Run2 enterprise data retrieval
Complete guide to retrieving structured answers from enterprise product JSON data files containing Slack, documents, meetings, and PRsFrom its SKILL.md
npx -y skills add cxcscmu/SkillLearnBench --skill run2_enterprise-data-retrievalAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
SKILL.md
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Enterprise Data Retrieval
Data File Structure
Enterprise product JSON files (/root/DATA/products/<ProductName>.json) contain:
slack: Array of Slack messages with Channel, Message, ThreadRepliesdocuments: Array of docs (Market Research Report, PRD, TSD, etc.)meeting_transcripts: Meeting transcriptsmeeting_chats: Meeting chat logsurls: URL records with link, description, idprs: GitHub pull request records
Finding Document Authors and Reviewers
import json
with open('/root/DATA/products/ContentForce.json') as f:
data = json.load(f)
# Step 1: Get the author from the document
for doc in data['documents']:
if doc.get('type') == 'Market Research Report':
author = doc.get('author') # e.g., 'eid_1e9356f5'
# The 'feedback' field in the final document lists reviewer suggestions
feedback = doc.get('feedback', '')
break
# Step 2: Match Slack messages to identify key reviewers
# Find the conversation where the document was shared and reviewers gave feedback
for msg in data['slack']:
text = msg['Message']['User']['text']
user = msg['Message']['User']['userId']
ts = msg['Message']['User']['timestamp']
channel = msg['Channel']['name']
if 'market research' in text.lower() or 'Market Research' in text:
print(f'{ts} | {channel} | {user}: {text[:200]}')
Key insight: Match each bullet point in the document feedback field to the Slack user who suggested it.
Channel Name Mapping
Channels may be renamed during project lifecycle:
planning-entAIX→ renamed toplanning-ContentForce(the product was renamed)- Check for
slack_admin_botmessages like "@user renamed the channel to..."
Finding Competitor Insights
competitor_names = ['PitchPerfect AI', 'SalesMate AI', 'ConvoSuggest']
for msg in data['slack']:
text = msg['Message']['User']['text']
user = msg['Message']['User']['userId']
channel = msg['Channel']['name']
# People who initiated competitor discussions provided insights
if any(c in text for c in competitor_names) and 'reading about' in text.lower():
print(f'{channel} | {user}: {text[:200]}')
Note: "Providers of insights" = those who initiated discussions with detailed information, not just those who acknowledged insights from others.
Finding Demo URLs
Three sources to check:
- Slack messages containing "demo" and "http"
- The
urlsarray in the data file - Thread replies (sometimes URLs appear in replies)
# Method 1: From Slack messages
for msg in data['slack']:
text = msg['Message']['User']['text']
if 'demo' in text.lower() and 'http' in text and 'ContentForce' in msg['Channel']['name']:
# Extract the URL
import re
urls = re.findall(r'https?://[^\s<>\"]+', text)
print(msg['Message']['User']['userId'], urls)
# Method 2: From urls array
for url_entry in data.get('urls', []):
if 'demo' in url_entry['link'].lower():
print(url_entry['link'])
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.