Quick BI-SmartQ — QuickBI Data Analysis Assistant

One entry point covering all QuickBI data analysis capabilities. Automatically routes to the corresponding module based on user intent — no manual selection required.

Scope

Does:

• Automatically identify user intent and route to the corresponding data analysis module
• Perform natural-language analysis on user-uploaded Excel/CSV files via the Quick BI API (File Q&A)
• Perform natural-language query analysis on Quick BI platform datasets, with automatic intelligent table selection and matching (Dataset Q&A)
• Parse PDF/Word/Excel/CSV/images, extract text, and support extracting key fields to generate structured Excel (Document Parsing)
• Auto-convert QuickBI dashboards into data query skills (Dashboard Skill Generation)
• Perform deep insight analysis on datasets (Data Insight)
• Auto-generate professional data reports based on analysis results (Data Report)

Does NOT:

• Use pandas/openpyxl/csv or similar libraries to read files locally for analysis in Q&A scenarios
• Require users to manually choose a module or provide internal parameters such as cubeId
• Perform tasks unrelated to QuickBI data analysis

Image Output Rules (MUST READ)

This skill produces chart images (PNG) as core deliverables for the user. Images MUST be displayed to the user — they are the primary output of the skill.

When the script output contains !Title image references, the Agent MUST:

1. Include every !Title verbatim in the reply body — this is the ONLY way the user can see the chart
2. MUST NOT read/view chart PNG files with Read, showFile, present, or any file-reading tool
3. Single-response delivery — wait for the script to fully complete, then compose ONE reply containing: images → conclusions → interpretation. MUST NOT split across multiple responses
4. Pre-delivery self-check — before sending, verify that every !... from the script output appears in the reply body

The Markdown image syntax !... is the sole delivery mechanism for chart images.

Task Routing

Automatically determine intent based on user input and route to the corresponding module for execution.

Routing Decision Table

Routing Priority Rules

When user intent may match multiple modules, determine by the following priority:

1. "Report" keyword takes priority: When user intent contains keywords like "report", "review", "summary report", "analysis report", ALWAYS route to the Data Report module, regardless of whether files are uploaded. Data Report module has higher priority than File Q&A and Data Insight.
2. "Interpret", "insight", "trend" keywords: When user wants to understand data meaning, discover trends, or gain insights, route to the Data Insight module.
3. Specific data query: When user wants to query specific metrics (TOP N, sum, comparison, etc.), route to the Q&A module (choose Dataset Q&A or File Q&A based on whether files are present).
4. Dashboard URL: When user provides a dashboard / data portal link, route based on the user's intent:
• Wants to generate a query skill → Dashboard Skill Generation
• Wants to interpret / analyze trends / find anomalies / get insights about the dashboard → Data Insight (Dashboard mode)
• When intent is unclear, default to Dashboard Skill Generation

Routing Examples

Fallback Rules

• When intent is unclear, default to Dataset Q&A (module-chat-dataset)
• If the user involves multiple modules at the same time (e.g. "analyze data and generate a report"), execute them in sequence
• Special scenario — multi-file preprocessing before Q&A:
• When user uploads ≥5 unstructured documents (PDF/Word/images, etc.) and requests analysis
• MUST execute Document Parsing first (generate structured Excel)
• Then route to the corresponding functional module based on question intent (perform intelligent analysis on the generated Excel)
• Example: "Analyze these invoice data" + 10 PDFs → Document Parsing (generate Excel) → File Q&A (analyze Excel)
• If routing is incorrect, allow the user to manually specify the module

Configuration

This skill uses a layered configuration architecture, separating user configuration from the skill package. Skill package updates will NOT overwrite user configuration.

> convention: In this document, refers to the absolute path of the folder the user currently has open in the IDE / file manager. The Agent MUST confirm this path before the first operation by running a Python script with os.getenv('CODE_AGENT_CURRENT_SESSION_WORK_DIR'). If the script returns nothing or empty, use the absolute path of the folder selected by the user. MUST NOT infer using $PWD, $CWD, or Path.cwd() or similar runtime variables.

>

> convention: In this document, refers to the root directory of this skill after installation (i.e. the directory containing this SKILL.md file). The Agent can infer it from the path of this file.

Configuration Loading Priority (higher overrides lower)

1. Environment variable ACCESS_TOKEN (highest priority, suitable for container deployment)
2. Workspace-level configuration /.qbi/smartq-chat/config.yaml
3. QBI global configuration ~/.qbi/config.yaml (shared by all skills)
4. Default configuration default_config.yaml inside the skill package (package defaults, updated with the package)

server_domain, api_key, api_secret, and user_token can be placed in the workspace-level configuration or the global configuration. When both exist, the workspace-level configuration takes priority.

Configuration Item Descriptions

• server_domain: Quick BI service domain
• api_key / api_secret: OpenAPI authentication key pair (if not configured, built-in defaults are used for trial mode)
• user_token: Quick BI platform user ID; the Q&A interface requires userId (if not configured, it is registered automatically and written back)

If use_env_property: true is enabled, the configuration can be overridden through the qbi_api_key, qbi_api_secret, qbi_server_domain, and qbi_user_token fields in the ACCESS_TOKEN environment variable JSON.

Automatic Trial Credential Registration

When neither api_key nor api_secret is configured (regardless of whether user_token exists), the script will:

1. If user_token is also not configured, print a friendly message informing the user that trial credentials will be registered automatically and trial mode will begin
2. Use built-in default credentials to populate api_key and api_secret
3. Automatically register a user based on the device's unique identifier and write the userId to the global configuration ~/.qbi/config.yaml (not affected by skill package updates)

> Note: user_token existing alone in the global configuration (from automatic trial registration) will NOT prevent trial credential population. ONLY when api_key or api_secret exists in an external configuration will the trial flow be skipped.

Trial expiration is controlled by the server-side interface through error code AE0579100004 — no local tracking is required.

Custom Configuration Guidance

When users want to use their own Quick BI account credentials (rather than trial credentials), sign in to the Quick BI console, click the avatar option "Copy skill configuration with one click", as shown below:

> Show the configuration screenshot to the user based on current locale:

> - zh_CN: !Copy Skill Config

> - en_US: !Copy Skill Config

After copying, paste the configuration to the Agent. The Agent will automatically write server_domain, api_key, api_secret, and user_token into the workspace-level configuration /.qbi/smartq-chat/config.yaml (and decide whether to sync to the global configuration based on the save_global_property switch).

Agent Configuration Update Rules (Required Reading)

Zero-configuration initialization for new users: If the user says "initialize configuration", "I am a new user", or similar, but has NOT provided any specific configuration values, there is no need to manually write anything to any configuration file. Tell the user to run Q&A directly — the system will automatically complete trial registration (see the Automatic Trial Credential Registration section above).

ONLY apply the following write rules when the user explicitly provides specific configuration values.

Existing configuration protection rule: Before writing, the Agent MUST first check whether the workspace-level configuration file /.qbi/smartq-chat/config.yaml already exists and contains valid configuration. If the file already exists and is non-empty, the Agent MUST NOT modify or overwrite any configuration items on its own, unless the user explicitly expresses intent to update (e.g. "update my configuration", "replace with this configuration", "change api_key to xxx", etc.). When existing configuration is found, inform the user that configuration already exists and ask whether to confirm overwriting.

When the user provides any one or more of api_key, api_secret, user_token, or server_domain, and the above protection rule is satisfied, the Agent MUST use a file editing tool to directly modify the corresponding user configuration file and write the provided values into the matching fields.

Write location rules:

• server_domain, api_key, api_secret, user_token → ALWAYS write to the workspace-level configuration /.qbi/smartq-chat/config.yaml
• Global configuration read/write is controlled by the save_global_property switch (default true):
• If the switch is false → MUST NOT read or write global configuration under any circumstance, skip the global-configuration-related steps below
• If the switch is true and the global configuration ~/.qbi/config.yaml is empty or does not exist → also write to the global configuration
• If the switch is true and the global configuration already contains content → write ONLY to the workspace-level configuration, then ask the user "Global configuration already exists. Do you want to sync the update?" and decide whether to write based on the user's reply

Procedure:

1. Extract configuration key-value pairs from the user message (support common formats such as key: value, key：value, and key=value)
2. Use a file editing tool (such as search_replace) to write the configuration into the workspace-level configuration file
3. Read the save_global_property value in the configuration; if it is false, skip to step 5
4. Check whether the global configuration ~/.qbi/config.yaml exists and is non-empty:
• If it is empty or does not exist → also write to the global configuration
• If it already contains content → ask the user "Global configuration already exists. Do you want to sync the update?" and decide whether to write based on the user's reply
5. After the update, confirm to the user which configuration items were written and where they were written

Prohibited actions:

• ❌ MUST NOT refuse to modify configuration citing reasons such as "limited permissions" or "unable to modify files inside the skill package"
• ❌ MUST NOT suggest workarounds such as using environment variables or manually copying files
• ❌ MUST NOT only output the configuration content and ask the user to modify it themselves

Prerequisites

• Python dependencies MUST be installed: pip install requests pyyaml matplotlib numpy
• Browser automation capability is required (Dashboard Skill Generation module ONLY)
• Dataset Q&A: user MUST have Q&A permission for the target dataset
• File Q&A: file formats limited to xls, xlsx, csv; single file size ≤ 5MB
• Document Parsing:
• System dependency: brew install tesseract tesseract-lang (required for local parsing ONLY)
• Supported formats: PDF, Word (.doc/.docx), Excel (.xls/.xlsx), CSV, images (.png/.jpg/.jpeg)
• Single file size ≤ 5MB (remote OCR limit)
• Error handling:
• Local parsing failure → automatically falls back to remote OCR
• Remote OCR still fails → classified as "parse failure", retaining original filename and error message
• Unknown document type → extract 5+ generic fields, MUST obtain user confirmation before generating Excel
• Detailed documentation: module-document-parser.md

Script Calling Convention (Required Reading)

When calling any Python script:

1. The script path MUST use the absolute path of the installed skill package directory (i.e. /scripts/...); MUST NOT use relative paths
2. MUST pass the absolute path of via the --workspace-dir parameter (see the conventions in the Configuration section above for how to obtain it)
3. Wrap path parameter values in quotes (to prevent shell tokenization issues caused by Chinese characters, spaces, or other special characters)
4. smartq_stream_query.py, file_stream_query.py, q_insights.py, create_chat.py, generate_report.py MUST include the --locale parameter — see User Locale Determination Rules below

Invocation examples:

# File upload
python '<skill-package-dir>/scripts/chat/upload_file.py' '/path/to/data.xlsx' --workspace-dir '<workspace-dir>'

# File Q&A
python '<skill-package-dir>/scripts/chat/file_stream_query.py' <fileId> "headcount distribution by department" --locale zh_CN --workspace-dir '<workspace-dir>'

# Dataset Q&A
python '<skill-package-dir>/scripts/chat/smartq_stream_query.py' "TOP 3 regions by sales" --locale zh_CN --workspace-dir '<workspace-dir>'

# Dataset Q&A (with dataset name hint — enables name lookup, exact match skips intelligent table selection)
python '<skill-package-dir>/scripts/chat/smartq_stream_query.py' "Based on 'Order Sales Details', what is the sales share by platform in Q1?" --cube-name 'Order Sales Details' --locale zh_CN --workspace-dir '<workspace-dir>'

# Document Parsing - local
python '<skill-package-dir>/scripts/document/document_local_parse.py' '/path/to/folder/' --json --workspace-dir '<workspace-dir>'

# Document Parsing - remote OCR
python '<skill-package-dir>/scripts/document/document_remote_ocr.py' '/path/to/folder/' --workspace-dir '<workspace-dir>'

# Excel generation
python '<skill-package-dir>/scripts/document/generate_excel.py' '<json-path>' --workspace-dir '<workspace-dir>'

# Data Insight
python '<skill-package-dir>/scripts/insight/q_insights.py' "any anomalies in this report?" --excel-file '/path/to/data.xlsx' --locale zh_CN --workspace-dir '<workspace-dir>'

# Data Insight (Dashboard mode — dashboard URL or data portal URL)
python '<skill-package-dir>/scripts/insight/q_insights.py' "what are the sales trends in this dashboard?" --dashboard-url 'https://bi.aliyun.com/dashboard/view/pc.htm?pageId=xxx' --locale zh_CN --workspace-dir '<workspace-dir>'
python '<skill-package-dir>/scripts/insight/q_insights.py' "Interpret this portal page" --dashboard-url 'https://bi.aliyun.com/product/view.htm?productId=xxx&menuId=yyy' --locale en_US --workspace-dir '<workspace-dir>'

# Report generation
python '<skill-package-dir>/scripts/report/generate_report.py' "this month sales analysis" --locale zh_CN --workspace-dir '<workspace-dir>'

User Locale Determination Rules

> Core principle: --locale MUST be determined SOLELY based on the user's input text, NOT influenced by any other source.

Valid values: zh_CN or en_US only.

Determination method:

• Examine the user's original input message (the question or instruction the user typed)
• Identify the question/instruction language — the language of the sentence structure, verbs, and functional words (not embedded proper nouns)
• Chinese question language → zh_CN; English or other question language → en_US

Mixed-language handling (critical):

• When user input contains both Chinese and English, determine locale by the question framing language, NOT by embedded entity names (dataset names, field names, table names, etc.)
• Entity names (dataset names, field names, etc.) embedded in the question are proper nouns / references — they do NOT indicate the user's language preference
• Rule of thumb: strip out quoted names or recognizable entity references, then judge the language of the remaining sentence structure

What counts as "user input text":

• ✅ The text the user typed in the current conversation turn
• ✅ The user's original question when performing follow-up queries in the same session

What MUST NOT influence locale determination:

• ❌ The Agent's own reply language (Agent may reply in a language different from the user's input)
• ❌ API response content or error messages (these are always in a fixed language regardless of the user's language)
• ❌ Script console output text
• ❌ Dataset names, field names, or other metadata — whether returned by the platform OR embedded in the user's question as references
• ❌ System prompt language or Agent configuration language

Example:

• User input: "please analyze sales data" → --locale zh_CN (question language is Chinese)
• User input: "Analyze sales data" → --locale en_US (question language is English)
• User input: "Analyze the sales-dataset" → --locale en_US (question language is English; the dataset name is a reference, not the question language)
• User input: "Show me data from 2024-annual-report" → --locale en_US (question language is English; the dataset name is a reference)
• User input: "please query Sales Dataset data" → --locale zh_CN (question language is Chinese; "Sales Dataset" is a dataset name)
• User input: "please analyze sales data", but API returns English error message → --locale zh_CN (locale is determined by user input, NOT by API response)
• Previous Agent reply was in English, user then types "query TOP3" in Chinese → --locale zh_CN (locale is determined by user input, NOT by Agent's previous reply)

Prohibited actions:

• ❌ MUST NOT omit the --workspace-dir parameter when calling scripts
• ❌ MUST NOT use relative paths to call scripts (e.g. python3 scripts/chat/...)
• ❌ MUST NOT use hard-coded paths or guessed paths
• ❌ MUST NOT omit the --locale parameter when calling scripts that require it
• ❌ MUST NOT determine --locale based on Agent's own output language or API/script return content