A weekly revenue brief your LLM writes

The job: Monday morning, a short written brief on what happened last week and what it means — assembled from your own data, not from a dashboard nobody opens.

Who this is for: founders and heads of growth who want the number and the sentence explaining it.

---

Before you start

• You need an API key (sk_sig_…) — mint one under Exports & API in the dashboard. The plaintext is shown exactly once; store it as SIGNAL_API_KEY.
• You need a connected store, for anything touching revenue or orders. Without one those endpoints return empty rather than failing, which is easy to misread as "no sales".

---

Gather, then write

Two steps, deliberately separate. Gathering is deterministic and should not involve a model. Writing is the part a model is good at.

import json, requests
BASE = "https://app.signal.geysera.com/signal-api/v1"
H = {"Authorization": f"Bearer {READ_KEY}"}

brief_inputs = {
    "attribution": requests.get(f"{BASE}/attribution", params={"days": 7}, headers=H).json(),
    "attribution_prior": requests.get(f"{BASE}/attribution", params={"days": 14}, headers=H).json(),
    "recommendations": requests.get(f"{BASE}/recommendations", headers=H).json(),
    "top_accounts": requests.get(f"{BASE}/accounts",
                                 params={"page_size": 25}, headers=H).json(),
}

There is no "last week versus the week before" endpoint. Two windows and a subtraction is the honest way to get it, and it makes the comparison explicit rather than hidden inside a metric.

Ask the commerce questions in English

The REST API covers acquisition. For revenue, ask:

def ask(q):
    r = requests.post(
        "https://app.signal.geysera.com/agent-api/signal/copilot/ask",
        headers={"Authorization": f"Bearer {COPILOT_KEY}"},
        json={"question": q}, timeout=60)
    return r.json()

brief_inputs["revenue"] = ask("What was revenue last week compared with the week before?")
brief_inputs["products"] = ask("Which products sold most last week?")

Each returns answer for humans and trace[].data for machines, plus disclosures — caveats the system attached because they change how the number should be read. Pass the disclosures through. They are the difference between a brief and a misleading brief.

The prompt

Write a Monday brief for the founder of a company. Maximum 250 words.

Data:
{brief_inputs_json}

Rules:
- Lead with the single most important change, not a list.
- Every number you state must appear in the data above. If you want to state a
  percentage change, compute it from two numbers that are both there.
- Repeat any disclosure that affects how a number should be read.
- End with ONE thing to do this week, drawn from the recommendations payload,
  naming the evidence behind it.
- If the week was unremarkable, say that in one sentence. Do not manufacture a
  narrative.

That final rule is what makes the brief trustworthy over time. A brief that finds drama every week teaches the reader to ignore it.

Scheduling

Any cron. The gathering takes seconds; the copilot calls take tens of seconds, so give the job a couple of minutes.

0 7 * * 1  /usr/bin/python3 /opt/briefs/weekly.py | mail -s "Monday brief" you@company.com

What will go wrong

The copilot refuses. It does that rather than answer from data it does not have. refusal is prose explaining why; put it in the brief verbatim instead of dropping the section, so a missing number is visible rather than silently absent.

Windows that include a gap. If order sync was broken for two days, a 7-day total is understated and the copilot will say so in disclosures. A brief that drops disclosures will report a fall in revenue that did not happen.

Comparisons across a plan-cap boundary. total is capped at what your plan may resolve. If you crossed the cap mid-week, week-over-week visitor counts are not comparable and the difference is billing, not behaviour.

---

Next: find the pages that create pipeline · ask questions in natural language