An institutional-grade financial data analyst skill for retrieving real-time quotes, valuation time series, and consensus forecasts across CN, HK, and US markets.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install stocki-financial-reader
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install stocki-financial-reader using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is stocki-financial-reader?
The stocki-financial-reader is an institutional-grade financial data analyst skill designed to query structured market data with maximum precision and output discipline. It seamlessly integrates into the ecosystem of Openclaw Skills, enabling developers to query real-time quotes, financial statements, valuation time series, OHLCV history, industry membership, and consensus forecasts across CN, HK, and US markets. By abstracting data access through an intelligent router, this skill routes incoming natural language requests to the correct endpoint to ensure high data integrity.
This skill is engineered specifically for programmatic financial data retrieval. It ensures strict output format compliance by masking raw data-vendor keys and symbols, returning only clean, user-facing metrics and canonical identifiers. For developers building financial automation tools with Openclaw Skills, stocki-financial-reader provides robust gateway handling, inline truncation guards up to 1000 records, and standardized error-mapping protocols.
stocki-financial-reader Use Cases
- Retrieving real-time market quotes and intraday pricing for stocks, ETFs, indices, futures, and crypto.
- Accessing historical financial statement panels and valuation time series (PE, PB, ROE) over custom periods.
- Resolving natural-language company names, sector concepts, or financial metrics into canonical identifiers before executing complex queries.
- Running deep composite fundamental analyses on CN/HK listed companies using multi-tier pre-computed valuation metrics.
- Generating consensus forecasts and target prices for institutional-grade equity analysis.
How stocki-financial-reader Works
- Pre-processing (Tier 0): The skill passes natural language inputs through name-resolver or metric-resolver to fetch canonical ticker codes and standard metric labels.
- Routing Assessment (Rules R1-R8): An internal decision engine evaluates the query to route it to the optimal reference endpoint (e.g., routing open-ended queries to financial-context or single-point checks to realtime-quote).
- Inline Threshold Calculation: For '/api/v3/datareader/read' requests, the skill estimates the record size (N) to dynamically configure the inline_threshold up to 1000 records.
- HTTP Gateway Communication: Sends secure POST requests to the stocki gateway ($STOCKI_GATEWAY_URL) with the required 'Authorization: Bearer $STOCKI_API_KEY' header.
- Output Filtering & Formatting: Filters out internal data-vendor keys, replacing them with user-friendly labels and bare ticker codes before returning output to the LLM agent.
stocki-financial-reader Setup
To deploy this skill within your Openclaw Skills environment, configure your system parameters and run the validation scripts.
Environment Variables
Set the following variables in your environment:
export STOCKI_GATEWAY_URL='https://skill.stocki.com.cn' # Use http://localhost:9996 for local development
export STOCKI_API_KEY='your_secure_api_key_here'
Self-Diagnosis and Setup Verification
Run the verification scripts to guarantee network reachability, valid authorization, and script workspace integrity:
# Verify environment configurations and file integrity
python3 ./scripts/doctor.py
# Perform a live gateway reachability and auth read smoke test
python3 ./scripts/diagnose.py
Note: Standardized exit codes (0: Success, 1: Auth Invalid, 2: Unreachable, 3: Gateway Unavailable, 4: Rate Limited) are returned directly without retrying.
stocki-financial-reader Data Schema & Taxonomy
Data Type Allocations & Margin Scaling
To optimize token usage while avoiding data truncation, the skill tracks data complexity and applies margin multipliers before modifying the inline threshold:
| Data Type | Margin Multiplier | Purpose |
|---|---|---|
price / fundamental / indicator / company_info |
1.2 | Standard quote, fundamentals, and company indicators |
consensus / estimate / forecast |
1.5 | Future earnings forecasts and consensus trends |
index_member |
1.33 | Benchmark and index member mapping tables |
revenue_breakdown |
2.0 | High-dimensional revenue segments and product splits |
Truncation Behavior (N = symbols * days * metrics)
| Record Threshold | Configured Action |
|---|---|
| N <= 50 | Standard behavior; default response limits are sufficient |
| 50 < N <= 1000 | Set inline_threshold = min(ceil(N * margin), 1000) |
| N > 1000 | Narrow query parameters (e.g., split by day/symbol) or fail-loud to prevent token waste |
stocki-financial-reader Advanced Features
- Intelligent Multi-Call Chaining: Safely triggers multiple tier reference lookups (e.g., tracking quotes, consensus targets, and company directories in parallel) without collapsing the structural accuracy.
- Vendor-Agnostic Output Isolation: Strips internal metadata markers (like pipe-delimited codes or system prefixes) to keep the generated context clean, robust, and decoupled from any underlying data provider.
- Self-Healing Routing Hierarchy: Dynamically shifts query resolution down to US stock fallbacks (realtime-quote and market cap metrics) when specialized composite CN/HK endpoints are unavailable.
SKILL.md
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