# Skill: X Publish Mandatory eval and approval pipeline for all X activity. Nothing goes out without review. Every post is a public commitment of the collective's credibility. **The standard: every Teleo agent is a top 1% contributor in their domain's circles on X. If a post wouldn't earn respect from a domain expert, it doesn't go out.** ## The Rule No agent publishes to X autonomously. Every post goes through: 1. **Agent drafts** → writes the post + self-eval 2. **Review queue** → draft enters the approval queue with eval attached 3. **Reviewer approves/rejects/requests changes** → with written comments 4. **Agent publishes** → only after approval 5. **Agent logs** → records the published post for tracking No exceptions during the bootstrap phase. This relaxes per-agent as voice calibrates and trust accumulates. --- ## Step 1: Draft + Self-Eval The agent writes the post and fills out the eval. Both go into a single file at `agents/{name}/x-queue/{timestamp}-{slug}.md`: ```yaml --- type: x-draft agent: clay status: pending # pending | approved | changes-requested | rejected | published created: 2026-03-15T14:00:00Z post_type: original | reply | quote | thread reply_to: "URL if reply or quote" topic: "2-3 word topic" kb_grounding: [] # claim filenames this post draws from --- ``` ```markdown # Draft [The full post text. For threads, separate tweets with ---] # Self-Eval ## Grounding - **KB-grounded or novel?** [grounded in X claims / partially grounded / novel interpretation] - **Evidence chain:** [can this be traced back through claims → evidence → source?] - **Confidence:** [how sure am I this is right?] ## Quality - **Would a domain expert respect this?** [yes/no + why] - **Does this add interpretation, not just relay?** [what's the agent's unique angle?] - **Voice check:** [does this sound like me, or generic AI prose?] ## Risk - **Names anyone?** [yes/no — if yes, who and what's the judgment?] - **Could be read as advice?** [financial / medical / safety / none] - **Outside my domain?** [yes/no — if yes, which domain and why am I speaking?] - **Virality risk:** [if this went viral for the wrong reasons, what's the damage?] - **Sensitivity score:** [0.0-1.0, where >0.5 means extra scrutiny needed] ## Strategic - **Why now?** [what makes this timely?] - **Thread context:** [is this part of an ongoing conversation? who are we engaging?] - **Expected audience:** [who specifically should find this valuable?] ``` ### Draft quality requirements Before submitting for review, the agent must verify: - [ ] Post passes all checks from `skills/tweet-decision.md` Steps 1-5 - [ ] Evidence chain is walkable (post → KB claim → evidence → source) - [ ] Voice is distinctive (not interchangeable with any other agent or generic AI) - [ ] Post earns its length — every sentence adds value - [ ] Uncertainty is acknowledged where it exists (builds credibility) - [ ] No unverified claims presented as fact - [ ] If quoting/replying, the original is accurately represented --- ## Step 2: Review Queue Pending drafts live in `agents/{name}/x-queue/` with `status: pending`. ### Who reviews **Bootstrap phase (now):** Cory reviews all posts. No agent posts without human approval. **Graduated phase (per-agent, earned):** After an agent has 20+ approved posts with zero rejections in the last 10, the agent can graduate to peer review: - Domain agents reviewed by Leo - Leo reviewed by the most relevant domain agent - Cross-domain posts always get both Leo + domain peer **Autonomous phase (future):** After 50+ approved posts with consistent quality, Tier 1 posts (KB-grounded commentary, replies in existing threads) can self-publish with async log review. Tier 2+ still requires approval. ### Graduation is per-agent, not collective Clay might graduate while Theseus is still in bootstrap. Each agent earns autonomy independently based on their track record. --- ## Step 3: Review The reviewer reads the draft + self-eval and adds a review block to the same file: ```markdown # Review **Reviewer:** [name] **Verdict:** approved | changes-requested | rejected **Date:** 2026-03-15T16:00:00Z ## Comments [Specific feedback. What works, what doesn't, what to change.] ## Changes requested (if applicable) - [specific change 1] - [specific change 2] ``` ### Review checklist 1. **The 1% test** — Would a domain expert find this valuable? Not just "not wrong" — actively good. 2. **Voice** — Does this sound like the agent? Could you tell which agent wrote it without seeing the name? 3. **Grounding** — Is the evidence chain real, or is the agent improvising beyond its KB? 4. **Risk** — Any reputational landmines? Name checks, advice risk, domain overreach? 5. **Timing** — Is this reactive or considered? Would waiting improve it? 6. **Value density** — Does every sentence earn its place? Could this be shorter? 7. **Intellectual honesty** — Does it acknowledge uncertainty where it exists? Or is it performing false confidence? ### Rejection criteria (instant reject, don't request changes) - Presents unverified claims as fact - Makes value judgments about named individuals without strong evidence - Could be reasonably read as financial, medical, or safety advice - Generic AI prose with no distinctive voice - Engagement farming (hot take designed to provoke, not inform) - Speaks with authority outside the agent's domain without flagging it --- ## Step 4: Revision + Approval If changes requested: 1. Agent revises the draft in the same file 2. Updates `status: pending` (for re-review) 3. Adds a "Revision" section explaining what changed and why If approved: 1. Reviewer sets `status: approved` 2. Agent publishes to X 3. Agent updates file: `status: published`, adds `published_url` and `published_at` --- ## Step 5: Post-Publish Log After publishing, the agent updates the file with: ```yaml published_url: "https://x.com/..." published_at: 2026-03-15T17:00:00Z ``` And adds a tracking section: ```markdown # Tracking ## 24h metrics impressions: 0 likes: 0 replies: 0 retweets: 0 quotes: 0 new_followers: 0 ## 7d metrics [same fields, updated at 7 days] ## Notable interactions - [any replies worth noting — domain experts engaging, challenges, amplification] ## Retrospective [Agent's own assessment after seeing the response: what worked, what didn't, what to do differently] ``` --- ## Weekly Review Cycle Every week, each active agent reviews their published posts: 1. **Performance scan** — which posts got engagement and why? 2. **Voice consistency** — do all posts sound like the same person? 3. **Domain drift** — any posts that strayed outside territory? 4. **Network discovery** — new handles found from interactions → update `network.json` 5. **Lesson extraction** — what did this week teach about what resonates? 6. **Queue management** — any drafts sitting too long? Kill or revise stale drafts. Write findings to `agents/{name}/x-queue/weekly-review-{date}.md`. --- ## Anti-Patterns **Volume over quality:** Drafting many mediocre posts hoping some get approved. Draft fewer, better posts. **Self-eval gaming:** Writing "yes" to every quality check without actually evaluating. Reviewers should flag hollow self-evals. **Revision fatigue:** Submitting the same post with minor tweaks after rejection. If rejected, rethink — don't polish. **Reactive drafting:** Rushing a draft because something is trending. The agent's credibility compounds over years. One news cycle doesn't matter. **Echo chamber:** Only engaging with accounts that already agree. The best posts challenge the agent's own audience. **Thread inflation:** A single insight inflated to a 7-tweet thread. If it fits in one tweet, post one tweet. --- ## File Structure ``` agents/{name}/ x-queue/ 2026-03-15-ai-video-costs.md # draft → review → publish lifecycle 2026-03-15-claynosaurz-annecy.md weekly-review-2026-03-15.md # weekly retrospective network.json # monitored X accounts ``` --- ## Metrics That Matter (in order) 1. **Expert engagement rate** — are domain experts replying/quoting? (quality of attention > quantity) 2. **Reply quality** — are replies substantive or just "great thread"? 3. **Follower quality** — are new followers domain-relevant people? 4. **Rejection rate trend** — should decrease over time as agent calibrates 5. **Impressions per post** — baseline reach (lagging indicator, don't optimize for this) Metrics that DON'T matter: total follower count, posting frequency, like count in isolation.