teleo-codex/skills/tweet-decision.md

Skill: Tweet Decision

Quality-filtered pipeline from learned claims to public tweets. The goal: every Teleo agent is a top 1% contributor in their domain's social circles on X — through contributing value, not volume.

When to Use

After the learn-cycle identifies tweet candidates. Also when an agent wants to proactively share a synthesis of recent learning.

Process

Step 1: Candidate assessment

For each tweet candidate from learn-cycle:

Novelty check:

• Has this already been widely discussed on X in the agent's domain?
• Is the agent's audience likely to already know this?
• Does the agent's interpretation add something new?

Evidence check:

• Can the claim be traced back through the evidence chain?
• Is the evidence strong enough to stake the agent's credibility on?
• Are there caveats or limitations that should be acknowledged?

Audience value check:

• Does this help the agent's followers make better decisions?
• Does this connect dots that others in the space haven't connected?
• Would a domain expert find this valuable or obvious?

Step 2: Volume filtering

If the agent has many candidates from a single learn cycle:

• Rank by importance: Which claims most change the landscape?
• Select top few: Maximum 2-3 tweets from a single cycle
• Consider synthesis: Would combining multiple claims into one thread be more valuable?
• Hold the rest: Claims can be tweeted later or combined with future learning

Rule: High signal, low noise. The agent's reputation is built on the quality of every single tweet, not the quantity. One great synthesis thread per week beats daily information relay.

Step 3: Timing decision

Not every tweet should go out immediately. Experiment with optimal waiting period, then vary:

Faster response (minutes to hours):

• Breaking developments that change the domain landscape
• Time-sensitive market information (Rio)
• Safety-critical findings (Logos)
• Corrections to the agent's own previous positions

Standard response (hours to a day):

• Novel claims that benefit from reflection
• Connections between recent developments
• Evidence that updates an ongoing debate

Slow response (days):

• Deep synthesis combining multiple recent learnings
• Position updates that need careful reasoning
• Nuanced topics where the agent wants to get the framing right

The agent can always choose to wait. If unsure, wait. The credibility cost of a hasty tweet exceeds the value of being first.

Step 4: Draft generation

The tweet (or thread) should:

• Be in the agent's distinctive voice
• Lead with the insight, not the source
• Include enough context for non-experts to understand significance
• Link to evidence or reasoning when space permits
• Acknowledge uncertainty when present (this builds credibility)
• Never be a bare claim relay — the agent's interpretation is the value

Thread vs single tweet:

• If the insight fits in one tweet: single tweet
• If the reasoning chain matters: thread (2-5 tweets)
• If combining multiple learnings: synthesis thread (3-7 tweets)
• Never thread for the sake of threading — each tweet must earn its place

Step 5: Quality gate

Before publishing, verify:

• Evidence chain is solid (claim → evidence → source)
• Agent voice is authentic (not generic AI prose)
• Would a domain expert respect this? (the 1% test)
• Is this tweet a net positive for the agent's reputation?
• No confidential information, no unverified claims presented as fact
• Timing is appropriate (not reactive, considered)

If any check fails: hold, revise, or discard.

Step 6: Publish and record

• Post tweet/thread
• Record in agent's positions/ folder if it represents a public position
• Update public_thread field on any relevant positions
• Track engagement for feedback (but never optimize for engagement over quality)

Anti-Patterns

News relay: Just restating what happened. The agent must add interpretation. Engagement farming: Hot takes designed to provoke, not inform. Agents build credibility through depth, not controversy. Thread padding: Adding tweets to a thread that don't earn their place. False certainty: Presenting speculative claims as established fact. Excessive hedging: So many caveats that the insight disappears. Be honest about uncertainty but still have a point of view. Reactive tweeting: Responding to every development. The agent's timeline should reflect considered thought, not a news feed.

Output

• Published tweet/thread with URL
• Updated position records (if applicable)
• Engagement tracking (for quality feedback, not optimization)
• Timing data (for experimentation — what wait periods produce best reception?)