What makes Earnings Call Processor notable?

961 downloads. Trust evidence available

How should Earnings Call Processor be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

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Earnings Call Processor

Transcribe earnings call audio via Whisper, analyze stock price data, and generate structured Feishu documents summarizing financial results. Skill: Earnings Call Processor Owner: terrycarter1985 Summary: Transcribe earnings call audio via Whisper, analyze stock price data, and generate structured Feishu documents summarizing financial results. Tags: latest:3.0.0 Version history: v1.1.0-alpha.1 | 2026-05-12T00:14:48.331Z | user v1.1.0-alpha.1: Local whisper fallback, structured analysis pipeline, Feishu draft integration, stock data via yfinance v3.0.0 | 2

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

961

Updated

Oct 12, 2026

Version

1.1.0-alpha.1

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 961 downloads reported by the source. Last updated 10/12/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 12, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 12, 2026
Adoption signal: 961 downloadsadoption · observed Oct 12, 2026
Latest release: 1.1.0-alpha.1release · observed May 12, 2026
Handshake status: UNKNOWNsecurity

What changed

Newest first. Only changes that point at a source are listed.

Release 1.1.0-alpha.1release
v1.1.0-alpha.1: Local whisper fallback, structured analysis pipeline, Feishu draft integration, stock data via yfinance
Observed May 12, 2026 · source

Install and run

Setup complexity: low.

clawhub skill install s17brwfrqyjhbjgadkvar20h8x8492g8:earnings-call-processor

Setup complexity is classified as HIGH. You must provision dedicated cloud infrastructure or an isolated VM. Do not run this directly on your local workstation.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-terrycarter1985-earnings-call-processor/snapshot"

Run-check

$0.02 USD

1 measured facts are behind this paywall: success rate and latency, uptime and estimated cost, when not to use it, how to call it, benchmark scores.

Agents pay $0.02 in USDC. A card payment is $0.50, the smallest a card allows.

Documentation

CLAWHUB

51,503 characters of source documentation, loaded on request.

Extracted files

3 files captured from the source.

SKILL.md

---
name: earnings-call-processor
description: Automated earnings call processing workflow. Transcribe earnings call audio (OpenAI Whisper API or local whisper), fetch stock/financial data, generate structured analysis, and publish as Feishu draft. Use when processing earnings call recordings, quarterly earnings audio, investor call transcripts, or when asked to analyze an earnings call. Triggers on phrases like "process earnings call", "analyze this earnings call", "transcribe earnings call", "财报电话会", "处理财报", "earnings call analysis".
---

# Earnings Call Processor

End-to-end pipeline: audio → transcript + stock data → structured analysis → Feishu draft.

## Prerequisites

- **OpenAI API key** (for Whisper API transcription) — set `OPENAI_API_KEY`; or use `--local` for offline transcription
- **openai-whisper** — `pip install openai-whisper` (required for `--local` mode; auto-fallback if API unavailable)
- **yfinance** — `pip install yfinance`
- **ffmpeg** — required by whisper for audio processing
- **Feishu** (optional, for draft creation) — requires feishu-doc skill configured

## Quick Start

```bash
# Full pipeline: transcribe + stock data + analysis + Feishu draft
python3 scripts/process_earnings_call.py ./call.wav AAPL

# Use local whisper (no API key needed)
python3 scripts/process_earnings_call.py ./call.wav AAPL --local

# Skip Feishu (local output only)
python3 scripts/process_earnings_call.py ./call.wav MSFT --no-feishu

# Custom output dir, language, and local model size
python3 scripts/process_earnings_call.py ./call.mp3 TSLA --output-dir ./results --language en --local --model small
```

## Pipeline Steps

1. **Transcribe** (`scripts/transcribe.py`) — Sends audio to OpenAI Whisper API (or local whisper with `--local`), returns transcript + segments
2. **Fetch Stock** (`scripts/fetch_stock.py`) — Pulls price history, company info, financials via yfinance
3. **Analyze** (`scripts/process_earnings_call.py` → `build_analysis()`) — Combines transcript + stock data into structured markdown
4. **Publish** (`scripts/create_feishu_draft.py`) — Creates Feishu document with the analysis

## Individual Scripts

### Transcribe only

```bash
# API mode (requires OPENAI_API_KEY)
python3 scripts/transcribe.py ./call.wav --output transcript.json

# Local mode (no API key needed)
python3 scripts/transcribe.py ./call.wav --local --model base --output transcript.json
```

### Stock data only

```bash
python3 scripts/fetch_stock.py AAPL --period 3mo --output stock.json
```

### Feishu draft from existing content

```bash
python3 scripts/create_feishu_draft.py --title "AAPL Q4 Analysis" --content-file analysis.md
```

## Output

All outputs go to `--output-dir` (default: `./output`):

| File | Description |
|------|-------------|
| `transcript.json` | Full transcript with segments and timestamps |
| `stock_data.json` | Price history, company info, financials |
| `analysis.md` | Structured analysis document (also sent to Feishu) |

## Agent Workflow

W

_meta.json

{
  "ownerId": "kn72jmd20ws94jr0p8b3p24wyn82abgj",
  "slug": "earnings-call-processor",
  "version": "1.1.0-alpha.1",
  "publishedAt": 1778544888331
}

references/output_format.md

# Earnings Call Output Format

## Document Structure

The Feishu draft follows this structure:

1. **Header** — Company name, ticker, generation timestamp, call duration
2. **Quick Metrics** — Current price, market cap, P/E, EPS, growth rates, price trend
3. **Transcript Summary** — First 500 chars of transcript as preview; full transcript in raw JSON
4. **Key Themes & Sentiment** — Template for analyst/LLM to fill: tone, key topics, guidance, risks
5. **Financial Context** — Table: sector, industry, 52-week range, forward P/E
6. **Price Action** — Recent 10 trading days table
7. **Action Items** — Checklist for follow-up analysis

## LLM Post-Processing (Optional)

After generating the draft, an LLM can enrich "Key Themes & Sentiment" by:
- Analyzing the full transcript for management tone (bullish/bearish/neutral)
- Extracting specific forward guidance statements
- Identifying risk factors mentioned
- Comparing language vs. previous quarter's call

The `process_earnings_call.py` script produces the structural scaffold. The agent invoking it should follow up with LLM-based analysis to fill the template sections.

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