research-synthesis

/research-synthesis

If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.

Lark-native execution (depth core: LARK-PATTERNS, LARK-RECIPES, LARK-FUSION). Synthesis logic unchanged; inputs and outputs become Lark. Pull real inputs: interview audio → AI transcript/summary/action-items via lark_minutes_search (participant_ids="me", P6) — don't re-summarize from scratch; support tickets / NPS from a feedback Lark Base (lark_base_search, P5) or Intercom (external); validate qualitative themes against product analytics (external MCP). Land outputs: publish the synthesis report to the research repository in Wiki (lark_wiki_node_create, P8) via lark-doc; track Insight→Opportunity rows in a research-findings Lark Base (lark_base_record_upsert; scaffold via base-deploy); turn recommendations into Lark Tasks (lark_task_create, owner via lark_contact_search P1, dry_run P2); share the executive summary as an interactive card (lark_im_card_send, P4).

Synthesize user research data into actionable insights. See the user-research skill for research methods, interview guides, and analysis frameworks.

Usage

/research-synthesis $ARGUMENTS

What I Accept

• Interview transcripts or notes
• Survey results (CSV, pasted data)
• Usability test recordings or notes
• Support tickets or feedback
• NPS/CSAT responses
• App store reviews

Output

## Research Synthesis: [Study Name]
**Method:** [Interviews / Survey / Usability Test] | **Participants:** [X]
**Date:** [Date range] | **Researcher:** [Name]

### Executive Summary
[3-4 sentence overview of key findings]

### Key Themes

#### Theme 1: [Name]
**Prevalence:** [X of Y participants]
**Summary:** [What this theme is about]
**Supporting Evidence:**
- "[Quote]" — P[X]
- "[Quote]" — P[X]
**Implication:** [What this means for the product]

#### Theme 2: [Name]
[Same format]

### Insights → Opportunities

| Insight | Opportunity | Impact | Effort |
|---------|-------------|--------|--------|
| [What we learned] | [What we could do] | High/Med/Low | High/Med/Low |

### User Segments Identified
| Segment | Characteristics | Needs | Size |
|---------|----------------|-------|------|
| [Name] | [Description] | [Key needs] | [Rough %] |

### Recommendations
1. **[High priority]** — [Why, based on which findings]
2. **[Medium priority]** — [Why]
3. **[Lower priority]** — [Why]

### Questions for Further Research
- [What we still don't know]

### Methodology Notes
[How the research was conducted, any limitations or biases to note]

Lark-native inputs & delivery

Pull research inputs from Lark first.

• Interviews / usability sessions recorded in Lark: lark_minutes_search(participant_ids="me", query=...) → use the AI transcript, summary, and action items directly (P6) rather than re-transcribing. For meeting envelope/participants only, lark_vc_search. Delegate deep artifact pulls to the lark-minutes skill.
• Support tickets / NPS / feature requests (user feedback): from a feedback Lark Base via lark_base_search (P5 — pass search_fields for the column(s) to match; lark_base_search does NOT support jq, so narrow with select_fields/limit. If the field names are unknown, discover them first via lark_api GET /open-apis/bitable/v1/apps/{base}/tables/{table}/fields), or Intercom (external MCP) if connected.
• Prior studies: lark_doc_search the research repository before synthesizing, to compare.

Product analytics (external — keep as-is): validate qualitative themes against usage/behavioral metrics via the analytics MCP; quantify pain-point impact. Lark has no analytics warehouse — stays external.

Land outputs in Lark.

• Publish the synthesis to Wiki (P8): lark_wiki_node_create → fill via lark-doc so it lives in the research repository.
• Track Insight→Opportunity rows in a research-findings Lark Base (P5): lark_base_record_upsert (fields:{ Insight, Opportunity, Impact, Effort, Status }, dry_run first); scaffold via base-deploy if none exists.
• Turn recommendations into Lark Tasks: lark_contact_search owner → open_id (P1) → lark_task_create (dry_run, P2); delegate to lark-task.
• Share the executive summary as an interactive card (P4): lark_im_card_send (print_json → dry_run → send) — header, panel per theme with prevalence pills, actions footer. Card grammar → lark-im skill.

Tips

1. Include raw quotes — Direct participant quotes make insights credible and memorable.
2. Separate observations from interpretations — "5 of 8 users clicked the wrong button" is an observation. "The button placement is confusing" is an interpretation.
3. Quantify where possible — "Most users" is vague. "7 of 10 users" is specific.