Social Autoposter Setup

Social Autoposter Setup

Set up social-autoposter end to end. Treat the user's setup request as a terminal goal, not as the beginning of an interview.

Operating contract

• Keep taking the next safe setup action until the system is working end to end.
• Do not ask whether to run setup, install a dependency, inspect status, connect X, scan the profile, research the website, save inferred fields, seed topics, or retry a recoverable failure. Do them.
• An explicit request to set up social-autoposter authorizes its owned local runtime installation and importing only x.com/twitter.com session cookies into its managed browser. Briefly warn that macOS keychain prompts may appear, then proceed; do not wait for another yes/no reply.
• Use existing configuration, the user's message/context, their connected X profile, public website pages, and reasonable factual inference before asking for information.
• Ask at most one bundled blocking question, and only when required information cannot be discovered or safely inferred. The usual legitimate blocker is: there is no configured project, no clear product URL in context or the X profile, and no way to identify what product to market.
• Never post a draft during setup unless the user explicitly asked for that.
• Never hand-run the X cycle. Do not run run-twitter-cycle.sh, claude_job.py, or any cycle script directly, and never offer to "trigger a cycle" or "run one now" so a draft appears faster. Only the launchd kicker may fire the cycle, with its required environment. A manual kick produces an empty-plan artifact and blocks the autopilot. Verification means scheduling the autopilot and letting the kicker drive it; you wait and poll, you never trigger.
• Do not edit the MCP server, plugin source, or an unrelated user workspace to work around setup failures. Use the product's setup/install tools.

Definition of done

Do not report setup complete until all of these are true:

1. The owned runtime is installed and ready.
2. At least one project is ready with name, website, description, ICP, voice, and search topics. For the personal-brand persona, the search topics and the grounding corpus come from the user's dictation interview (below), not from the profile scan alone.
3. Search topics have been seeded into the backend. Seeding is POSTPONED until after the dictation interview so the topics reflect what the user wants to be in conversations about, not only what they already posted.
4. X is connected and the real handle has been auto-detected.
5. The draft autopilot is scheduled and has produced a draft card without posting. A returned/pending review batch is the strongest success signal. If X simply has no matching supply, report that precise result only after configuration/auth/runtime checks pass.

Architecture

• Config: ~/social-autoposter/config.json — projects[] (what to post about) and accounts (where to post).
• Data + stats: backend HTTP API at https://s4l.ai, scoped by a stable per-install identity in identity.json. There is no local Postgres or DATABASE_URL.
• Search topics: the X cycle reads project_search_topics, seeded automatically from each project's search_topics. No topics means nothing to scan. search_topics is the ONLY field that changes what gets scanned; every other persona field only shapes the draft after a thread is found. For the persona, the profile scan is backward-looking (only what the user already posted), so topics are sourced primarily from the dictation interview.

Choose the path

• If the social-autoposter MCP tools are connected (project_config, runtime, queue_setup, approve_drafts, get_stats, dashboard), use the MCP path. Do not hand-edit config.json.
• If only the CLI/skill is installed, use the CLI fallback.

MCP path

1. Inspect and repair the environment

Call project_config in status mode and runtime (action:'status') immediately.

If the runtime is not ready:

1. Call runtime with action:'install'.
2. Poll runtime (action:'status') until it succeeds or returns a concrete failure.
3. For a recoverable/partial failure, call runtime action:'install' again and continue. Do not send the user away to install Chrome, Python, uv, Chromium, or browser-harness manually; the owned installer handles them.

Preserve any already-ready project or X connection. Resume from the first incomplete milestone instead of restarting the interview.

2. Connect X and learn the user's voice

If X is not connected:

1. Call project_config with action:'detect_x_sources'.
2. Choose recommended, preferring a source whose x_session is present. Do not ask the user to choose a browser profile when the tool can choose.
3. Tell the user in one short progress update that macOS may show browser Safe Storage prompts and they should enter their Mac password and click Allow or Always Allow.
4. Immediately call project_config with action:'connect_x', confirm:true and the selected x_source. The setup request itself is authorization; do not add a separate consent round-trip.
5. If the result is transient, retry. If it opens managed Chrome in needs_login, tell the user to finish signing in to x.com in that window. This is an unavoidable user action, not a product-choice question. Re-run connect_x after sign-in and continue.

Once connected, call project_config with action:'profile_scan'. Treat the returned bio, links, recent posts, and replies as grounding truth for the VOICE, not as the primary source of topics (the scan only shows what they already posted):

• profession/identity;
• voice, casing, phrasing, and tone;
• ICP;
• wording or claims the user avoids;
• recurring themes (reinforcement for topics, not the origin).

Do not ask the user to approve the inferred voice during initial setup. Save a specific, conservative best draft and mention afterward that it can be edited.

Then run the DICTATION INTERVIEW before extracting topics or seeding. This is where the persona's search_topics and grounding corpus come from. Keep the framing CHILL: this is a casual brain-dump, not a form. Invite the user to answer the following in ONE spoken dictation (the Claude input box already supports dictation, so they talk once and you split the answers into fields), and make clear there is no pressure: they can answer as much or as little as they like, skip anything, and come back to the rest whenever they feel like it. Preface the list with one short low-key line saying exactly that (e.g. "No pressure here, ramble as much or as little as you want; you can always come back to this later."), then ask verbatim as a single numbered list:

1. Who are you, and what do you want to be known for? (→ description)
2. What subjects could you talk about for an hour, work and non-work? (→ search_topics; this is the load-bearing answer, it is the only thing that decides what gets scanned on X)
3. Your most contrarian takes — what does everyone in your field get wrong, and what did you used to believe that you have reversed on? (→ content_angle + corpus)
4. What can you explain in 5 minutes that took you years, and what mistake do you watch beginners make over and over? (→ content_angle + corpus)
5. Best or worst thing that happened to you recently, and a failure you learned the most from? (→ corpus, keeps drafts current)
6. Who do you love or hate reading online, and any lines or phrases you say a lot? (→ voice calibration)
7. Anything off-limits (topics, companies, people), and how spicy can we get — safe, opinionated, or provocative? (→ content_guardrails + voice.never)

From the dictation, synthesize the fields: search_topics primarily from answer 2 (fold in recurring scan themes only as reinforcement), the rest from the other answers. Keep the RAW transcript VERBATIM as the persona corpus (do not paraphrase; the actual numbers, opinions, and phrasing are what make drafts sound like them). Pass it as content_corpus when you call engagement_mode (or project_config); it is stored in the persona_corpus.txt sidecar, not config.json. If the user answers only some questions, take what they gave, continue setup without nagging for the rest, and mention once that they can answer the remaining questions any time later to make drafts sound more like them. If the user declines or gives nothing usable, fall back to scan-derived topics.

Only after the dictation is captured do you set the engagement mode / save the persona, which seeds the topics. Do not seed topics from the scan before the dictation.

3. Discover and research the product

Find the product URL in this order:

1. an existing project/config;
2. the user's setup request and conversation context;
3. the connected X profile URL/bio/recent posts;
4. a clearly associated public product discovered with web research.

When one product is clearly supported by the evidence, use it without asking. If several are plausible and no primary product is evident, choose the one most prominent in the current context/profile. Ask one bundled blocking question only if no defensible product can be identified.

Visit the product site with your own browser/fetch tools and read at least five pages when available: homepage, pricing, features/product, about, docs, changelog/blog, FAQ, and customer/case-study pages. From what you actually read, derive:

• name: short lowercase machine slug;
• website;
• description;
• differentiator;
• icp;
• get_started_link;
• content_guardrails;
• voice and search_topics, grounded in the profile scan.

Do not invent features, metrics, customers, or guarantees. If the site is thin, save a conservative factual description rather than stopping for optional details.

4. Save and seed

Call project_config once with the complete inferred project whenever possible. It merges fields, reports missing required fields, seeds search_topics into project_search_topics, and expands them into search queries.

If required fields remain, first attempt to derive them from the sources already collected. Ask the user only if a genuinely unknowable required field blocks readiness. Optional/recommended fields never justify stopping setup.

5. Schedule the autopilot + verify end to end

Schedule the draft autopilot: call queue_setup, then for EACH returned task call the host tool create_scheduled_task (taskId, cronExpression, prompt verbatim; "already exists" is fine). The autopilot then drafts on its own — the launchd kicker fires a draft-only cycle and the queue worker drafts the replies; nothing posts. Then wait: poll the dashboard tool until the pending-draft count rises; a returned review batch is the strongest success signal.

Do not hand-run a cycle to make this happen faster, and do not offer the user to trigger one. Scheduling the autopilot is the only sanctioned way to produce the verification draft; the kicker drives it on its own minute-by-minute schedule.

If no card appears, diagnose the fixable reason, fix it, and let the autopilot retry on its next scheduled cycle:

• missing topics: derive/add topics through project_config, then retry;
• runtime/browser-harness/Chrome issue: run/repair the owned runtime, then retry;
• stale X session: reconnect X, then retry;
• transient backend/network issue: retry once;
• Claude CLI login/usage limit or an interactive X login: report the exact blocker and the single user action required.

Do not enable autopilot automatically. Offer it only after setup is verified, or enable it when the user's original request explicitly included hands-free posting.

Once verification passes (or you reach a precise blocker), call the dashboard tool so the user sees the finished setup rendered visually, then give the completion summary below.

CLI fallback

Use only when MCP tools are unavailable. Execute the flow yourself rather than turning it into instructions for the user.

1. Install/repair: npx -y social-autoposter@latest init
2. Inspect ~/social-autoposter/config.json and preserve existing projects.
3. Discover the product and voice using the same evidence order above. For a personal-brand persona, run the dictation interview (the 7 questions above) and take search_topics + the raw corpus from it, scan themes as backup.
4. Write a complete project with name, website, description, ICP, voice, and search_topics.
5. Seed topics: python3 ~/social-autoposter/scripts/seed_search_topics.py --project <name>
6. Connect X: python3 ~/social-autoposter/scripts/setup_twitter_auth.py connect The user may need to approve a macOS keychain prompt or sign in once in the managed browser; continue automatically afterward.
7. Do not load launchd/autopilot jobs unless explicitly requested, and never hand-run run-twitter-cycle.sh or any cycle script to "verify." A manual kick produces an empty-plan artifact and blocks the autopilot. The cycle runs only when the launchd autopilot is scheduled and the kicker fires it. In CLI fallback, setup is configured once the project is saved, topics are seeded, and X is connected; the first draft appears after the autopilot is scheduled.

Completion summary

Render the dashboard tool once setup is verified, then report outcomes (not a recap of every prompt):

Social Autoposter Setup Complete

Runtime:       ready
Project:       NAME — ready
Topics seeded: N
X/Twitter:     @HANDLE
Verification:  autopilot produced a draft (not posted)
Autopilot:     off
Stats:         https://s4l.ai/stats/HANDLE

If setup is blocked, do not call it complete. State the exact completed milestones, the blocker, and the one user action needed to resume.