Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add tahirraufkeeyu/software-development-agent-stack--sdas --skill analytics-reportgit clone --depth 1 https://github.com/tahirraufkeeyu/software-development-agent-stack--sdasWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/tahirraufkeeyu/software-development-agent-stack--sdas/analytics-report)<a href="https://agentmods.dev/skills/tahirraufkeeyu/software-development-agent-stack--sdas/analytics-report"><img src="https://agentmods.dev/badge/skills/tahirraufkeeyu/software-development-agent-stack--sdas/analytics-report.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00063 | $0.02902 |
| Opus 5 | $0.00032 | $0.01451 |
| Sonnet 5 | $0.00013 | $0.00580 |
| Haiku 4.5 | $0.00006 | $0.00290 |
Grade A, and why
analytics-report scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 267 lines — stays where its author put it; the contents beside it link to each section on GitHub.
When to use
Trigger this skill when the request includes any of:
- "Run the weekly marketing analytics report"
- "What changed in our numbers last week?"
- "Why did organic traffic drop on Tuesday?"
- "Summarize email and social performance for the Friday standup"
- A scheduled Friday-afternoon job
Do not use for deep attribution analysis (separate multi-touch modeling project), real-time dashboards (live BI tool territory), or ad-hoc queries that would be faster answered directly in the source tool.
Inputs
Required:
- Reporting period — defaults to "last completed week, Monday through Sunday." Accepts custom ranges.
- Data sources — which of GA4, Mixpanel, HubSpot, LinkedIn, X, and email platform the report should cover.
Optional:
- Previous week's report — enables consistent format and trend continuity.
- Known events — launches, outages, holidays, or large campaigns that landed during the period. These are context that changes the interpretation.
- Focus metrics — if a specific KPI is in focus this quarter (e.g., "CVR on the pricing page"), flag it.
Outputs
A one-page Markdown report with the following sections:
- Header — reporting period, data sources, known context (launches, outages, campaigns).
- Top-line scoreboard — 6-10 KPIs with this week's value, previous week's value, absolute delta, and percentage delta.
- What changed — 3-5 bullets calling out the largest movements, each tagged as signal or noise.
- Why we think — a hypothesis for each signal-tagged change, with confidence level (high/medium/low) and the evidence behind it.
- What to do next — 1-3 specific actions for the coming week, each with an owner and a success criterion.
- Noise log — brief note on the noise-tagged movements and why they are being dismissed. Important for auditability.
Tool dependencies
- GA4 MCP (or manual export) — sessions, users, engagement rate, conversions.
- Mixpanel MCP (or manual export) — product-level events (signup, activation, feature usage).
- HubSpot MCP (or manual export) — email open/click, list growth, MQL/SQL counts, pipeline contribution.
- LinkedIn and X analytics — impressions, engagement rate, profile visits, follower growth.
- Email platform analytics — open rate, CTR, unsubscribe rate, deliverability.
- Optional: Slack MCP to post the report into
#marketingon Friday afternoon.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago First seen · 267 lines · 63 tokens per session scan A f41a050449d1
analytics-report is a skill published in the GitHub repository tahirraufkeeyu/software-development-agent-stack--sdas (18 stars, last pushed 4mo ago), licensed MIT. It adds 63 tokens to every session and 2,902 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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