# GEO Content Gap - AI Editorial Brief Generator vs Competitors (`dltik/geo-content-gap`) Actor Find content gaps where AI mentions competitors but not your brand, then auto-generate editorial briefs (H1, must-cover points, target keywords) per gap. Multi-LLM consensus across ChatGPT and Claude. Ready-to-write briefs, not raw audits. - **URL**: https://apify.com/dltik/geo-content-gap.md - **Developed by:** [Walid](https://apify.com/dltik) (community) - **Categories:** SEO tools, Marketing, AI - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $0.00005 / actor start This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## GEO Content Gap — AI Editorial Brief Generator vs Competitors **GEO Content Gap** finds the exact prompts where AI cites your competitors but **not** your brand — and turns each gap into a **ready-to-write editorial brief** (H1, must-cover points, target keywords). Multi-LLM consensus across **ChatGPT and Claude**. Stop guessing what content to publish to get cited by AI. > Built for SEO leads, content strategists and growth teams. **Output editorial briefs, not raw audits.** > ⭐ **Found this useful?** Click the **Bookmark** button at the top of [this page](https://apify.com/dltik/geo-content-gap) — it helps the scraper stay visible to others who need it. Takes 1 click. No signup beyond your existing Apify account. --- ### What can GEO Content Gap do? - 🔎 **Detect AI citation gaps** — for every prompt, see if AI mentions you, your competitors, or neither - ⚔️ **Competitor mention delta** — exact count of competitor citations vs your own, per prompt - 📝 **Auto-generated content brief per gap** — recommended H1, intent, must-cover points, target keywords - 🧠 **Topic cluster grouping** — find recurring themes across gaps to prioritize content roadmaps - 🏆 **Priority ranking** — gaps sorted by competitor mention strength so you publish the highest-leverage content first - 🤖 **Multi-LLM consensus** — GPT-4o Mini and Claude Haiku queried in parallel for stable detection - 📊 **vs Competitor breakdown** — share of voice per competitor across your prompt set - 💸 **Cheapest gap-with-brief actor on Apify Store** — same tier as premium audits, at one flat price per prompt --- ### What data can you extract per gap? | Field | Description | |-------|-------------| | `brand_domain` | Your brand domain or name being audited | | `prompts_analyzed` | Total number of prompts queried against the LLMs | | `llms_tested` | Array of OpenRouter model IDs used (default: GPT-4o Mini + Claude Haiku) | | `gap_prompts` | Priority-ranked array of gap prompts, each with full content brief | | `competitor_mentions_by_brand` | Dict: total mention count per competitor across all prompts | | `vs_competitor_breakdown` | Per-competitor share of voice and prompts mentioned in | | `user_brand_mention_count` | Times your brand was mentioned across all responses | | `gap_score` | % of prompts where competitors won but you weren't mentioned | | `topic_clusters` | Theme groupings of detected gaps with gap_count per cluster | | `content_brief` | Top-priority brief at root level: h1, intent, must_cover_points, target_keywords, topic_cluster | | `recommended_h1` | Catchy H1 for the highest-priority gap (≤70 chars) | | `must_cover_points` | 5-7 concrete points the article must address to compete | | `target_keywords` | 4-6 SEO target keywords for the top-priority gap | | `priority_rank` | 1 = highest leverage gap to publish | | `raw_responses` | First 30 raw LLM responses (truncated) for transparency | | `date_iso` | UTC ISO timestamp of the run | --- ### How to find your AI content gaps 1. **[Create a free Apify account](https://apify.com/sign-up)** — no credit card required 2. **Open [GEO Content Gap](https://apify.com/dltik/geo-content-gap)** in Apify Store 3. **Enter your brand domain** (e.g. `notion.so`) and **competitor names** (e.g. `Asana`, `ClickUp`) 4. **List the natural-language prompts** users would ask AI in your niche 5. **Click Start** — gap detection + brief generation in 1–2 minutes 6. **Open the dataset** to get a ranked list of gaps each with a content brief ready to hand to your writers --- ### How much does AI content gap analysis cost? **$0.50 per prompt analyzed** via Apify Pay-Per-Event. No surprise compute charges — flat per prompt, regardless of how many LLMs respond. | Run | Prompts | Apify cost | LLM consensus | |-----|---------|------------|---------------| | Quick (5 prompts) | 5 | ~$2.50 | GPT-4o Mini + Claude Haiku | | Standard (15 prompts) | 15 | ~$7.50 | GPT-4o Mini + Claude Haiku | | Deep (25 prompts) | 25 | ~$12.50 | GPT-4o Mini + Claude Haiku | | Massive (100 prompts) | 100 | ~$50.00 | GPT-4o Mini + Claude Haiku | > A traditional content gap audit from an SEO agency costs $1,500–$5,000 for a similar deliverable. GEO Content Gap delivers the same prioritized briefs from $2.50. > 🆚 Compared to `apilab/ai-content-gap-agent` ($6.90–$10.20 per result, SERP-only no LLM), `khadinakbar/llm-visibility-tracker` ($90/1K keyword checks, no briefs), and `constructive_calm/llm-visibility-monitor` ($150–$500/1K samples) — GEO Content Gap is the only one that delivers competitor-vs-brand gap detection AND ready-to-write editorial briefs at the per-prompt tier. --- ### Input parameters | Parameter | Type | Required | Default | Description | |-----------|------|----------|---------|-------------| | `brandDomain` | string | ✅ | — | Your brand domain or name (e.g. `notion.so`, `Notion`) | | `brandAliases` | array | ❌ | `[]` | Extra variants to match (e.g. `["Notion Labs", "Notion AI"]`) | | `competitors` | array | ✅ | — | Competitor brand names (e.g. `["Asana", "ClickUp"]`) | | `prompts` | array | ✅ | — | Natural-language prompts users would ask AI in your niche | | `industry` | string | ❌ | `general` | Industry context used when generating editorial briefs | | `llms` | array | ❌ | 2 models | Override which OpenRouter models to query | | `maxPrompts` | integer | ❌ | `25` | Safety cap on prompts processed in one run (1–200) | | `generateBriefs` | boolean | ❌ | `true` | If false, skip brief generation (saves 1 LLM call per chunk) | --- ### Output example ```json { "_type": "geo_content_gap", "brand_domain": "notion.so", "industry": "project management and team productivity", "prompts_analyzed": 4, "llms_tested": ["openai/gpt-4o-mini", "anthropic/claude-haiku-4.5"], "gap_score": 50.0, "n_gaps": 2, "user_brand_mention_count": 3, "competitor_mentions_by_brand": { "Asana": 6, "ClickUp": 4, "Monday": 5 }, "vs_competitor_breakdown": [ { "competitor": "Asana", "total_mentions": 6, "prompts_mentioned_in": 3, "share_of_voice_pct": 75.0 }, { "competitor": "Monday", "total_mentions": 5, "prompts_mentioned_in": 2, "share_of_voice_pct": 50.0 }, { "competitor": "ClickUp", "total_mentions": 4, "prompts_mentioned_in": 2, "share_of_voice_pct": 50.0 } ], "topic_clusters": [ { "cluster": "remote team tools", "gap_count": 1 }, { "cluster": "all-in-one workspace", "gap_count": 1 } ], "gap_prompts": [ { "priority_rank": 1, "prompt": "Best project management tools for remote teams in 2026", "competitors_mentioned": ["Asana", "ClickUp", "Monday"], "competitor_mention_total": 5, "topic_cluster": "remote team tools", "intent": "comparison", "recommended_h1": "Best Project Management Tools for Remote Teams in 2026 — Notion vs Asana, ClickUp, Monday", "must_cover_points": [ "Side-by-side comparison table: Notion vs Asana vs ClickUp vs Monday on async collaboration features", "Use case: distributed teams across timezones with shared docs + tasks in one workspace", "Pricing per seat at 10, 50, 200 users for remote orgs", "Real example: a 100-person fully remote startup using Notion as the single source of truth", "Integration ecosystem: Slack, GitHub, Zoom — what Notion does natively vs competitors" ], "target_keywords": [ "best project management tools remote teams", "Notion for remote teams", "Notion vs Asana remote work", "async project management software" ], "rationale_30w": "AI currently cites only competitors for this query. A comparison-led article with Notion-first framing on async + docs-in-one earns the citation slot." } ], "recommended_h1": "Best Project Management Tools for Remote Teams in 2026 — Notion vs Asana, ClickUp, Monday", "must_cover_points": [ "Side-by-side comparison table", "Use case: distributed teams across timezones", "Pricing per seat at 10, 50, 200 users", "Real example: 100-person fully remote startup", "Integration ecosystem" ], "target_keywords": [ "best project management tools remote teams", "Notion for remote teams" ], "priority_rank": 1, "date_iso": "2026-06-04T10:00:00Z" } ```` *** ### Use cases - **Content strategy roadmap for SEO/GEO teams** — turn your AI gap audit into a prioritized editorial backlog with one click - **Agency monthly client deliverable** — run on a client domain, hand them a ranked list of briefs with rationale, charge them by deliverable - **Brand monitoring for B2B SaaS** — schedule monthly to catch new prompts where competitors gain ground in AI answers - **Pre-launch competitive moat** — for a new product, find every AI prompt where incumbents are cited and pre-write the comparison articles - **AEO / AI Engine Optimization brief generation** — fast-track content briefs designed specifically for AI citation, not just Google ranking - **Cross-sell from the GEO Visibility Score actor** — if you already know your overall AI visibility is low, this tells you *exactly which content to publish* to fix it *** ### Use GEO Content Gap via API **Python:** ```python import requests run = requests.post( "https://api.apify.com/v2/acts/dltik~geo-content-gap/run-sync-get-dataset-items", headers={"Authorization": "Bearer YOUR_APIFY_TOKEN"}, json={ "brandDomain": "notion.so", "competitors": ["Asana", "ClickUp", "Monday"], "prompts": [ "Best project management tools for remote teams in 2026", "Top Notion alternatives for small businesses", "Project management software with strong roadmap features", ], "industry": "project management and team productivity", }, timeout=300, ).json() print(run) ``` **curl:** ```bash curl -X POST "https://api.apify.com/v2/acts/dltik~geo-content-gap/runs" \ -H "Authorization: Bearer YOUR_APIFY_TOKEN" \ -H "Content-Type: application/json" \ -d '{ "brandDomain": "notion.so", "competitors": ["Asana", "ClickUp"], "prompts": ["Best project management tools for remote teams in 2026"], "industry": "project management" }' ``` *** ### FAQ **How is this different from a traditional content gap tool like SEMrush or Ahrefs?** SEMrush and Ahrefs find keywords your competitors rank for on Google. GEO Content Gap finds **prompts where AI** (ChatGPT, Claude, Gemini) currently recommends competitors instead of you — a fundamentally different surface. The competitor `apilab/ai-content-gap-agent` is SERP-based; this actor is LLM-citation-based, and it adds the editorial brief on top. **How do you avoid false negatives on brand-name matching (e.g. "Notion" vs "Notion Labs")?** Each brand and competitor name is normalized into multiple variants (raw, lowercased, with/without TLD, base form before suffixes like "Inc"/"Labs"). Matching uses word-boundary regex (no partial substring inside `notional` etc.). You can also pass `brandAliases` to add manual variants like `Notion Labs` or `Notion AI`. **What if the same LLM mentions me one run and not the next?** LLMs are stochastic. GEO Content Gap defaults to **2-LLM consensus** with temperature 0.3, which is the sweet spot between cost and stability. For maximum signal, run twice and treat persistent gaps as the priority backlog. **How do you generate the editorial briefs?** After gap detection, we batch all gap prompts (5 per call) and ask GPT-4o Mini to produce structured briefs (H1, intent, must-cover points, target keywords, rationale). Batching keeps the cost negligible — under $0.005 of LLM cost per brief. **Can I disable brief generation if I only want the gap detection?** Yes. Set `generateBriefs` to `false`. You still get the full gap list and competitor mention breakdown, just without the editorial briefs. **I need help or a custom solution.** Open an issue on the [Issues tab](https://apify.com/dltik/geo-content-gap/issues) or contact us through Apify. *** ### Connect with Make, Zapier & n8n This actor integrates with any automation platform via the Apify API. #### Make (Integromat) 1. Add an **Apify module** in your Make scenario 2. Select **Run Actor** and choose `dltik/geo-content-gap` 3. Configure the input (paste your JSON or build it dynamically) 4. Add a **Get Dataset Items** module to retrieve the ranked gap briefs 5. Pipe each brief to Notion, Trello, Linear, Google Docs or your CMS #### Zapier 1. Use the **Apify integration** on Zapier 2. Trigger: **Actor Run Finished** 3. Action: **Get Dataset Items**, then iterate over `gap_prompts` 4. Push each brief into your editorial calendar (Asana, ClickUp, Airtable) #### n8n 1. Add an **HTTP Request** node calling `https://api.apify.com/v2/acts/dltik~geo-content-gap/runs` 2. Wait for completion, then fetch dataset items 3. Loop over `gap_prompts` and create a draft per gap in your CMS #### Webhooks ```python run = client.actor("dltik/geo-content-gap").call( run_input={...}, webhooks=[{ "eventTypes": ["ACTOR.RUN.SUCCEEDED"], "requestUrl": "https://your-webhook-url.com", }], ) ``` *** ⭐ **Found GEO Content Gap useful? Bookmark it** — Apify ranks actors by bookmarks, so it's the strongest signal for Store visibility. One click = directly helps this actor stay surfaced for new users. *** ### Complete your GEO analysis with the full suite | Actor | What it does | Start with this if... | |-------|-------------|----------------------| | ✅ [GEO Site Audit](https://apify.com/dltik/geo-site-audit) | Technical AI readiness score | **Always start here** | | 📊 [GEO Visibility Score](https://apify.com/dltik/geo-visibility-score) | GEO Score, mention rate, citation rate | You want your baseline AI visibility | | ⚔️ [GEO Competitor Research](https://apify.com/dltik/geo-competitor-research) | Share of Voice vs competitors | You know which competitors to track | | 🔍 [GEO Prompt Research](https://apify.com/dltik/geo-prompt-research) | AI keyword research & prompt scoring | You want to find new AI ranking opportunities | | 💬 [GEO Brand Sentiment](https://apify.com/dltik/geo-brand-sentiment) | How AI models describe your brand | You want to understand AI brand perception | | 📝 [GEO Content Gap](https://apify.com/dltik/geo-content-gap) | Editorial briefs per AI citation gap | You know your gaps, want the briefs to fix them | *** ### Other scrapers by dltik | Actor | What it does | Price | |-------|-------------|-------| | [GEO Visibility Score](https://apify.com/dltik/geo-visibility-score) | Measure your brand's visibility across ChatGPT, Claude, Gemini | $0.05/response | | [GEO Brand Sentiment](https://apify.com/dltik/geo-brand-sentiment) | How AI models describe your brand: sentiment, strengths, weaknesses | $0.05/response | | [Google Maps Email Extractor](https://apify.com/dltik/google-maps-email-extractor) | Extract emails, phones, WhatsApp from Google Maps businesses | $25/1K | | [TikTok Scraper](https://apify.com/dltik/tiktok-scraper) | Scrape profiles, videos, hashtags, search, trending | $1/1K | | [Reddit Scraper](https://apify.com/dltik/reddit-scraper) | Scrape posts, comments, profiles with sentiment analysis | $2/1K | | [HackerNews MCP Server](https://apify.com/dltik/mcp-server-hackernews) | HN search for AI agents — tech audience research | $5/1K | # Actor input Schema ## `brandDomain` (type: `string`): Your brand domain or name (e.g. 'notion.so', 'Notion', 'apify.com'). Used for word-boundary matching in LLM responses. ## `brandAliases` (type: `array`): Extra brand variants to match (e.g. \['Notion Labs', 'Notion AI']). Each alias is matched case-insensitively with word boundaries. ## `competitors` (type: `array`): Competitor brand names to detect in AI responses (e.g. \['Asana', 'ClickUp', 'Monday']). A gap is found when competitors are mentioned and your brand is not. ## `prompts` (type: `array`): List of natural-language prompts users would ask AI in your niche (e.g. 'Best project management tools for remote teams'). Each prompt is charged once as 'gap-prompt-analyzed'. ## `industry` (type: `string`): Your industry / niche, used as context when generating editorial briefs (e.g. 'project management', 'CRM for SaaS startups'). ## `llms` (type: `array`): OpenRouter model IDs to query for gap detection. Default uses 2 cheap fast models for consensus. ## `maxPrompts` (type: `integer`): Hard cap on number of prompts processed in this run (safety knob to control cost). Extra prompts are ignored. ## `generateBriefs` (type: `boolean`): If true (default), generates a content brief (H1, must-cover points, target keywords) for each detected gap. Disable to save 1 extra LLM call per run. ## Actor input object example ```json { "brandDomain": "notion.so", "competitors": [ "Asana", "ClickUp", "Monday" ], "prompts": [ "Best project management tools for remote teams in 2026", "Top Notion alternatives for small businesses", "Project management software with strong roadmap features", "Best all-in-one workspace for startups" ], "industry": "project management and team productivity", "llms": [ "openai/gpt-4o-mini", "anthropic/claude-haiku-4.5" ], "maxPrompts": 25, "generateBriefs": true } ``` # Actor output Schema ## `results` (type: `string`): Full gap report with priority-ranked editorial briefs per gap topic. # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "brandDomain": "notion.so", "competitors": [ "Asana", "ClickUp", "Monday" ], "prompts": [ "Best project management tools for remote teams in 2026", "Top Notion alternatives for small businesses", "Project management software with strong roadmap features", "Best all-in-one workspace for startups" ], "industry": "project management and team productivity" }; // Run the Actor and wait for it to finish const run = await client.actor("dltik/geo-content-gap").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "brandDomain": "notion.so", "competitors": [ "Asana", "ClickUp", "Monday", ], "prompts": [ "Best project management tools for remote teams in 2026", "Top Notion alternatives for small businesses", "Project management software with strong roadmap features", "Best all-in-one workspace for startups", ], "industry": "project management and team productivity", } # Run the Actor and wait for it to finish run = client.actor("dltik/geo-content-gap").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "brandDomain": "notion.so", "competitors": [ "Asana", "ClickUp", "Monday" ], "prompts": [ "Best project management tools for remote teams in 2026", "Top Notion alternatives for small businesses", "Project management software with strong roadmap features", "Best all-in-one workspace for startups" ], "industry": "project management and team productivity" }' | apify call dltik/geo-content-gap --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=dltik/geo-content-gap", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "GEO Content Gap - AI Editorial Brief Generator vs Competitors", "description": "Find content gaps where AI mentions competitors but not your brand, then auto-generate editorial briefs (H1, must-cover points, target keywords) per gap. Multi-LLM consensus across ChatGPT and Claude. Ready-to-write briefs, not raw audits.", "version": "1.0", "x-build-id": "6zLZ9Fe6iqOwkfZiW" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/dltik~geo-content-gap/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-dltik-geo-content-gap", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/dltik~geo-content-gap/runs": { "post": { "operationId": "runs-sync-dltik-geo-content-gap", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/dltik~geo-content-gap/run-sync": { "post": { "operationId": "run-sync-dltik-geo-content-gap", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "brandDomain", "competitors", "prompts" ], "properties": { "brandDomain": { "title": "Your brand domain", "type": "string", "description": "Your brand domain or name (e.g. 'notion.so', 'Notion', 'apify.com'). Used for word-boundary matching in LLM responses." }, "brandAliases": { "title": "Brand aliases (optional)", "type": "array", "description": "Extra brand variants to match (e.g. ['Notion Labs', 'Notion AI']). Each alias is matched case-insensitively with word boundaries.", "items": { "type": "string" } }, "competitors": { "title": "Competitor brands", "type": "array", "description": "Competitor brand names to detect in AI responses (e.g. ['Asana', 'ClickUp', 'Monday']). A gap is found when competitors are mentioned and your brand is not.", "items": { "type": "string" } }, "prompts": { "title": "Prompts to analyze", "type": "array", "description": "List of natural-language prompts users would ask AI in your niche (e.g. 'Best project management tools for remote teams'). Each prompt is charged once as 'gap-prompt-analyzed'.", "items": { "type": "string" } }, "industry": { "title": "Industry context", "type": "string", "description": "Your industry / niche, used as context when generating editorial briefs (e.g. 'project management', 'CRM for SaaS startups')." }, "llms": { "title": "LLMs to query", "type": "array", "description": "OpenRouter model IDs to query for gap detection. Default uses 2 cheap fast models for consensus.", "items": { "type": "string" }, "default": [ "openai/gpt-4o-mini", "anthropic/claude-haiku-4.5" ] }, "maxPrompts": { "title": "Max prompts to process", "minimum": 1, "maximum": 200, "type": "integer", "description": "Hard cap on number of prompts processed in this run (safety knob to control cost). Extra prompts are ignored.", "default": 25 }, "generateBriefs": { "title": "Generate editorial briefs", "type": "boolean", "description": "If true (default), generates a content brief (H1, must-cover points, target keywords) for each detected gap. Disable to save 1 extra LLM call per run.", "default": true } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```