community-research-insight

name	community-research-insight
description	Extract structured insight briefs from community research transcripts or notes. Produces pain points, stakeholder needs, opportunity maps, risks, and follow-up questions. Requires human review before publication.
kind	meta
meta_priority	60
always	false
final_text_mode	step:final_response
triggers	["community research","research insight","community insight","transcript analysis","社区研究","研究洞察","访谈分析","社区洞察"]
provenance	{"origin":"openclaw.net","license":"MIT"}
composition	{"steps":[{"id":"collect","kind":"user_input","with":"[Truncated]","clarify":"[Truncated]"},{"id":"analyze","kind":"llm_chat","depends_on":"[Truncated]","on_failure":"analyze_fallback","output_contract":"[Truncated]","with":"[Truncated]"},{"id":"analyze_fallback","kind":"llm_chat","output_contract":"[Truncated]","with":"[Truncated]"},{"id":"draft","kind":"llm_chat","depends_on":"[Truncated]","output_contract":"[Truncated]","with":"[Truncated]"},{"id":"validate","kind":"llm_chat","depends_on":"[Truncated]","output_choices":"[Truncated]","route":"[Truncated]","with":"[Truncated]"},{"id":"validation_revise","kind":"llm_chat","depends_on":"[Truncated]","with":"[Truncated]"},{"id":"preview","kind":"llm_chat","depends_on":"[Truncated]","with":"[Truncated]"},{"id":"review","kind":"user_input","depends_on":"[Truncated]","with":"[Truncated]","clarify":"[Truncated]"},{"id":"final_response","kind":"llm_chat","depends_on":"[Truncated]","with":"[Truncated]"}]}

Community Research Insight Extractor

Extracts pain points, stakeholder needs, risks, and practical technology opportunities from community-engaged research discussions. Produces a structured insight brief for human review before publication.

What It Does

Step	Kind	Purpose
collect	user_input	Collect transcript, context, and audience via chat
analyze	llm_chat	Extract grounded themes as structured JSON
analyze_fallback	llm_chat	Produce best-effort grounded JSON if primary analysis fails
draft	llm_chat	Draft the full 6-section insight brief as structured JSON
validate	llm_chat	Gate preview on PASS vs REVISE grounding validation
validation_revise	llm_chat	Explain why the brief is blocked when validation fails
preview	llm_chat	Render validated findings as human-readable Markdown
review	user_input	Pause for human approve/revise/reject decision
final_response	llm_chat	Produce final output based on review decision

Guardrails

• Never invent quotes, names, dates, or statistics.
• Never attribute views to named people unless present in the source.
• Never recommend replacing community engagement with automation.
• Always separate evidence from inference.
• Always flag missing information rather than filling gaps.
• Always require human review before publication or named attribution.

Fallback

If analyze fails (timeout, provider error, or JSON contract failure), runs a single-turn on the same transcript and must satisfy the same JSON output contract. If returns REVISE, the preview path is blocked and explains what must be fixed before human review.