tweet-reflect

name	tweet-reflect
description	Weekly strategy update — reweight content types from engagement data, prune accounts
var
tags	["twitter","strategy"]

Weekly strategy calibration based on real engagement data from the past 7 days.

Step 1: Aggregate engagement by content type

Read memory/x-performance.jsonl. For snapshots in the last 30 days, group by content_type and compute:

• median_likes, median_replies, median_reposts
• engagement_score = median_likes + median_replies * 2 + median_reposts * 1.5
• sample_count (how many tweets of this type)

Content types with fewer than 3 samples: mark as insufficient_data, do not change their weight.

Step 2: Update memory/x-strategy.md

Rewrite the ## Content Type Weights section of memory/x-strategy.md with the computed scores. Normalise scores to sum to 1.0. Keep the prose sections intact — only update the weights table.

Format:

## Content Type Weights

_Updated: 2026-06-08 by tweet-reflect. Based on 14 engagement snapshots (last 30 days)._

| Type | Weight | Median engagement score | Sample count |
|------|--------|------------------------|--------------|
| on-chain-report | 0.35 | 6.2 | 5 |
| ecosystem-commentary | 0.28 | 4.8 | 4 |
| agent-philosophy | 0.22 | 3.9 | 3 |
| lp-update | 0.15 | 2.4 | 2 |
| reaction | — | — | insufficient_data (1 sample) |

If no performance data exists yet, write a note: "No data yet — equal weights applied by tweet-engage."

Step 3: Record promoted candidates

Read memory/x-performance.jsonl and memory/x-tweet-log.jsonl. Identify the top 3 tweets by engagement_score = likes + replies * 2 + reposts * 1.5 from the past 30 days. For each, fetch the tweet text from x-tweet-log.jsonl by tweet_id. Append to memory/x-promoted-candidates.jsonl (skip if tweet_id already present):

{"tweet_id":"...","content_type":"on-chain-report","engagement_score":8.5,"text":"[full tweet text]","nominated_at":"2026-06-08T09:00:00Z","status":"candidate"}

These are nomination-only — the operator manually moves approved candidates into identity/examples/promoted/.

Step 4: Prune and rank x-accounts.json

Read memory/x-accounts.json. For each account:

• If engagement_score == 0 and added_at is more than 30 days ago: mark as status: inactive
• If account has been mentioned in a tweet that got >10 likes (check x-tweet-log.jsonl): increment engagement_score

Write updated memory/x-accounts.json.

Step 5: Process discovery queue

Read memory/x-discovery-queue.jsonl (may not exist — skip silently if absent). For each entry, decide whether to add to x-accounts.json:

• Add if: the wallet belongs to a Liquid Protocol token deployer whose token has >$1K volume (check Dune Q7591697 data in MEMORY.md or memory/on-chain-state.json)
• Skip if: wallet is unknown or token has zero activity

Mark processed entries with processed: true in the queue file.

Step 6: Write reflection

Append to memory/logs/{today}.md:

tweet-reflect: top content type this week: TYPE (score X.X) | strategy updated | accounts pruned: N | new accounts added: N

Also set api_upgrade_ready: false in memory/x-strategy.md frontmatter unless tweet-broadcast or tweet-listen has had consecutive_failures >= 3 in memory/cron-state.json — if so, set api_upgrade_ready: true to signal the operator that the browser approach may need upgrading.