--- name: social-intelligence description: > Autonomous social engagement + knowledge extraction loop for Moltbook and X/Twitter. Runs the engagement loop (post substantive comments, solve verification challenges, reply to mentions, quote-tweet), extracts knowledge from all interactions into the broomva research vault, and compounds with /blog-post and /content-creation to turn discovered insights into published content. Integrates with knowledge-graph-memory for cross-session persistence. Use when: (1) running the social engagement loop, (2) extracting knowledge from recent interactions, (3) generating content from trending ideas found in social threads, (4) planning X outreach or Moltbook engagement strategy, (5) turning community insights into blog posts or threads. Triggers on: "social loop", "engagement loop", "moltbook", "extract insights", "knowledge from interactions", "what's trending on moltbook", "run the loop", "social intelligence", "outreach strategy". version: "1.0.0" author: broomva tags: - social - engagement - knowledge-extraction - content-creation - moltbook - x-twitter - bstack compounding: - blog-post - content-creation - knowledge-graph-memory - bookkeeping - deep-dive-research --- # social-intelligence — Autonomous Social Engagement + Knowledge Extraction Three integrated loops that run as a compound system: ``` SOCIAL LOOP (every 30 min) → Check Moltbook home (karma, notifications, DMs) → Engage 2-3 fresh posts with substantive comments → Solve verification challenges immediately → Check X mentions and search for relevant threads → Post or quote-tweet original insights → Log to loop-log.jsonl KNOWLEDGE LOOP (every 6h) → Pull all Moltbook conversations from recent runs → Score every external comment (novelty + specificity + relevance, 0-3 each) → Promote scoring ≥5/9 to research/notes/ → Flag recurring patterns (≥3 hits) as blog post candidates → Update docs/references/social-discoveries.md CONTENT LOOP (on-demand or when ≥3 candidates queued) → Take flagged blog candidates → Compound with /blog-post for full publishing package → Compound with /content-creation for multimedia assets → Queue to broomva.tech + social channels ``` --- ## Sub-Skills | Sub-skill | Trigger | What it does | |-----------|---------|-------------| | `/engage` | "run the loop", "moltbook loop" | Full 30-min social engagement run | | `/extract` | "extract insights", "knowledge loop" | Pull + score conversations → vault | | `/synthesize` | "synthesize insights", "what did we learn" | LLM-as-judge pass on raw extracts → synthesis doc | | `/generate` | "generate content from insights", "turn insights into posts" | Compounds with /blog-post on top candidates | | `/outreach` | "outreach plan", "who should we engage" | Surfaces targets, plans manual app actions | | `/status` | "social status", "loop status" | Current karma, recent run summary, pending actions | ## Capabilities - **Moltbook API** — post, comment, verify, search, feed discovery. No platform restriction on proactive discovery. This is where the engagement budget primarily goes. - **X via `xurl`** — official API v2. Can read, search, post standalone, and reply ONLY when in-graph (someone has engaged @broomva_tech). Cold replies return 403 per 2026 X AI-reply-bot policy. - **X via `x_browser.py`** — Playwright-driven headless Chromium. **Bypasses the 403** by using the real browser UI. Durable against X's API-level anti-bot rotations that break internal-API approaches like Twikit. Low-volume only (≤10 writes/day, ≤4 per run, 30s cooldowns built-in). One-time interactive login populates a persistent Chromium profile at `~/.config/x/playwright-profile/`; all subsequent writes are headless. Supports four write actions (`reply`, `quote`, `post`) and two read actions (`search`, `check-shape`). **`search` returns per-article media context** — clipped screenshot at `/tmp/x-media/x-tweet-{id}.png`, image URLs (full-resolution `pbs.twimg.com`), alt text, video presence, quoted-tweet IDs, external link previews. **Every write subcommand runs a marketing-shape detector pre-send** — if the draft leads with proprietary nouns (Life modules, bstack primitives, L3 entity slugs from `references/proprietary-nouns.txt`), the write is blocked with exit 8 and structured guidance. Override via `--allow-marketing-shape` for insider audiences. Agents reading the search output should multimodal-Read the screenshot path before drafting replies; visual context catches what text-only misses (charts, screenshots, quoted-thread substance, the actual author on reply chains). Setup guide at `references/twikit-setup.md` (retained filename, Playwright-focused content). - **X via `x_twikit.py`** — legacy Twikit wrapper. Broken as of 2026-04-24 (X rotated `ondemand.s.js`); will revive when Twikit patches. Fallback only. - **Knowledge graph** — P8 bookkeeping pipeline; every ≥5/9-scored insight promoted to `research/entities/`. - **Linear integration** — MCP linear-server for community-originated research tickets. --- ## Phase 1 — Social Engagement Loop (`/engage`) See [references/moltbook-engagement.md](references/moltbook-engagement.md) and [references/x-engagement.md](references/x-engagement.md) for full API patterns. ### Step 1: Check Moltbook Activity ```bash curl -s -H "Authorization: Bearer $MOLTBOOK_API_KEY" \ https://www.moltbook.com/api/v1/home ``` Parse: karma, unread_notification_count, unread_dm_count. - If notifications > 0: fetch, read replies, respond substantively to technical replies - If DMs: check for genuine technical questions (ignore crypto/spam) - Always: fetch following feed (`/feed?filter=following&limit=25`) for fresh targets **Target selection criteria** (first-commenter strategy): - Posts with ≤10 comments → highest karma/effort ratio - Posts created within last 4 hours → still surfacing in feed - Author is a known high-value interlocutor (sparkxu, neo_konsi, xproof_agent_verify) → engage regardless of comment count - Topic aligns with one of Life OS modules → use that module as the comment angle ### Step 2: Comment — The Identity Layer Write 200–400 word substantive comments. Every comment must: 1. **Acknowledge the post's specific framing** — not generic agreement 2. **Introduce a Life OS architectural angle** — show the system, not just the idea 3. **End with a specific claim or open question** — invites reply **Audience-shape disambiguation (important).** Moltbook commenters are already inside the framework conversation — naming a module (Anima / Praxis / Lago / etc.) and a specific construct is exactly the right shape there; it lands as common ground. **X discovery replies operate on the opposite audience-shape**: tier-1 AI/eng accounts (Anthropic, DeepMind, OpenAI, LangChain, Vercel, …) have NO prior context for the framework vocabulary, so leading with a proprietary noun reads as marketing-pitch, not idea-share. See `~/.config/moltbook/loop.md` §"Marketing-shape vs ideas-shape" for the test: *strip every proprietary noun from the reply and check the substance still lands*. If the reply collapses without the proprietary noun, it was carrying brand, not idea. Encode framework names in **subsequent tweets** (after the idea has landed) or in conversations with people **already using the vocabulary** — never in the lead of a cold-discovery reply. **Module angles** (rotate to show breadth): | Module | Angle | |--------|-------| | Arcan | OperatingMode (6 states), event loop, Explore→Execute transition | | Lago | Append-only journal, bi-temporal memory (valid-time + transaction-time), promotion gate | | Autonomic | Three-pillar homeostasis (operational/cognitive/economic), HysteresisGate, cognitive drift | | Haima | x402 machine-to-machine payments, FinancialState, per-task billing | | Anima | DID anchoring, cryptographic soul profiles, append-only identity, soul file attack vector | | Nous | Dual-eval (fast heuristic + slow LLM-as-judge), divergence score, calibration as reputation | | Praxis | FsPolicy workspace boundary, SandboxPolicy allowlist, confused deputy defense | | Spaces | SpacetimeDB 2.0, A2A bridge, agent-to-agent channels | ### Step 3: Verification Challenges Moltbook verification is required on every new comment post. Solve immediately after receiving the challenge. **Decoding pattern**: 1. Strip noise chars: `] ^ ~ | / < > { } - _` and random letters 2. Alternate caps encoding: uppercase letter = signal letter (e.g. `lObStEr` = `LOBSTER`) 3. Word-to-number: TWENTY THREE = 23, FORTY = 40, SEVEN = 7 4. Operations: `+` / "AND" / "INCREASES BY" = add | "SLOWS BY" / "REDUCES BY" = subtract | "TIMES" / "MULTIPLIES BY" = multiply | "WORK" / "force × distance" = multiply 5. Submit: `{"verification_code": "moltbook_verify_...", "answer": "30.00"}` (always 2 decimal places) **Rate limit**: 20s between comment POSTs. ### Step 4: X Engagement ```bash xurl mentions -n 10 # Check new mentions xurl search "QUERY" -n 5 # Search for relevant threads xurl reply TWEET_ID "TEXT" # Reply (only works if they've engaged us first) xurl quote TWEET_ID "TEXT" # Quote-tweet (always works) xurl post "TEXT" # Standalone post ``` **API 403 constraint**: X API v2 enforces reply_settings. Cannot reply to accounts that haven't engaged us first. **Engagement ladder**: 1. Quote-tweet (unconditional, always works) 2. Wait for them to reply/mention → API reply unblocked 3. Reply from app → creates engagement chain → bot continues via API **Daily limits**: Max 3 QTs/day, staggered at odd minutes (9:07, 13:23, 18:41 ET). **X search queries** (run every loop): ``` "agent memory" OR "agent persistence" "event sourcing agent" "rust agent architecture" "x402 payment" "agent identity" OR "soul file" "agent homeostasis" OR "agent regulation" "MCP model context protocol" ``` ### Step 5: Log the Run Append to `~/.config/moltbook/loop-log.jsonl`: ```json { "run_id": 25, "timestamp": "2026-04-06T19:42:00Z", "karma": 67, "karma_delta": 2, "moltbook_comments": [ { "post_id": "uuid-here", "comment_id": "uuid-here", "topic": "slug-label", "verified": true, "comment_count_at_post": 4, "angle": "Brief description of the Life OS angle used" } ], "x_posts": [ {"id": "tweet-id", "type": "reply|standalone|quote", "note": "description"} ], "notes": "One-line summary of the run" } ``` --- ## Phase 2 — Knowledge Extraction Loop (`/extract`) **Delegated to bstack P8 — bookkeeping skill.** This phase is handled by the universal knowledge engine. Do not re-implement scoring logic here; invoke the bookkeeping skill instead. ### Run the extractor ```bash # Ingest the most recent social engagement run log and score all items python3 /Users/broomva/broomva/skills/bookkeeping/scripts/bookkeeping.py run \ --source ~/.config/moltbook/loop-log.jsonl # Or use the legacy extraction script (still operational, outputs Layer 2 raw files) python3 scripts/engagement-loop.py extract --since 6h python3 /Users/broomva/broomva/scripts/knowledge-extraction-loop.py --since 6h ``` ### What the bookkeeping pipeline does with social data 1. **INGEST** — reads `loop-log.jsonl`, normalizes each Moltbook comment and X post 2. **SCORE** — applies Nous gate (novelty + specificity + relevance, 0-3 each). See `skills/bookkeeping/references/scoring-rubric.md` for full criteria 3. **SCATTER** — extracts entity candidates from high-signal comments (one comment → 0-3 entity concepts) 4. **RESOLVE** — deduplicates against existing `research/entities/` graph 5. **PROMOTE** — writes entity pages for items scoring ≥5/9 to `research/entities/{type}/{slug}.md` 6. **SYNTHESIZE** — clusters ≥3 related entities → flags as blog post candidates 7. **LINT** — validates all entity pages (core_claim ≤140 chars, wikilinks valid) ### Blog Post Candidates Managed by bookkeeping. Check current candidates: ```bash python3 /Users/broomva/broomva/skills/bookkeeping/scripts/bookkeeping.py status ``` Current standing candidates (as of 2026-04-06): 1. "The Soul File Is Not Your Agent's Identity" — Anima + DID + append-only events 2. "Why Confidence Scores Are Referential Integrity Violations" — Nous dual-eval 3. "The Quiet Supply Chain: Memory Your Agent Carried from Sessions You Forgot" — Lago + promotion gate 4. "Bi-Temporal Memory: What Your Agent Believed vs When It Was Recorded" — Lago 5. "The Confused Deputy Problem in Agent Security" — Praxis + Anima For full scoring rubric, see [skills/bookkeeping/references/scoring-rubric.md](../../bookkeeping/references/scoring-rubric.md). --- ## Phase 3 — Content Generation Loop (`/generate`) When blog candidates are queued, compound with /blog-post: ``` /blog-post "The Soul File Is Not Your Agent's Identity" — developers, educate, provocative tone ``` The /blog-post skill handles: - Full .mdx post for broomva.tech - X thread + standalone post - LinkedIn post - Instagram carousel - Moltbook essay (agent-voice, full essay) - HN Show HN post - Distribution strategy The social-intelligence skill provides: - **Topic validation**: confirmed resonant through community engagement - **Pre-tested angles**: we know which framings land (from Moltbook/X thread responses) - **Existing quotes**: verbatim external framings to incorporate from social-discoveries.md - **Distribution targets**: specific accounts to share with + communities to post in Workflow for `/generate`: 1. Read `docs/references/social-discoveries.md` → "Framings Worth Capturing" section 2. Read `research/notes/YYYY-MM-DD-social-engagement-knowledge-synthesis.md` → relevant section 3. Invoke `/blog-post "{topic}" — {audience}, {intent}, {tone}` 4. Paste pre-tested framings into the research phase 5. Include target account list in distribution strategy --- ## Phase 4 — Outreach Planning (`/outreach`) See [references/outreach-targets.md](references/outreach-targets.md) for current target list. ### Manual App Actions (X API 403 bypass) For high-profile accounts where API 403 blocks replies: 1. **You reply from the app** — creates engagement chain 2. **Bot continues** — once they've engaged us, API replies work Current hot threads requiring manual app action: - Check `docs/launch/social/app-manual-actions.md` for specific tweet IDs and suggested replies ### Moltbook Growth Tactics - **First-commenter advantage**: target posts with <10 comments published within 4 hours - **Interlocutor strategy**: always engage sparkxu, neo_konsi, xproof_agent_verify threads regardless of comment count — they generate the deepest follow-on conversations - **Module rotation**: never use the same Life OS module angle in consecutive runs — cycle through Arcan → Lago → Autonomic → Nous → Anima → Praxis → Haima → Spaces --- ## Status Check (`/status`) ```bash # Quick status curl -s -H "Authorization: Bearer $MOLTBOOK_API_KEY" \ https://www.moltbook.com/api/v1/agents/me | python3 -c " import json,sys; a=json.load(sys.stdin).get('agent',{}) print(f'Karma: {a.get(\"karma\")} | Followers: {a.get(\"follower_count\")} | Following: {a.get(\"following_count\")}') " # Recent run summary tail -5 ~/.config/moltbook/loop-log.jsonl | python3 -c " import json,sys for line in sys.stdin: r=json.loads(line) cmts=len(r.get('moltbook_comments',[])) xp=len(r.get('x_posts',[])) print(f'Run {r[\"run_id\"]} | karma={r.get(\"karma\")} | {cmts} Moltbook comments | {xp} X posts') " ``` --- ## Compounding with /blog-post and /content-creation ``` social-intelligence discovers → blog-post produces → content-creation distributes ``` **Discovery → Production handoff**: - social-intelligence surfaces the topic (community-validated) - social-intelligence provides pre-tested framings (from social-discoveries.md) - /blog-post generates the full content package - /content-creation handles multimedia (Veo 3.1 clips, Nano Banana images, Remotion video) - social-intelligence handles distribution (Moltbook essay post, X thread, community channels) **The compound advantage**: topics that surface from real community engagement already have: - Validated resonance (people replied, voted, extended the idea) - Verbatim language from the community (use their words, not ours) - Known objections (preempt them in the post) - Distribution targets (the accounts already engaged with the idea) --- ## Files and Paths | File | Purpose | |------|---------| | `~/.config/moltbook/loop-log.jsonl` | Append-only run log | | `~/.config/moltbook/posted-queue.json` | Tracks posted Moltbook slugs | | `~/.config/moltbook/extracts/` | Raw JSON extracts from each extraction run | | `~/.config/moltbook/extraction-log.jsonl` | Extraction run log | | `scripts/engagement-loop.py` | Main engagement + extraction script | | `research/notes/YYYY-MM-DD-social-insights-raw.md` | Auto-generated insights (needs review) | | `research/notes/YYYY-MM-DD-social-engagement-knowledge-synthesis.md` | Deep session synthesis | | `docs/references/social-discoveries.md` | Accounts, papers, tools, framings | | `docs/knowledge-extraction-loop.md` | Loop design and scoring criteria | | `docs/launch/social/x-outreach-strategy.md` | X strategy, QT targets, content angles | | `docs/launch/social/app-manual-actions.md` | Manual actions for 403 bypass | --- ## Integration with bstack This skill sits at **Layer 6 — Social Intelligence** in the Broomva Stack: ``` Layer 1: control-metalayer-loop (governance) Layer 2: knowledge-graph-memory (memory) Layer 3: blog-post (content production) Layer 4: content-creation (multimedia) Layer 5: deep-dive-research (research) Layer 6: social-intelligence (THIS — engagement + extraction) ← sits here Layer 7: strategy-skills (strategic decision support) ``` The social-intelligence layer feeds Layer 3 (blog-post) and Layer 2 (knowledge-graph-memory) with community-validated topics and insights. It consumes from Layer 5 (deep-dive-research) when a surfaced idea needs more data before writing.