Building a Keyword Opportunity Agent: Weekly ASO Briefs From Live Data
Most ASO teams are drowning in data they check inconsistently. App Store Connect exports, third-party rank trackers, competitor movement alerts, and review sentiment — all of it sitting in separate tabs, reviewed whenever someone remembers to look. The result is predictable: a keyword opportunity spikes for a week, nobody acts on it, and it closes before a metadata update ships.
An ASO keyword opportunity agent fixes this by making the data pull automatic and the synthesis opinionated. Instead of a human checking five dashboards, the agent checks them on a schedule, compares this week's numbers to last week's baseline, and writes a brief that surfaces the three to five things actually worth acting on. The human's job shrinks to reviewing a Slack message and deciding what to update.
This post covers the architecture, data sources, prompt design, and failure modes for building that agent. We're not selling a SaaS tool — we're walking through how to build the real thing.
What the Agent Actually Does
Before touching code, be explicit about the agent's scope. Scope creep is the fastest way to build something that doesn't ship.
The agent's job is narrow: once per week, pull keyword rank data for a defined set of target keywords, compare it to the prior week, identify meaningful movement (up or down), cross-reference search volume estimates, and write a structured brief that tells the ASO manager what changed and why it might matter.
It does not automatically update metadata. It does not push changes to App Store Connect. It is a briefing agent, not an execution agent. This matters because ASO metadata changes require human judgment about brand voice, character limits, and competitive positioning. Automation that skips that judgment creates risk.
The output looks like this:
Week of Oct 27 ASO Brief — [App Name]
- "meal plan app" jumped from rank 14 → 8 on iOS US. Competitor [X] dropped from 6 → 11 in the same window. Worth updating subtitle to include this phrase.
- "grocery list builder" is flat at rank 22 but search volume estimate increased ~15%. Monitor for two more weeks before acting.
- "nutrition tracker free" fell from 11 → 18. Likely driven by a new competitor entry. Review their metadata before the next update cycle.
That's the target output. Everything below is how you produce it reliably.
Data Sources and API Strategy
The agent needs three types of data: rank data, search volume estimates, and competitor snapshots.
| Data Type | Source Options | Notes |
|---|---|---|
| Keyword rank (iOS) | AppFollow API, AppTweak API, Sensor Tower API | AppTweak has the most granular daily rank history; AppFollow is cheaper for smaller keyword sets |
| Keyword rank (Android) | Same vendors, Google Play Console (limited) | Google Play Console doesn't surface keyword-level rank directly; third-party vendors are necessary |
| Search volume estimates | AppTweak, MobileAction, Sensor Tower | These are estimates, not Google-scale actuals. Label them as estimates in the brief. |
| Competitor rank changes | Same rank vendors, filtered by app category + keyword overlap | Pull top 10 ranked apps per keyword and flag new entrants |
| Review sentiment (optional) | App Store Connect RSS, AppFollow, Appbot | Useful context for rank drops — if 1-star reviews spiked, that's a factor |
The agent queries these APIs via HTTP tool calls. Most rank tracker APIs return JSON with a rank field per keyword per date. You need at minimum two data points per keyword: the current week's rank and the prior week's rank.
Don't over-index on real-time data. Weekly granularity is sufficient for ASO. Daily rank pulls exist if you want them, but daily ASO briefs create alert fatigue and the signal-to-noise ratio collapses. Run the agent on a Monday morning cron so the brief lands when the team is planning the week.
Agent Architecture
The agent runs as a scheduled workflow, not a conversational agent. It doesn't need a user-facing interface. Here's the component breakdown:
Scheduler: A cron job (GitHub Actions works fine; Lambda with EventBridge is more robust at scale) triggers the agent every Monday at a configurable time.
Data fetcher: A set of API tool calls that pull rank data for the keyword list. The keyword list lives in a config file or a simple database table so the ASO manager can add/remove keywords without touching code.
Comparator: A deterministic function (not LLM-driven) that calculates rank delta for each keyword: delta = prior_rank - current_rank. Positive delta means the app moved up. This step is pure math — don't use an LLM for arithmetic.
Opportunity scorer: A lightweight scoring function that weights rank movement by estimated search volume. A keyword jumping 10 positions in a high-volume category matters more than the same jump in a niche category. The scorer produces a ranked list of keywords worth surfacing in the brief.
LLM synthesis layer: The top N keywords (typically five to eight) from the scorer get passed to an LLM with a structured prompt. The LLM's job is to write human-readable commentary — not to decide which keywords matter (the scorer already did that).
Delivery: The brief goes to Slack via webhook, or to email, or both. Include the raw data table as an attachment for anyone who wants to dig deeper.
If you're building AI agents for your app's growth stack, our mobile app marketing team has shipped ASO workflows on top of stacks like this — we can help you figure out what's worth automating and what isn't.
Prompt Design for the Synthesis Layer
The prompt is where most keyword agents fail. Generic prompts produce generic briefs that nobody reads after the third week. The prompt needs to be opinionated and context-aware.
System prompt (abbreviated):
You are an ASO analyst writing a weekly keyword brief for [App Name], a [category] app targeting [primary audience] on iOS and Android US.
Your brief must:
- Lead with the two or three most actionable findings, not the most dramatic rank changes
- Distinguish between rank changes likely caused by competitor movement vs. likely caused by the app's own metadata or rating changes
- Flag any keyword where estimated search volume increased but rank is flat — this is an underexploited opportunity
- Never speculate beyond the data provided. Use "likely," "possibly," "worth monitoring" for uncertain attributions.
- Write each bullet in one to two sentences. No padding.
Output format: A bulleted list of findings, followed by a "Monitor" section for keywords with ambiguous signals.
User message (constructed dynamically):
Pass the keyword table as structured text — keyword name, prior rank, current rank, delta, estimated search volume tier (high/medium/low), and any competitor entries or exits in the top 10. The LLM does not need raw API JSON. Pre-format it.
Context window management matters here. If you're tracking hundreds of keywords, don't pass all of them. Pass only the ones the scorer flagged. For a deeper look at managing context limits in long-running agents, see our post on AI agent context windows and token limits.
Temperature: Set to 0.3 or lower. You want consistency across weeks, not creative variation. The ASO manager should be able to skim the brief in 90 seconds — unpredictable formatting breaks that.
Handling Failure Gracefully
Scheduled agents that fail silently are worse than no agent at all. If the agent runs but produces nothing, the team assumes everything is fine. It isn't.
Build explicit failure handling into every layer:
- API failure: If the rank data API returns an error or incomplete data, the agent should send a brief that says "Data pull incomplete for [reason] — skipping this week's synthesis. Manual check recommended." Don't send a partial brief dressed up as a complete one.
- LLM timeout or refusal: Log it, send a fallback message with the raw data table, and alert the owner. The raw table is better than silence.
- No significant movement: If the scorer finds nothing above your movement threshold, the brief should say exactly that — "No keyword moved more than three positions this week. No action recommended." This is a valid output.
For a broader treatment of what breaks agents in production, our post on agent failure modes covers the patterns we see repeatedly across engagements.
Keyword List Curation and Maintenance
The agent is only as good as the keyword list it monitors. A poorly curated list produces noisy, irrelevant briefs.
Seed the list with three tiers:
- Core keywords — the terms you're actively optimizing your title, subtitle, and keyword field around. These should always be in the list.
- Opportunity keywords — terms where you rank between positions 8 and 20 and search volume is meaningful. These are the ones most likely to respond to metadata changes.
- Competitor keywords — terms where a direct competitor ranks in the top 5 and you don't rank at all or rank poorly. Track these to catch when their position weakens.
Revisit the list quarterly. Keywords that have been flat for three months with no volume change are probably wasting API quota.
List size: In our experience, 40 to 80 keywords is the practical ceiling for a weekly brief that stays readable. Beyond that, the scoring and synthesis layers start losing resolution — everything looks equally important, which means nothing is actionable.
FAQ
Do I need a paid ASO platform to build this agent?
You need access to keyword rank data, which means either a paid ASO API (AppTweak, AppFollow, Sensor Tower) or a scraping approach that violates most platforms' terms of service. Budget for one of the legitimate APIs. The cost is typically in the range of a few hundred dollars per month depending on keyword volume and query frequency — far less than the hours saved on manual pulls.
Can this agent work for both iOS and Android simultaneously?
Yes, but treat them as separate data streams. Rank signals on the App Store and Google Play don't correlate reliably. Run separate scoring passes for each platform and combine them in the brief with clear labeling. A keyword opportunity on iOS is not automatically an opportunity on Android.
How long does it take to build the first version?
A working prototype — scheduler, one rank API integrated, basic LLM synthesis, Slack delivery — typically takes one to two weeks of focused engineering time. A production-hardened version with failure handling, config management, and multi-platform support is closer to four to six weeks. The difference is mostly edge case handling, not core logic.
How do I know if the brief is actually driving metadata updates?
Track it explicitly. Add a simple log where the ASO manager marks each brief's recommendations as "acted on," "deferred," or "dismissed." After eight weeks, you'll know whether the agent is surfacing genuinely actionable signals or producing noise. If the "dismissed" rate is above roughly 60%, revisit the scoring weights and prompt.
Should the agent automatically suggest specific metadata copy?
We'd recommend against it in v1. Metadata copy involves brand voice, character limit constraints, and A/B testing strategy that require human context. The agent's job is to identify where to focus, not to write the subtitle. Add copy suggestions as an optional mode once the team trusts the briefing layer.
What LLM should I use for the synthesis layer?
GPT-4o and Claude 3.5 Sonnet both perform well for this task. For a scheduled weekly agent with low output volume, cost is negligible — this isn't a high-token-volume use case. Pick whichever your team already has API access to. The prompt design matters more than the model choice at this scale.
If you're ready to move beyond manual ASO workflows and want a team that's built this kind of agent on real app growth stacks, talk to our mobile app marketing team about what an automated ASO briefing layer could look like for your app. Or skip the form and book 30 minutes directly — we'll tell you quickly whether this is the right build for your stage.
