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 petrycz/ecommerce-mcp-automation --skill daily-reportgit clone --depth 1 https://github.com/petrycz/ecommerce-mcp-automationWrote 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/petrycz/ecommerce-mcp-automation/daily-report)<a href="https://agentmods.dev/skills/petrycz/ecommerce-mcp-automation/daily-report"><img src="https://agentmods.dev/badge/skills/petrycz/ecommerce-mcp-automation/daily-report/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/petrycz/ecommerce-mcp-automation/daily-report"><img src="https://agentmods.dev/badge/skills/petrycz/ecommerce-mcp-automation/daily-report.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00057 | $0.00629 |
| Opus 5 | $0.00028 | $0.00315 |
| Sonnet 5 | $0.00011 | $0.00126 |
| Haiku 4.5 | $0.00006 | $0.00063 |
Grade A, and why
daily-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 10d 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 — 55 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Daily P&L + Ad Performance Report
Produces a formatted spreadsheet combining same-day Shopify revenue/COGS/gross profit with Meta Ads spend/ROAS — the workflow this whole repo exists to demonstrate. No manual copy-paste: both sources are pulled and written to the workbook in one run.
When to use this skill
Trigger on requests like "run the daily report," "what's today's P&L," "pull
this week's ad performance," "regenerate the report," or "how's ROAS looking
today." Not for one-off questions about a single number — for those, call the
get_daily_pnl / get_daily_ad_performance MCP tools directly instead of
running the full workbook build (this requires the MCP servers to be
registered — see the README's "As MCP servers" section. The full report path
below does not need MCP config; it runs the client code directly).
Running it
python -m ecommerce_mcp.reporting.daily_report [output_path]
- Defaults to
examples/sample_daily_report.xlsxif no path is given. - Defaults to mock mode (
MOCK_MODE=true) — runs with zero credentials against realistic fixtures. - For live data: set
SHOPIFY_STORE_DOMAIN/SHOPIFY_ACCESS_TOKENand/orMETA_ACCESS_TOKEN/META_AD_ACCOUNT_IDin.env(see.env.example). Each integration switches to live independently the moment its credentials are present — you don't need both to go live at once.
After running, report back the key numbers from the Summary sheet (revenue, COGS, gross profit, ad spend, net profit, blended ROAS) rather than just saying the file was written — that's the actual answer to "how are we doing today."
Architecture notes (for extending this skill)
src/ecommerce_mcp/clients/shopify_client.py/meta_ads_client.py— typed API clients, real endpoints and pagination, mock/live swappable viaclients/http.py.src/ecommerce_mcp/mcp_servers/— the same clients exposed as MCP tools, for ad hoc questions in a chat rather than a full report run.src/ecommerce_mcp/reporting/daily_report.py— orchestrates both clients concurrently and callsspreadsheet.pyto build the workbook.
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.
- 10d ago First seen · 55 lines · 57 tokens per session scan A f864714ae469
daily-report is a skill published in the GitHub repository petrycz/ecommerce-mcp-automation (0 stars, last pushed 15d ago), licensed MIT. It adds 57 tokens to every session and 629 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 2026-08-31.
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