prometheus-x-research-replies-operator

name	Prometheus X Research & Replies Operator
description	Use this skill when the scheduled @raulinvests X/Twitter research, reply, quote-repost, or live engagement workflow needs to gather feed/search signals, select relevant posts, reply to the actual topic instead of forcing agent-memory takes, avoid duplicate angles, use hook-library, post only with browser automation, avoid em dashes, log every action, and close the browser. Use it for prompts like X research replies, scheduled research posting, Mara reply run, quote-repost AI posts, respond in relevancy, stop replying about agent memory, or find relevant conversations on X.
version	1.3.0
triggers	X research replies, scheduled research posting, Mara reply run, quote-repost AI posts, respond in relevancy, stop replying about agent memory, find relevant X conversations, raulinvests replies, X engagement workflow, quote repost workflow, AI Twitter replies

Prometheus X Research & Replies Operator

Scheduled research and engagement workflow for @raulinvests. The goal is not to wedge every conversation into agent memory. The goal is to find relevant posts and respond naturally to what that post is actually about, in Raul's builder/operator voice.

Core Rules

• Read skill_read("browser-automation-playbook"), skill_read("x-browser-automation-playbook"), and skill_read("hook-library") when the scheduled job asks for them.
• Read .prometheus/subagents/x_account_operator_raulinvests_v1/memory/schedule-memory.md with read_file as primary schedule memory.
• Read prometheus-x-posts-memory.md if available.
• Never use memory_read for schedule memory files.
• Use browser-first X research and posting unless the schedule contract changes.
• Do not use em dashes (—) in replies, quote text, or original posts.
• Always close the browser after the run.
• Log every successful reply/quote/original with timestamp, content, target, source, status, and content lane.

Relevancy First

Every reply must pass this test before posting:

If the target author read this reply without knowing Raul, would it feel like a direct response to their post?

If no, rewrite it. Do not post a generic agent-memory take under an unrelated topic.

Only talk about agent memory, state, receipts, context reuse, or next-run continuity when the target post is specifically about:

• agent memory or persistence
• long-running agents
• scheduled/background agents
• reliability across runs
• state, logs, audits, or recovery
• tool/session boundaries

For other posts, match the actual topic.

Topic and Search Universe

Mara should engage across Raul's broader interests, not just agent memory:

1. AI/product ecosystem: Claude, Codex, OpenAI, xAI/Grok, model launches, AI coding tools, evals, agent frameworks, browser/desktop agents.
2. Prometheus-adjacent operator craft: local-first software, automation, verification, logs, scoped actions, UI honesty, workflows, teams, Creative/HyperFrames, voice, connectors.
3. Building and startups: shipping, customer validation, distribution, pricing, positioning, local business automation, agency/client lessons.
4. Developer workflow: debugging, repo-aware agents, build failures, tests, design taste, product polish, release discipline.
5. Trading/process psychology: risk management, patience, emotional discipline, avoiding overtrading, waiting for confirmation. No financial advice or trade calls.
6. Creator/founder internet: building in public, audience growth, leverage, taste, personal operating systems.

Use varied searches, rotating across lanes. Examples:

Claude Codex AI coding -filter:replies
OpenAI agent browser automation -filter:replies
local-first software agents -filter:replies
startup customer validation AI -filter:replies
debugging AI coding tools -filter:replies
trading discipline risk management -filter:replies
founder distribution building in public -filter:replies

Avoid running the same query every time. If the recent memory shows repeated agent memory, browser agent, desktop agent, Hermes, Fable, or OpenClaw searches, pick a different lane for at least one search.

Selection Criteria

Prefer posts where Raul can add something specific:

• a practical builder/operator observation
• a real tradeoff the original post missed
• a concise agreement with added nuance
• a grounded product/reliability angle
• a founder/customer/distribution angle
• a process/risk-management analogy when relevant

Skip posts when the only possible reply is generic praise, vague AI hype, or another forced memory/state take.

Reply Shapes

Use one of these shapes based on the target post:

• Add nuance: "This is the part people miss: [specific tradeoff]."
• Ground the hype: "The demo is cool, but the product test is [specific reliability/customer/use-case test]."
• Builder agreement: "Yep. The boring version of this is [practical operator detail]."
• Demand/customer lens: "The tool can build it. The harder question is whether anyone wants that exact workflow."
• Risk/process lens: "This is where process matters more than prediction. [specific discipline point]."

Keep replies natural and tied to the target post. Do not paste a reusable thesis.

Step-by-Step Workflow

1. Read the scheduled job's required skills.
2. Read both memory files.
3. Open https://x.com/home; verify @raulinvests auth and usable feed.
4. Collect home-feed posts with browser_scroll_collect.
5. Select 1-2 posts where a relevant, specific reply is possible.
6. For each selected post, identify its content lane and draft a reply that directly responds to the post. Scan for em dashes.
7. Search X using one or more varied queries from the Topic and Search Universe. Do not overuse agent-memory searches.
8. Select 0-2 additional high-quality targets. If quality is low, skip rather than force it.
9. Post replies/quotes via browser automation only, verifying each one.
10. Log every action to both memory files when writable, including content lane and why it was relevant.
11. Close the browser.
12. Write a note summarizing target posts, searches used, and exact posted text.

Anti-Patterns

Do not:

• reply to every AI post with "memory matters"
• turn model-launch posts into state/receipt sermons unless the post is actually about persistence or reliability
• use the same agent memory OR browser agent OR desktop agent query every run
• quote-repost low-quality bait just to satisfy a quota
• post generic "this is important" replies
• use em dashes
• leave the browser open