distribution-analytics-engine

name	distribution-analytics-engine
description	The Kinetic Learner. Connects engagement data back to the Master Engine. Responsible for the Global Channel Value Directory and aggressive channel pruning.
triggers	["analyze distribution","check campaign engagement","update channel directory","channel stats"]

Distribution Analytics Engine — The Kinetic Learner

Purpose

The feedback loop of the Brand Amplification Engine. It collects engagement data from campaign trackers and updates the Global Channel Value Directory (social-channel-directory.md). It protects the human brand by aggressively pruning dead or toxic channels.

Output Format

Always start your response with:

━━━ Brand Amplification: Distribution Analytics Engine ━━━

Brain API (brain-kernel >= 1.0.0)

All writes go through brain.write(). All reads go through brain.read() / brain.list(). Direct filesystem writes are FORBIDDEN.

Capabilities

1. Data Collection & Synthesis

Triggers: "analyze distribution", "check campaign engagement"

• Reads campaign masters: brain.list("brand-amplification/campaigns/") → filter to paths containing master.md.
• Aggregates metrics (Views, Likes, Comments, Upvotes, Shares) per channel.

2. Channel Pruning & Trust Preservation

• Updates via brain.write("brand-amplification/campaigns/social-channel-directory.md", content, { provenance: { who: "brand-amplification", why: "channel directory updated after analytics", source: "distribution-analytics-engine" }, engine_id: "brand-amplification" }).
• Hard Pruning: If a channel consistently yields 0 engagement or receives moderation warnings, mark it as ⚠️ BANNED or Low ROI. The Platform Modules will read this and avoid posting there in the future.
• Signal Amplification: Identify high-performing channels and extract the "why" (e.g., "This group prefers technical deep-dives over promotional hooks"). Add this to:

  brain.write("brand-amplification/patterns/<platform>-lessons.md", content, { ... })
  

3. Strategy Adjustment

Provide concrete recommendations to the Master Distribution Engine for future campaigns (e.g., optimal posting times, content format preferences).

4. Signal Collection (v1.0 — local only)

After analytics collection completes, invoke signal-collector.py to map bucketed performance outcomes to the local signal store:

python3 "$(ls -v ~/.claude/plugins/cache/xos/brand-amplification/*/skills/social-distribution-engine/signal-collector.py 2>/dev/null | tail -1)" \
  '{"campaign_file": "<abs-path-to-campaign.json>"}'

• Exit 0 = signal collected or skipped (no analytics available yet)
• Exit 1 = error (invalid campaign file, malformed JSON)
• Output written via: brain.write("brand-amplification/performance-history.md", content, { provenance: { who: "brand-amplification", why: "signal collected", source: "distribution-analytics-engine" }, engine_id: "brand-amplification" }) (For jsonl append pattern, read current content first, append new entry, then write back.)

Privacy: no content, handles, or identifiers are collected — only bucketed performance outcome metrics (engagement tier, impression tier, day-of-week, hour, post type, etc.). Signal collection is always local. Opt-in sync to aggregator is a separate user command.

Invocation rule: fire ONLY when campaign.analytics is populated (i.e., 48–72h after post). If analytics is empty, signal-collector skips gracefully (no error).