Comprehensive Analysis

Deep GitHub analysis for [CUSTOMIZE: your-org/your-repo].
Analysis period: last 7 days (from [7_DAYS_AGO] to [TODAY]).

Collect:
1. ALL PRs merged in the last 7 days:
   gh pr list --repo [CUSTOMIZE: your-org/your-repo] --state merged --search "merged:>=[7_DAYS_AGO]" --json number,title,author,mergedAt,labels,additions,deletions --limit 100

2. ALL open PRs with full detail:
   gh pr list --repo [CUSTOMIZE: your-org/your-repo] --state open --json number,title,author,createdAt,reviewDecision,labels,updatedAt,additions,deletions --limit 100

3. Contributor activity breakdown:
   For each contributor, count: PRs merged, PRs opened, commits, review comments given.
   gh api repos/[CUSTOMIZE: your-org/your-repo]/stats/contributors

4. Code frequency (additions/deletions per week):
   gh api repos/[CUSTOMIZE: your-org/your-repo]/stats/code_frequency

5. Commit activity by day:
   gh api repos/[CUSTOMIZE: your-org/your-repo]/stats/commit_activity

6. ALL PR review comments from the last 7 days:
   gh api repos/[CUSTOMIZE: your-org/your-repo]/pulls/comments --paginate --jq '[.[] | select(.created_at >= "[7_DAYS_AGO]")]'

DEEP ANALYSIS:
A) **Contributor Velocity Matrix**: For each contributor — PRs merged, lines changed, review comments given/received. Who's shipping? Who's reviewing? Who's doing both?
B) **Code Churn**: Are we rewriting the same files repeatedly? Flag files with >3 PRs touching them in one week.
C) **PR Lifecycle**: Average time from PR open → first review → merge. Where's the bottleneck?
D) **Review Quality**: Are reviews substantive (comments with suggestions) or rubber stamps (approved with no comments)?
E) **Week-over-Week Trends**: Compare this week's velocity to last week. Accelerating or decelerating?
F) **Technical Debt Signals**: Large PRs with no tests, PRs that touch >10 files, dependency-only PRs.

Return structured data AND insights.

Deep Slack analysis for last 7 days across key channels.
Analysis period: [7_DAYS_AGO] to [TODAY].

Instructions:
1. Use ToolSearch to load Slack tools
2. Read messages from [CUSTOMIZE: your-team-channel] for the full 7-day window
3. If you have access, also check: [CUSTOMIZE: additional-channels] (e.g., #general, #engineering, #product)

For EACH significant thread (>3 replies or involving decisions):
- Full topic summary
- Key participants
- Decision reached (or explicitly: "no decision reached")
- Action items with owners
- Sentiment (positive/negative/neutral/heated)
- Links to external resources shared

DEEP ANALYSIS:
A) **Communication Patterns**: Who's driving discussions? Who's mostly silent? Any asymmetry?
B) **Decision Velocity**: How many decisions were made vs. how many discussions ended without resolution?
C) **Topic Clustering**: Group discussions by theme (product, engineering, bugs, strategy, etc.)
D) **Unresolved Threads**: List all discussions that need follow-up — no decision, open question, blocked waiting for someone
E) **Sentiment Map**: Overall team mood. Are discussions constructive or frustrated?
F) **External Intel**: All links shared (articles, competitor news, tools) — categorize by relevance

Return structured data AND insights.

Deep Linear analysis for last 7 days.
Analysis period: [7_DAYS_AGO] to [TODAY].

Instructions:
1. Use ToolSearch to load ALL Linear tools
2. Collect comprehensive data:

   a) All issues updated in last 7 days (mcp__claude_ai_Linear__list_issues)
   b) All issues created in last 7 days
   c) All issues completed in last 7 days
   d) All blocked issues (any date)
   e) Full initiative list with projects (mcp__claude_ai_Linear__list_initiatives)
   f) Detailed status for each initiative (mcp__claude_ai_Linear__get_initiative)
   g) All active cycles (mcp__claude_ai_Linear__list_cycles)
   h) All milestones for active projects (mcp__claude_ai_Linear__list_milestones)
   i) All projects with progress (mcp__claude_ai_Linear__list_projects)
   j) Recent status updates (mcp__claude_ai_Linear__get_status_updates) for each initiative

DEEP ANALYSIS:
A) **Initiative Trajectory**: For each initiative, model whether current velocity will hit the target date. Use issues_completed_per_week vs. issues_remaining / weeks_remaining.
B) **Cycle History**: Compare current cycle progress to previous cycles. Are we improving?
C) **Scope Creep Quantified**: How many issues were added mid-cycle vs. planned at start?
D) **Priority Drift**: Issues that changed priority during the week. Why?
E) **Assignee Load Balance**: Distribution of issues across team members. Overloaded? Underutilized?
F) **Stale In-Progress**: Issues marked "In Progress" for >5 days with no status change.
G) **Dependency Map**: Issues that block other issues. What's the critical path?
H) **Label/Project Distribution**: Where is effort being spent? Does it align with initiative priorities?

Return structured data AND insights.

Deep PostHog analysis for last 7 days.
Project ID: [CUSTOMIZE: your-posthog-project-id].
Analysis period: [7_DAYS_AGO] to [TODAY].

Instructions:
1. Use ToolSearch to load PostHog tools (search "+posthog")
2. Run these analyses:

   === CORE METRICS (7-day window) ===

   a) Daily visitors, sign-ups, and core events — broken out by day:
      Use mcp__posthog__query-run with HogQL:
      SELECT toDate(timestamp) as day, count(DISTINCT person_id) as unique_users, count() as events
      FROM events WHERE event = '$pageview' AND timestamp >= '[7_DAYS_AGO]'
      GROUP BY day ORDER BY day

   b) Same for sign-ups and core value events (separate queries)

   c) Week-over-week comparison:
      Compare [7_DAYS_AGO to TODAY] vs [14_DAYS_AGO to 7_DAYS_AGO]

   === FUNNEL ANALYSIS ===

   d) Key funnel (sign-up → first core action → repeat):
      Use mcp__posthog__insight-query or HogQL to build a funnel:
      - Step 1: user_signed_up
      - Step 2: [CUSTOMIZE: your_core_event] (first time)
      - Step 3: [CUSTOMIZE: your_core_event] (second time, >24h later)
      Compare this week's funnel to last week's.

   === FEATURE ADOPTION ===

   e) Feature usage matrix:
      SELECT event, count() as uses, count(DISTINCT person_id) as unique_users
      FROM events WHERE timestamp >= '[7_DAYS_AGO]' AND event NOT LIKE '$%'
      GROUP BY event ORDER BY unique_users DESC LIMIT 25

   f) New vs returning user behavior:
      Compare event types for users whose first_seen is within last 7 days vs. older users.

   === ERROR ANALYSIS ===

   g) Error trends:
      Use mcp__posthog__list-errors for the period. Group by error type.

   h) Error-to-deploy correlation:
      For each error spike, check the timestamp against known deploy times (from GitHub merged PR data).

DEEP ANALYSIS:
A) **Growth Trajectory**: At current rate, where will key metrics be in 30/60/90 days?
B) **Funnel Health**: Where do users drop off? Has it improved or degraded vs last week?
C) **Feature Value Matrix**: Which features correlate with retention? (users who use feature X come back more)
D) **New User Experience**: First-session behavior patterns. What do new users do? Where do they get stuck?
E) **Error Impact**: Which errors affect the most users? Which are increasing fastest?
F) **Engagement Segments**: Power users vs casual vs churned. How big is each segment?

Return structured data AND insights with trends.

Review ALL meeting notes from the last 7 days.
Analysis period: [7_DAYS_AGO] to [TODAY].

Instructions:
1. Glob for ALL meeting files in [CUSTOMIZE: path/to/meetings/] from the last 7 days
2. Also check [CUSTOMIZE: path/to/checkins/] for daily checkins
3. Read all found files

For EACH meeting, extract the same items as the daily brief agent.

DEEP ANALYSIS:
A) **Action Item Completion Rate**: Of all action items assigned in meetings this week, how many have corresponding GitHub/Linear activity?
B) **Decision Log**: Comprehensive list of every decision made this week, with context and who made it.
C) **Priority Shifts**: Did priorities change during the week? Track what was said Monday vs. Friday.
D) **Commitment Tracking**: Did people do what they said they'd do? (Cross-reference with GitHub/Linear data)
E) **Meeting Effectiveness**: Are meetings producing decisions and action items, or just discussion?

Return structured data AND insights.