skills/opportunity-scan/SKILL.md

---
name: opportunity-scan
description: >
  Scan Sealmetrics data for revenue opportunities — money left on the table.
  Trigger on: "where am I losing money", "find opportunities", "what should
  I optimize", "how can I improve my campaigns", "dónde pierdo dinero",
  "what would you change", "audit my marketing", or any open-ended
  optimization request.
short-description: 'Scan for revenue left on the table. Use for "where am I losing money", "find opportunities", "what should I optimize", "dónde pierdo dinero", "audit my marketing".'
---

# Opportunity Scan

Before writing your answer, read `examples/output.md` in this skill directory
and match its density, structure and tone. It is the reference for what a good
run of this skill looks like.

Run the pattern library against current data and report what fires.
Patterns and detection logic:
`skills/seal-copilot/references/opportunity-patterns.md` (14 patterns
covering revenue lift, friction repair, and waste reduction).
Thresholds: `skills/seal-copilot/references/methodology.md`.
Budget: ≤12 tool calls.

> Scope: this skill is a revenue-opportunity scan. For media-budget
> reallocation use `channel-mix-optimizer`; for operational waste use
> `cost-reduction`; for per-SKU PDP issues use `product-friction`. This
> skill cross-references those when a finding clearly belongs there.

## Procedure

0. **Read the ledger.** Load `<state-dir>/<site_id>/recommendations.jsonl`.
   Do not re-report a pattern that already has an `open` entry for the same
   subject unless its impact has grown ≥50% — then report it as an escalation
   and name the date it was first flagged. Entries marked `discarded` stay
   suppressed for 90 days. See `skills/seal-copilot/references/state-schema.md`.
1. Baseline (3 calls): `get_overview(30d, compare=previous)`,
   `get_top_channels(30d)`, `get_conversions(30d)` — site averages for CR and
   AOV, needed by every pattern.
2. Campaign patterns (1–2 calls): `get_campaigns(30d, sort_by=entrances,
   limit=50)` — screen for patterns 1 (leaky) and 2 (hidden star) in one
   pass.
3. Landing pattern (1 call): `get_landing_pages(30d, sort_by=bounce_rate)`
   — pattern 3.
4. Device pattern (1 call): `get_device_types(30d)` — pattern 4.
5. Property pattern (2 calls): `list_property_keys` →
   `get_property_breakdown` on the most business-relevant key — pattern 7.
   For ecommerce, if a product property exists (`sku`, `product_id`,
   `item_id`, `product_name`), additionally screen pattern 11 (catalog
   friction) by computing the view→AtC ratio across the top viewed SKUs;
   if it fires, recommend the full `product-friction` skill for depth.
6. Pick at most 2 more patterns based on vertical: content-group mismatch (14)
   via `get_content_groups` for blog-heavy or SaaS accounts, terms (5) for heavy SEM
   users, countries (6) for international sites, micro→macro (10) if
   microconversions are tracked, RPE gap (12) for accounts running
   multiple paid channels, intraday gap (13) only if a watchdog baseline
   already exists (`calibrate-watchdog` has run).
7. Validate any anomaly with `get_bot_stats(days=30)` — pattern 9 — before
   reporting. An empty result means agent analytics is off, not 0% bots:
   mark the finding "unvalidated for bots".

## Output format

**Max 3 opportunities, ordered by estimated revenue impact.** Each has all
five parts below; none is optional. Two runs out of three dropped the last one
when the finding felt obvious — a recommendation without a way to check it is
an opinion, and it cannot go into the ledger.

- **Name + pattern** (e.g. "Hidden star: campaign summer-sale-es")
- **Evidence:** the numbers, the period, vs what baseline
- **Action:** specific and executable this week
- **Impact:** estimated €/month with the assumption stated
- **Verify:** the tool to re-run, the metric that should move, and when
  (2–4 weeks; one booking cycle for hotels). Write the word "Verify".

Do not report how many tool calls you used.

Then one line listing patterns checked that did NOT fire (transparency
builds trust), and one line for any pattern suppressed as an already-open
recommendation. If fewer than 30 conversions in a cell, label the finding
"directional — low sample" instead of dropping it silently.

Append each reported opportunity to `recommendations.jsonl` with its metric,
baseline, target and `verify_on` date. Log the run in `runs.jsonl`.