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evo-enterprise-qa-pipeline

Orchestrates the end-to-end question-answering pipeline for enterprise Slack data. Parses malformed question.txt files, searches extracted document text for answers (employee IDs, URLs, team member names), tracks token consumption, and writes the final answer.json output.

tasks-evolved/enterprise-information-search/environment/skills/evo-enterprise-qa-pipeline/SKILL.md

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Last checked: Nov 6, 2026

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Repository stars (not skill ratings): 97
Repository last updated: Sep 10, 2026
License: Apache-2.0

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agent-skills Repository topic
ai-agents Repository topic
llm-agents Repository topic
open-world Repository topic
self-evolving-agents Repository topic
data Derived category
testing Derived category
testing-browser Derived category

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SKILL.mdcore · 1,643 bytes
scripts/utils.pyruns · 8,267 bytes

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# evo-enterprise-qa-pipeline

End-to-end QA pipeline for enterprise Slack conversation data.

## Key Functions

- `parse_question_file(filepath)` - Parse malformed/unquoted JSON question file using YAML trick
- `extract_employee_ids(text)` - Extract employee IDs matching eid_XXXXXXXX pattern
- `extract_urls(text)` - Extract HTTP/HTTPS URLs from text
- `search_context_for_answer(messages, keywords, context_window)` - Search Slack messages for relevant context
- `find_report_authors_and_reviewers(product_data, report_name)` - Find who authored and reviewed a report
- `find_competitor_insight_providers(product_data, insight_type)` - Find who discussed competitor strengths/weaknesses
- `find_competitor_demo_urls(product_data)` - Find demo URLs for competitor products
- `count_tokens_for_text(text)` - Count tokens using tiktoken cl100k_base encoding