What is the AlphaEar Reporter?

A skill that plans, writes, and edits professional financial reports and generates finance chart configurations, using a structured agentic workflow.

Which prompts are used?

Cluster Signals Prompt, Write Section Prompt, Final Assembly Prompt, and optional json-chart blocks or charts via scripts/visualizer.py.

What dependencies exist?

sqlite3 is built-in and required for data handling.

alphaear-reporter

Scanned

npx machina-cli add skill RKiding/Awesome-finance-skills/alphaear-reporter --openclaw

Files (1)

SKILL.md

1.1 KB

AlphaEar Reporter Skill

Overview

This skill provides a structured workflow for generating professional financial reports. It includes planning, writing, editing, and creating visual aids (charts).

Capabilities

1. Generate Structured Reports (Agentic Workflow)

YOU (the Agent) are the Report Generator. Use the prompts in references/PROMPTS.md to progressively build the report.

Workflow:

Cluster Signals: Read input signals and use the Cluster Signals Prompt to group them.
Write Sections: For each cluster, use the Write Section Prompt to generate analysis.
Assemble: Use the Final Assembly Prompt to compile the report.

2. Visualization Tools

Use scripts/visualizer.py to generate chart configurations if needed manually, though the Writer Prompt usually handles this via json-chart blocks.

Dependencies

sqlite3 (built-in)

Source

git clone https://github.com/RKiding/Awesome-finance-skills/blob/main/skills/alphaear-reporter/SKILL.mdView on GitHub

Overview

AlphaEar Reporter provides a structured workflow for generating professional financial reports. It covers planning, writing, editing, and creating visual aids (charts) to condense finance analysis into a coherent output.

How This Skill Works

You act as the Report Generator, using the Cluster Signals Prompt to group input signals, then Write Section Prompt to generate analysis for each cluster. Finally, the Final Assembly Prompt compiles the sections into the report, with optional chart configurations generated via json-chart blocks or the visualizer.py tool.

When to Use It

Condensing dense financial analysis into a formal, stakeholder-ready report.
Creating clearly separated sections per analyzed theme using cluster-based grouping.
Including chart configurations to accompany key metrics in the report.
Enforcing a repeatable, auditable workflow from signals to final assembly.
Preparing reports that reuse a consistent format across periods.

Quick Start

Step 1: Gather input signals and reference prompts (Cluster Signals Prompt, Write Section Prompt, Final Assembly Prompt).
Step 2: Cluster signals, write sections per cluster, and generate chart configurations as needed.
Step 3: Run the Final Assembly Prompt to compile, then review and finalize the report.

Best Practices

Verify input signals before clustering to ensure accuracy.
Leverage the Cluster Signals Prompt for logical grouping.
Write concise, well-supported sections with citations to data.
Include json-chart blocks or use visualizer.py to standardize visuals.
Run the Final Assembly Prompt and perform a final edit pass.

Example Use Cases

A quarterly earnings report with sectioned analysis and accompanying charts.
An executive summary highlighting revenue, costs, and margin trends.
A stakeholder-ready report for an investment committee with structured sections.
A risk and exposure visualization configuration tied to textual analysis.
An auditable report produced by sequential prompts from signals to assembly.

Frequently Asked Questions

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