| name | acquisition-trends |
| description | Use this skill when the user wants to analyze acquisition or performance trends over multiple months — new invitees per month, conversion rate trajectory, monthly commission, daily-active-trader trends. Triggers: "拉新趋势", "近 N 个月的拉新", "monthly trend", "show me the last 6 months", "DAT history", "when was my peak", "analyze acquisition over time", "what's the YoY". Do NOT use for single-day briefings (use `daily-briefing`) or single-user history (use `whale-deep-dive`).
|
Acquisition trends — multi-month performance analysis
"分析下我过去 6 个月的拉新情况" / "show me the last 12 months"
Time-series view of the affiliate node: new invitees, activation rates, deposits, volume,
commission, and active-trader counts. Surfaces inflection points and the events behind
them.
When to use this skill
- User asks for trends, history, MoM, QoQ, YoY analysis
- Quarterly review / pitch deck preparation
- Investigating a sudden change ("why is commission down 30% this month?")
- Replicating a past success ("what did we do in August?")
What the agent does
Step 1 — Choose the window
Default to 12 months if user says "trend" / "history" without a number. For specific
requests like "last 6 months" or "Q1 2026" honor that exactly.
Step 2 — One call per calendar month
Loop over each month in the window:
{
"name": "okx-affiliate-performance-summary",
"arguments": {
"begin": "<month start ms>",
"end": "<month end ms>"
}
}
Cap parallelism at ≤ 5 concurrent calls; the endpoint rate-limits with code: 50011.
Insert a 200–500 ms delay between batches.
Step 3 — For finer-grained trends, pull daily data
If the user asks for daily-active-trader (DAT) history or wants to identify peak days, loop
day-by-day over the window:
{
"name": "okx-affiliate-performance-summary",
"arguments": {
"begin": "<day 00:00:00 UTC ms>",
"end": "<day 23:59:59 UTC ms>"
}
}
90 days = 90 calls. Run serially with sleep 0.4 between requests to avoid 429s. For >180
days, suggest the user request only monthly granularity unless they truly need the daily
resolution.
Step 4 — Cache aggregates
Save results as CSV (one row per period) for follow-up analysis without re-pulling:
date,invitees,firstTimeTrader,traderCnt,depAmt,vol,commission
Use the details[] array from the response to sum across SPOT/DERIVATIVE/BSC.
Insights to extract
- Direction of travel — first 30 days vs middle 30 vs last 30. Quote the deltas.
- Peak month and peak day — the highest value of each metric and the date
- Acquisition velocity vs commission lag — new invitees in month M often produce most
of their commission in months M+1 to M+3; surface this lag if it's pronounced
- Activation rate trend —
firstTimeTrader / invitees per month. Healthy nodes hold
this ratio above 40%; below 30% signals quality erosion
- Volume-per-trader trend — total
vol / traderCnt. Falling per-trader volume with
steady trader count = users trading smaller (market conditions or product-mix shift)
- Spot vs derivatives mix drift — if derivatives share is climbing, the user base is
getting more risk-tolerant; useful for product recommendations
- Concentration risk — % of monthly commission from the top 3 users. If > 50% the node
is fragile
Sample output format
📊 Acquisition trend — <START> to <END> (<N> months)
📈 Monthly snapshot
| Month | New inv | First-time | Activation | Deposit | Volume | Commission |
| 2025-06 | 338 | 135 | 40% | $2.13M | $756M | $61,970 |
| ...
📊 Quarterly view
• Q2 (M–M): X new / Y first / $Z commission — strongest acquisition quarter
• Q3 (M–M): ...
• ...
🏔️ Peaks
• Peak monthly new invitees: <N> in <YYYY-MM>
• Peak monthly commission: $X in <YYYY-MM>
• Peak DAT day: <N> traders on <YYYY-MM-DD>
🔍 Key insights
1. <inflection point with date and likely cause>
2. <activation rate trend>
3. <commission concentration>
4. <volume-per-trader drift>
📌 Suggested action
<one paragraph: what to replicate from the best period, what to watch in the current
period, where to focus the next 30 days>
📁 Raw data
CSV with daily/monthly rows saved at <path>. Useful for replay against BTC price,
campaign timestamps, etc.
Recommended follow-ups
- "Why did spike?" — pull the channel-level (
okx-affiliate-link-list) data for
that month to see if a specific link drove it
- "Cross-reference with BTC price" — pull a price chart from blockbeats and overlay
- "Who were the top users in ?" — re-run
high-potential-invitees with
the peak month's date range and see if they're still active
Gotchas
- Custom-range timestamps are Unix milliseconds in UTC.
int(datetime(2026,4,1).timestamp()*1000)
in Python. Off-by-year mistakes are common.
- Months have different lengths — when comparing MoM, normalize daily averages or be
explicit about absolute vs. per-day numbers.
- The endpoint returns 429 above ~5 RPS, then succeeds again seconds later. Build retry
logic with exponential backoff; do not panic on a single 429.
okx-affiliate-sub-affiliate-list is lifetime-only in the new MCP — for time-windowed
sub-affiliate analysis pull invitee-list with subAffiliateUid... but that filter is
currently broken, so client-side filtering is needed.- For long backfills, consider chunked monthly calls (12–24 calls) rather than daily
(300+ calls). The user usually wants the shape, not every day.
