# XavvyNess AI Quality Auditor (`xavvyness/xavvyness-audit-agent`) Actor Score any Apify actor or AI endpoint on 5 dimensions (XAQS 0–100): Completeness, Accuracy, Actionability, Structure, Cost-Efficiency. Returns grade A+ to F with specific improvement recommendations. Audit your actors before publishing. - **URL**: https://apify.com/xavvyness/xavvyness-audit-agent.md - **Developed by:** [XavvyNess](https://apify.com/xavvyness) (community) - **Categories:** AI, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $20.00 / 1,000 agent audit reports 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 ## XavvyNess Agent Quality Auditor (XAQS) **The first standardized quality auditing framework for AI agents.** Run any Apify actor or AI agent through a structured test suite and get a XAQS score (0–100) across 5 quality dimensions — with specific, actionable improvement recommendations. Use it on your own actors before publishing, or benchmark competitors. ### Demo > 🎬 **Video demo coming soon.** Upload `audit-agent.mp4` to YouTube, then run `python3 scripts/actor-video-gen.py --embed-readmes` to embed it here automatically. --- ### What It Does 1. Runs your agent with multiple test prompts (customizable) 2. Scores each response on 5 dimensions (20 points each = 100 total) 3. Generates a grade (A+ → F) with specific improvement recommendations 4. Returns a structured audit report ready for your README or team review **The 5 XAQS Dimensions:** | Dimension | What It Measures | |-----------|-----------------| | **Completeness** | Does it fully answer? No truncation, no "continued..." | | **Accuracy** | Factual, no hallucinations, no invented URLs or names | | **Actionability** | Concrete next steps, imperative recommendations | | **Structure** | Headers, bullets, code blocks — readable at a glance | | **Cost-Efficiency** | Output value per token, no bloat or padding | **Grade Scale:** - 90–100 → A+ (5-star, marketplace ready) - 80–89 → A (4-star, minor gaps) - 70–79 → B (3-star, needs work) - 60–69 → C (2-star, significant issues) - <60 → F (1-star, not ready for publishing) --- ### Input | Field | Type | Default | Description | |-------|------|---------|-------------| | `agentName` | string | `"My AI Agent"` | Human-readable agent name for the report | | `apifyActorId` | string | — | Apify actor ID to audit (e.g. `RBobzxRYFVgoX74uu`) | | `agentEndpoint` | string | — | HTTP endpoint if not an Apify actor | | `testSuite` | array | `[]` | Custom test cases (see below) | | `dimensions` | array | all 5 | Dimensions to score | | `runs` | integer | `3` | How many times to run each test (consistency scoring) | | `apifyToken` | string | — | Your Apify token (required for Apify actor audits) | #### Example — Audit an Apify actor ```json { "agentName": "My Research Agent", "apifyActorId": "RBobzxRYFVgoX74uu", "apifyToken": "apify_api_YOUR_TOKEN", "runs": 3 } ```` #### Example — Audit a specialized scraper (custom test suite) ```json { "agentName": "My Smart Extractor", "apifyActorId": "IN4O5pGUjye34xW0O", "apifyToken": "apify_api_YOUR_TOKEN", "testSuite": [ { "query": "Extract top stories from HN", "payload": { "urls": ["https://news.ycombinator.com"], "extractionPrompt": "Extract top 5 story titles and point scores", "outputFormat": "json" }, "expectedKeywords": ["title", "points"], "minLength": 100 } ], "runs": 2 } ``` *** ### Example Output ```json { "agentName": "XavvyNess Research Engine", "xaqsScore": 84, "grade": "A", "passedTests": 6, "totalTests": 9, "dimensions": { "completeness": { "score": 18, "max": 20, "notes": "Reports are thorough; one truncation detected on deep queries" }, "accuracy": { "score": 17, "max": 20, "notes": "Sources cited correctly; one hallucinated URL in 9 runs" }, "actionability": { "score": 16, "max": 20, "notes": "Key findings actionable; recommendations could be more specific" }, "structure": { "score": 19, "max": 20, "notes": "Excellent use of headers and numbered citations" }, "cost_efficiency": { "score": 14, "max": 20, "notes": "Occasional verbose preambles inflate token count" } }, "topIssues": [ "1/9 runs produced a hallucinated source URL — add URL validation", "Deep queries occasionally truncate before conclusion section", "Preamble text (2-3 sentences before ### Overview) adds no value — remove it" ], "recommendations": [ "Add output validation to catch empty or truncated reports before returning", "Strip preamble text from model output using regex before pushing to dataset", "Increase max_tokens for deep queries from 3500 to 5000" ], "auditedAt": "2026-04-08T23:00:00.000Z", "agent": "XavvyNess Agent Quality Auditor" } ``` *** ### Pricing **$0.02 per audit** ($20.00 per 1,000 audits) — PAY\_PER\_RESULT, charged only on successful completion. A standard audit (3 runs × 3 tests = 9 total agent calls) typically completes in 3–5 minutes. Failed runs are not charged. *** ### Who Should Use This - **Apify builders** — Audit your actors before publishing to the marketplace - **Businesses using AI agents** — Verify quality before going to production - **Researchers** — Benchmark agent quality systematically across providers - **Developers** — Catch regressions when updating an agent *** ### Custom Test Suites The default test suite sends `{ query, depth, format }` — works perfectly for research-style agents. For specialized actors (scrapers, analyzers, extractors), use the `testSuite` field to define custom `payload` objects that match your actor's actual input schema. Each test case supports: - `query` — human-readable test name - `payload` — exact input JSON sent to the actor (overrides default) - `expectedKeywords` — words that must appear in the output - `minLength` — minimum output length in characters - `maxLatencyMs` — maximum acceptable run time *** ### Integration ```javascript import { ApifyClient } from 'apify-client'; const client = new ApifyClient({ token: 'YOUR_APIFY_TOKEN' }); const run = await client.actor('g1HQWHIikrGNlt7RF').call({ agentName: 'My Agent', apifyActorId: 'YOUR_ACTOR_ID', apifyToken: 'apify_api_YOUR_TOKEN', runs: 3, }); const { items } = await client.dataset(run.defaultDatasetId).listItems(); console.log(`XAQS Score: ${items[0].xaqsScore}/100 — ${items[0].grade}`); console.log('Top issues:', items[0].topIssues); ``` *** ### About XavvyNess XavvyNess builds AI agents that do real work — and XAQS is how we hold them accountable. Run it on any agent, including ours. [More agents →](https://apify.com/javybar) # Actor input Schema ## `agentName` (type: `string`): Human-readable name of the agent being audited (for the report). ## `apifyActorId` (type: `string`): Apify actor ID to audit (e.g. 'RBobzxRYFVgoX74uu'). Leave blank to audit a custom HTTP endpoint. ## `agentEndpoint` (type: `string`): If not an Apify actor, provide the POST endpoint URL for the agent. Receives {query} in body, returns {response} or {output}. ## `testSuite` (type: `array`): Array of test cases. Each item: { query, payload, expectedKeywords, minLength, maxLatencyMs }. Use 'payload' to override the default {query} input for specialized actors (e.g. {urls:\[...]} for scrapers). Leave blank for default suite. ## `dimensions` (type: `array`): Which quality dimensions to score. Valid values: completeness, accuracy, actionability, structure, cost\_efficiency. ## `runs` (type: `integer`): How many times to run each test case (for consistency scoring). ## `apifyToken` (type: `string`): Required if auditing an Apify actor. Used to trigger runs and read results. Get yours at console.apify.com/account/integrations. ## Actor input object example ```json { "agentName": "My AI Agent", "testSuite": [], "dimensions": [ "completeness", "accuracy", "actionability", "structure", "cost_efficiency" ], "runs": 3 } ``` # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("xavvyness/xavvyness-audit-agent").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 = {} # Run the Actor and wait for it to finish run = client.actor("xavvyness/xavvyness-audit-agent").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 '{}' | apify call xavvyness/xavvyness-audit-agent --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=xavvyness/xavvyness-audit-agent", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "XavvyNess AI Quality Auditor", "description": "Score any Apify actor or AI endpoint on 5 dimensions (XAQS 0–100): Completeness, Accuracy, Actionability, Structure, Cost-Efficiency. Returns grade A+ to F with specific improvement recommendations. Audit your actors before publishing.", "version": "1.0", "x-build-id": "oIyjjtesoRWH9rTaD" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/xavvyness~xavvyness-audit-agent/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-xavvyness-xavvyness-audit-agent", "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/xavvyness~xavvyness-audit-agent/runs": { "post": { "operationId": "runs-sync-xavvyness-xavvyness-audit-agent", "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/xavvyness~xavvyness-audit-agent/run-sync": { "post": { "operationId": "run-sync-xavvyness-xavvyness-audit-agent", "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": [ "agentName" ], "properties": { "agentName": { "title": "Agent Name", "type": "string", "description": "Human-readable name of the agent being audited (for the report).", "default": "My AI Agent" }, "apifyActorId": { "title": "Apify Actor ID (optional)", "type": "string", "description": "Apify actor ID to audit (e.g. 'RBobzxRYFVgoX74uu'). Leave blank to audit a custom HTTP endpoint." }, "agentEndpoint": { "title": "Agent HTTP Endpoint (optional)", "type": "string", "description": "If not an Apify actor, provide the POST endpoint URL for the agent. Receives {query} in body, returns {response} or {output}." }, "testSuite": { "title": "Test Suite", "type": "array", "description": "Array of test cases. Each item: { query, payload, expectedKeywords, minLength, maxLatencyMs }. Use 'payload' to override the default {query} input for specialized actors (e.g. {urls:[...]} for scrapers). Leave blank for default suite.", "default": [] }, "dimensions": { "title": "Audit Dimensions", "type": "array", "description": "Which quality dimensions to score. Valid values: completeness, accuracy, actionability, structure, cost_efficiency.", "default": [ "completeness", "accuracy", "actionability", "structure", "cost_efficiency" ], "items": { "type": "string" } }, "runs": { "title": "Number of Test Runs", "minimum": 1, "maximum": 10, "type": "integer", "description": "How many times to run each test case (for consistency scoring).", "default": 3 }, "apifyToken": { "title": "Apify API Token (for actor audits)", "type": "string", "description": "Required if auditing an Apify actor. Used to trigger runs and read results. Get yours at console.apify.com/account/integrations." } } }, "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 } } } } } } } } } } ```