feat: internalai-business-context

2026-02-19 18:24:56 -05:00
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+---
+name: internalai-business-context
+description: Generate business context updates for InternalAI meetings. Fetches the latest InternalAI meeting (or specified meeting ID), analyzes participant contributions, researches business context via DataIndex, and creates a concise business update document with 1-week priorities. Requires Greyhaven_Company_Documentation.md in repo root.
+user-invocable: true
+argument-hint: [meeting_id]
+---
+
+# InternalAI Business Context Update
+
+Generate a business context update document for InternalAI meetings, connecting technical discussions to Greyhaven's strategic mission.
+
+## What I do
+
+- Fetch the latest InternalAI meeting transcript (or use specified meeting ID)
+- Extract participant talking points and work items
+- Research business context for each participant via DataIndex (last 30 days)
+- Synthesize findings into a concise business update document
+- Map work to Greyhaven's strategic pillars and revenue goals
+
+## When to use me
+
+Use this skill after InternalAI meetings to create business-focused updates that:
+- Connect technical work to business value
+- Show strategic alignment with Greyhaven's mission
+- Highlight immediate priorities (1 week ahead)
+- Provide context for stakeholders (Max, Jordan, investors)
+
+## Prerequisites
+
+- Greyhaven_Company_Documentation.md in repository root
+- DataIndex API access for meeting transcripts and business context
+- If Greyhaven doc missing, run `/init-greyhaven` first
+
+## Workflow
+
+### Step 1: Check for Greyhaven Documentation
+
+```bash
+# Check if Greyhaven_Company_Documentation.md exists in repo root
+ls -la Greyhaven_Company_Documentation.md
+```
+
+**If missing:** Run `/init-greyhaven` to generate it, then retry this skill.
+
+### Step 2: Fetch Meeting Data
+
+**Option A: Use specified meeting ID**
+```python
+dataindex_get_entity_by_id(
+    entity_id="reflector:{meeting_id}",
+    include_raw_data=true,
+    max_content_length=null
+)
+```
+
+**Option B: Fetch latest InternalAI meeting (default)**
+```python
+# Query for latest InternalAI meeting
+dataindex_query_entities(
+    entity_types="meeting",
+    connector_ids="reflector",
+    search="internalai",
+    limit=1,
+    sort_by="timestamp",
+    sort_order="desc"
+)
+
+# Then fetch full transcript
+dataindex_get_entity_by_id(
+    entity_id="reflector:{latest_meeting_id}",
+    include_raw_data=true,
+    max_content_length=null
+)
+```
+
+**Extract from meeting entity:**
+- `raw_data.transcript` - Full conversation text
+- `participants` - List of attendees with contact_ids
+- `title` - Meeting title
+- `timestamp` - Meeting date
+- `id` - Meeting ID for reference links (extract UUID portion)
+
+### Step 3: Parse Meeting into JSON Structure
+
+Create structured JSON with participant talking points:
+
+```json
+{
+  "meeting": {
+    "title": "Meeting Title",
+    "date": "2026-02-18",
+    "room": "internalai",
+    "participants": ["Name1", "Name2"],
+    "meeting_id": "reflector:xxx"
+  },
+  "participants": {
+    "ParticipantName": {
+      "business_value": ["talking point 1", "talking point 2"],
+      "things_done": ["completed item 1"],
+      "things_in_progress": ["current work 1"],
+      "things_to_do": ["planned item 1"],
+      "key_references": ["reference 1"]
+    }
+  }
+}
+```
+
+**Parsing approach:**
+1. Segment transcript by speaker (lines starting with "SpeakerName: ")
+2. Extract business outcomes from each segment
+3. Categorize into: completed, in-progress, planned
+4. Identify key technical references mentioned
+
+### Step 4: Research Business Context (Parallel Subagents)
+
+For each participant, launch parallel subagents to search DataIndex.
+
+**Subagent task template:**
+```
+Research business context for {participant_name} from meeting on {date}.
+
+Meeting context: {meeting_title} - {brief_description_of_discussion}
+
+Search DataIndex for the last 30 days (from {date_minus_30} to {date}) to find:
+- What projects this person is working on
+- What business problems they're solving
+- Recent decisions or priorities
+- Related work from other team members
+
+**Search Command to Use:**
+Use dataindex_search with these parameters:
+- query: [your search terms]
+- limit: 10
+- date_from: {date_minus_30} (ISO format)
+- date_to: {date} (ISO format)
+
+**Maximum 10 searches per participant.** Choose your search queries strategically based on the meeting topics discussed.
+
+**URL Construction Helpers:**
+
+For Zulip references (from threaded_conversation entities):
+- Base: https://zulip.monadical.com/#narrow/channel/
+- Format: https://zulip.monadical.com/#narrow/channel/{stream_id}-{stream_name}/topic/{topic_name}/with/{first_message_id}
+- stream_id: From entity.connector_metadata.stream_id or parse from entity.id
+- stream_name: From entity.title (extract stream name before the dash)
+- topic_name: From entity.connector_metadata.topic or parse from entity.title
+- message_id: Use the first message ID from entity.recent_messages[0].id
+
+For Reflector references (from meeting entities):
+- Base: https://reflector.monadical.com/transcripts/
+- Format: https://reflector.monadical.com/transcripts/{transcript_id}
+- transcript_id: Extract from meeting.entity_id (remove "reflector:" prefix)
+
+**What to return:**
+For each participant, provide:
+1. Key themes from their work (2-3 themes)
+2. Business value of each theme (1 sentence)
+3. Strategic alignment with Greyhaven (1 sentence)
+4. Direct URLs to relevant sources (use formats above)
+
+Return as structured JSON:
+{
+  "participant": "Name",
+  "research_date_range": "{date_minus_30} to {date}",
+  "key_themes": [
+    {
+      "theme": "Theme name",
+      "business_value": "Why this matters commercially",
+      "strategic_alignment": "How this supports Greyhaven's mission",
+      "references": [
+        {
+          "type": "zulip|reflector",
+          "title": "Brief description",
+          "url": "Full URL"
+        }
+      ]
+    }
+  ]
+}
+```
+
+**Launch all subagents simultaneously** - one per participant.
+
+**Search command example for subagents:**
+```python
+# Example search - subagent adapts query based on context
+dataindex_search(
+    query="{participant_name} {topic}",
+    limit=10,
+    date_from="2026-01-18T00:00:00Z",
+    date_to="2026-02-18T23:59:59Z"
+)
+```
+
+### Step 5: Read Greyhaven Business Context
+
+```bash
+# Read Greyhaven documentation
+cat Greyhaven_Company_Documentation.md
+```
+
+Extract key context:
+- Three Pillars: Human-centered design, Local-first sovereignty, Rapid prototyping
+- Other Three Pillars of Data Sovereignty: Choice, Control, Clarity
+- Current positioning: "Palantir for SMEs"
+- Key stakeholders: Max (Founder), Jordan (BizDev), Corey Gallon (Strategic Advisor)
+- Immediate business priorities: Fundraising, first paying customer (Electra), enterprise readiness
+
+### Step 6: Generate Business Context Document
+
+**Output file:** `internalai_context_YYYY-MM-DD.md` (in repo root)
+
+**Document structure:**
+
+```markdown
+# InternalAI Business Context Update
+
+[2-3 sentences total business impact across all participants]
+
+---
+
+## Participant Updates
+
+### {Participant Name}
+
+**What They've Done:**
+[2-3 sentences on completed work with business outcomes]
+
+**Immediate Priorities (Next 7 Days):**
+- [Priority 1]
+- [Priority 2]
+
+**Strategic Context:**
+[1-2 sentences connecting work to Greyhaven's mission and pillars]
+
+**References:**
+- [Title](https://zulip.monadical.com/#narrow/channel/{stream_id}-{stream}/topic/{topic}/with/{message_id})
+- [Title](https://reflector.monadical.com/transcripts/{transcript_id})
+
+---
+
+**Revenue Enablement:**
+[How this work supports fundraising, customer acquisition, or retention]
+
+**Market Positioning:**
+[How this supports "Palantir for SMEs" or data sovereignty messaging]
+
+---
+
+## Reference Links
+
+### Key Meetings
+- [Meeting Title](https://reflector.monadical.com/transcripts/{transcript_id})
+
+### Zulip Threads
+- [Thread Title](https://zulip.monadical.com/#narrow/channel/{stream_id}-{stream}/topic/{topic}/with/{message_id})
+
+---
+
+*Document generated from meeting transcript and DataIndex research*
+```
+
+**Content guidelines:**
+- **Per participant:** Max 2 short paragraphs (What Done + Strategic Context)
+- **Immediate priorities:** Only 1 week ahead (not 2 weeks)
+- **Business language:** No technical jargon, focus on outcomes
+- **URL formats:** 
+  - Zulip: `https://zulip.monadical.com/#narrow/channel/{stream_id}-{stream_name}/topic/{topic_name}/with/{message_id}`
+  - Reflector: `https://reflector.monadical.com/transcripts/{transcript_id}`
+
+### Step 7: Generate Meeting JSON
+
+Create `{date}_meeting_analysis.json` with parsed talking points (saved by default):
+
+```json
+{
+  "meeting": {
+    "title": "...",
+    "date": "...",
+    "participants": ["..."],
+    "meeting_url": "https://reflector.monadical.com/transcripts/{id}"
+  },
+  "participants": {
+    "Name": {
+      "business_value": [...],
+      "things_done": [...],
+      "things_in_progress": [...],
+      "things_to_do": [...],
+      "key_references": [...]
+    }
+  },
+  "decisions_made": [...],
+  "open_questions": [...]
+}
+```
+
+### Step 8: Output Summary
+
+Display to user:
+```
+✅ Business Context Update Complete
+
+📄 Documents generated:
+   - internalai_context_{date}.md
+   - {date}_meeting_analysis.json
+
+👥 Participants covered: {names}
+
+🎯 Key business themes:
+   - {theme 1}
+   - {theme 2}
+```
+
+## Commands Summary
+
+**Pre-flight check:**
+```bash
+ls -la Greyhaven_Company_Documentation.md || echo "Run /init-greyhaven first"
+```
+
+**Fetch meeting (latest):**
+```python
+dataindex_query_entities(
+    entity_types="meeting",
+    connector_ids="reflector",
+    search="internalai",
+    limit=1,
+    sort_by="timestamp",
+    sort_order="desc"
+)
+```
+
+**Fetch meeting (by ID):**
+```python
+dataindex_get_entity_by_id(
+    entity_id="reflector:{meeting_id}",
+    include_raw_data=true,
+    max_content_length=null
+)
+```
+
+**Research business context (per participant - max 10 searches):**
+```python
+# Launch parallel subagents, one per participant
+# Each subagent runs up to 10 dataindex_search queries
+# Subagent constructs queries based on meeting context
+```
+
+**Generate output:**
+```bash
+# Write to: internalai_context_YYYY-MM-DD.md
+# Write to: YYYY-MM-DD_meeting_analysis.json
+```
+
+## URL Construction Reference
+
+### Zulip URLs
+From `threaded_conversation` entity:
+```
+https://zulip.monadical.com/#narrow/channel/{stream_id}-{stream_name}/topic/{topic_name}/with/{message_id}
+```
+
+**Field mapping:**
+- `stream_id`: entity.connector_metadata.stream_id
+- `stream_name`: Parse from entity.title (before dash)
+- `topic_name`: entity.connector_metadata.topic
+- `message_id`: entity.recent_messages[0].id
+
+### Reflector URLs
+From `meeting` entity:
+```
+https://reflector.monadical.com/transcripts/{transcript_id}
+```
+
+**Field mapping:**
+- `transcript_id`: entity.entity_id.replace("reflector:", "")
+
+## Error Handling
+
+- **Greyhaven doc missing:** Prompt user to run `/init-greyhaven`
+- **No InternalAI meetings found:** Check room_name filter, suggest checking reflector connector status
+- **Empty transcript:** Mark meeting as processed but note limited content
+- **Subagent failures:** Continue with available data, note gaps in output
+- **Large transcripts (>200KB):** Save to temp file, pass path to subagents
+
+## Dependencies
+
+- [dataindex](../dataindex/SKILL.md) — meeting queries and business context search
+- [init-greyhaven](../init-greyhaven/SKILL.md) — Greyhaven business context generation (if needed)
+- [contactdb](../contactdb/SKILL.md) — participant name resolution
+
+## Example Usage
+
+**Default (latest meeting):**
+```
+/internalai-business-context
+```
+
+**Specific meeting:**
+```
+/internalai-business-context aecfd2e9-990f-4f25-b746-eb14ddae7494
+```
+
+**Output location:**
+- `{repo_root}/internalai_context_2026-02-18.md`
+- `{repo_root}/2026-02-18_meeting_analysis.json`