrampstackco/claude-skills | 935 | 1 repos | ~2.4k | Automated safety check: Pass | MIT | |
| App Analyticsappeeky/aso-skills | 2.1k | — | ~1.6k | Automated safety check: Pass | MIT |
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Categories
Questions about Analytics Reporting
What does Analytics Reporting do?
Generate comprehensive marketing analytics reports by collecting KPIs, analyzing trends, and delivering actionable insights with attribution modeling and funnel analysis. Analytics Reporting is an agent skill from seb1n/awesome-ai-agent-skills. Generate comprehensive marketing analytics reports by collecting KPIs, analyzing trends, and delivering actionable insights with attribution modeling and funnel analysis.
When should I use Analytics Reporting?
Analytics Reporting fits situations like: the user requests analytics reporting; provides relevant inputs for this workflow.
How do I install Analytics Reporting in Claude Code?
Run `npx skills add seb1n/awesome-ai-agent-skills --skill analytics-reporting -a claude-code`. Or copy the skill folder (marketing-and-seo/analytics-reporting in seb1n/awesome-ai-agent-skills) into .claude/skills/analytics-reporting in your project. Claude Code loads it when a task matches its description.
How do I install Analytics Reporting in Codex?
Run `npx skills add seb1n/awesome-ai-agent-skills --skill analytics-reporting -a codex`. Or copy the skill folder (marketing-and-seo/analytics-reporting in seb1n/awesome-ai-agent-skills) into .agents/skills/analytics-reporting in your project. Codex loads it when a task matches its description.
Can I use Analytics Reporting in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add seb1n/awesome-ai-agent-skills --skill analytics-reporting -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/analytics-reporting, .gemini/skills/analytics-reporting, .github/skills/analytics-reporting and .opencode/skills/analytics-reporting in your project.
What does Analytics Reporting need to run?
SKILL.md names no scripts, command-line tools or credentials: Analytics Reporting is instructions for the agent only.
Does Analytics Reporting access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Analytics Reporting safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Analytics Reporting use?
Analytics Reporting is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Analytics Reporting use?
About 2.7k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Analytics Reporting?
Skills that share tags, products or a category with Analytics Reporting: Analytics (Nexus-JPF/note-companion, 869 stars), Marketing Analytics (cbrock84/headcount, 2k stars), Analytics Tracking (borghei/Claude-Skills, 874 stars) and Analytics Strategy (rampstackco/claude-skills, 935 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Analytics Reporting?
seb1n (a GitHub user) maintains it in seb1n/awesome-ai-agent-skills, which has 206 GitHub stars. The repository holds 92 skills in this directory. The repository was last updated on August 9, 2026.
Source: seb1n/awesome-ai-agent-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.