Deep context

Deep Context Skill

Cross-channel research agent that searches ALL connected systems for everything known about a topic, then synthesizes a comprehensive briefing. Searches beyond Atlassian to include Obsidian, Gmail, Slack, GitHub, and Granola.

When to Activate

• User says "deep context", "tell me everything about X", "research X"
• User says "what do we know about X?", "full context on X"
• User says "brief me on X", "context dump for X"
• Before writing a spec, RFC, or making a strategic decision
• When onboarding someone to a topic

How It Differs from Simple Search

	/simple-search	/deep-context
Sources	Confluence, Jira, Slack, local context files	ALL of those + Obsidian vault + Gmail + GitHub + Granola
Depth	Quick answers, 1-3 sources	Comprehensive briefing, all sources
Speed	Fast (seconds)	Thorough (30-60 seconds)
Output	Direct answer with citations	Structured briefing document
Use case	"Who owns search?"	"Give me the full history of our search relevancy work"

Use /simple-search for quick factual questions. Use /deep-context for comprehensive research.

Input

Required:

• Topic: The subject to research (e.g., "ESP corridors", "MatchMaker redesign", "lead quality")

Optional:

• Time range: Defaults to "all time". User can specify "last 3 months", "Q1 2026", etc.
• Focus: "decisions", "status", "history", "people" - narrows the synthesis
• Output: "conversation" (default), "file", "confluence"

Execution

Step 0: Expand Search Terms

Use domain terminology expansion:

• Input "ESP" → also search "Explore Similar Programs", "multiplier", "loop yield"
• Input "search" → also search "Elasticsearch", "SERP", "relevancy", "RRF"
• Input "ads" → also search "Kevel", "Featured Listings", "sponsored"

For general topics, generate 2-3 synonym expansions.

Step 1: Parallel Data Gathering (Fan-Out)

Launch ALL searches in parallel using Agent tool or direct MCP calls:

Source	Tool	Query Pattern
Obsidian vault	Obsidian vault skill or direct file search	Grep for topic + expansions across vault
Granola MCP	query_granola_meetings	Topic keywords in meeting transcripts
Jira MCP	searchJiraIssuesUsingJql	text ~ "{topic}" ORDER BY updated DESC (max 20)
Confluence MCP	searchConfluenceUsingCql	text ~ "{topic}" ORDER BY lastModified DESC (max 10)
Slack MCP	slack_search_public_and_private	Topic keywords, sorted by recency
Gmail MCP	gmail_search_messages	Topic keywords in email threads
GitHub MCP	search_issues + search_pull_requests + search_code	Topic in issues, PRs, and code
Local context	Read domain-search reference files	Check master and tech context docs

Step 2: Deduplicate and Rank

• Same item across sources (e.g., Jira ticket mentioned in Slack) = merge, cite both
• Rank by: recency first, then richness of content
• Group by theme/subtopic if the topic is broad

Step 3: Synthesize Briefing

Build the structured output from merged results.

Output Format

# Deep Context: {Topic}

**Generated:** {timestamp}
**Sources searched:** {list of sources that returned results}
**Total items found:** {count across all sources}

---

## Executive Summary
{3-5 sentences: What is this topic, what's its current status, and what matters most right now}

## Timeline
| Date | Event | Source |
|:-----|:------|:-------|
| {date} | {What happened} | {Jira/Confluence/Slack/etc.} |
| {date} | {What happened} | {source} |
| {date} | {What happened} | {source} |

## Current Status
- **State:** {Active / Completed / Paused / Blocked}
- **Owner:** {Person or squad}
- **Last activity:** {date} in {source}
- {Key metrics or status indicators if available}

## Key Decisions
1. **{Decision}** ({date}): {What was decided and why}
   - Source: {link or reference}
2. **{Decision}** ({date}): {What was decided and why}
   - Source: {link or reference}

## Open Questions / Unresolved
- {Question or thread that hasn't been resolved}
- {Blocked item or pending decision}

## Key People
| Person | Role in This Topic | Recent Activity |
|:-------|:-------------------|:----------------|
| {name} | {their involvement} | {what they did recently} |

## All Sources
### Jira
- [{TICKET}] {title} - {status} ([link])

### Confluence
- {Page title} - last updated {date} ([link])

### Slack
- #{channel}: {thread summary} ({date})

### Obsidian
- {Note title} ({date}): {relevant excerpt}

### Gmail
- {Subject line} from {sender} ({date})

### GitHub
- {PR/Issue title} - {status} ([link])

---
*Generated by /deep-context*

Rules

• Exhaustive then concise: Search everything, but synthesize tightly. The briefing should be readable in 3-5 minutes.
• Source everything: Every claim must have a source citation.
• Conflict surfacing: If sources disagree, call it out explicitly (same pattern as domain-search).
• Recency weighting: More recent items get more prominence, but don't omit history.
• No speculation: If something is unclear, say "unclear" not "probably".
• Privacy: Don't include others' private Slack DMs. Only include threads where the user is a participant or in public channels.

Optimization

For very broad topics that return too many results:

1. Cap each source at 15-20 items
2. Prioritize last 90 days unless user specified otherwise
3. Group by subtopic and summarize groups rather than listing every item
4. Offer to "go deeper" on any subtopic

Fallback

If MCP tools are unavailable:

1. Always search Obsidian vault (local, always available)
2. Always check domain-search local context files
3. Note which sources were unreachable
4. Produce partial briefing with available data
5. Suggest: "Re-run when {source} is available for a complete picture"

Examples

User: "deep context ESP corridors"
→ Searches all sources for ESP, Explore Similar Programs, multiplier, corridor, loop yield
→ Produces full briefing with Q1 OKR data, Jira tickets, meeting decisions, Slack threads

User: "tell me everything about the MatchMaker redesign"
→ Searches all sources for MatchMaker, recommendation form, wizard, lead capture
→ Produces timeline from initial design to current implementation status

User: "deep context INS-1959"
→ Searches for the specific bug across Jira, Slack, GitHub PRs, meeting notes
→ Produces lifecycle view: reported → triaged → discussed → current state

User: "brief me on lead quality, focus on decisions, last 3 months"
→ Narrows time range, emphasizes Key Decisions section
→ Produces decision-focused briefing