# Peec Setup > End-to-end Peec AI project setup — competitor discovery from real AI chats, customer-journey prompt design across Awareness → Consideration → Decision → Retention, topic/tag taxonomy, GSC-based keyword mapping, forum pain-point mining (Reddit, Gutefrage, t3n, OMR), and a categorized executable backlog. Use when the user wants to set up, restructure, or audit a Peec AI project for their own brand or a client. 9 phases, full funnel coverage, real buyer language. - Skill: `antonioblago/peec-setup` (Agent Skill) - Install (CLI): `npx skillmds@latest add antonioblago/peec-setup` - Raw SKILL.md: https://api.skillmd.com/api/skills/antonioblago/peec-setup/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Product & Planning - Author: AntonioBlago (https://skillmd.com/u/antonioblago) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/antonioblago/peec-setup --- # AI Visibility Setup ## Role Take a Peec AI project from empty (or broken) to operator-ready: correct competitors, full-funnel prompts, coherent taxonomy, GSC keyword mapping, forum-mined buyer language, and a categorized executable backlog the client can run for the next 2 weeks. ## Input - `project_id` (resolved via `mcp__peec-ai__list_projects`) - `target_country` — ISO 3166-1 alpha-2 (`DE`, `AT`, `CH`, `US`, `UK`, ...). Default `DE`. Drives SERP/GSC filters and forum source selection. - `prompt_language` — ISO 639-1 (`de`, `en`, `fr`, ...). Default = lowercase of `target_country` (`DE` → `de`). Drives the language Peec prompts are authored in. - Optional: `secondary_languages` — list, default `[]`. Used for multi-market projects (e.g. DE primary + EN secondary). - Optional: `offer_keywords` (retainer, monthly, etc.), `own_domain` - Optional: `scope` — `full` | `audit` | `partial:` | `competitors_only` | `prompts_only` | `taxonomy_only` (default: auto-detected from setup state, see Phase 0) **Resolving language/country at start:** 1. If state file exists with these fields → use them, skip the question. 2. Else if user passed them as arguments → use those. 3. Else infer from `own_domain` TLD (`.de` → DE/de, `.at` → AT/de, `.ch` → CH/de + ask de/fr, `.com` → ASK). 4. Else ASK the user **once** before Phase 1: "Target country (ISO, e.g. DE)? Prompt language (ISO, e.g. de)?". Persist the answer in state. Never silently default to `EN`/`en` when the project has no signal — this corrupts every downstream skill. ## Output A setup report with: before/after counts, funnel distribution (e.g. 5/5/5/5), the single **hero prompt** to win first, the refresh timeline (24h for fresh data), a categorized P0/P1/P2 backlog, and any user-preference memories saved. No dashboards. ## When to use - "Set up Peec for " - "My Peec competitors are wrong / not real competitors" - "Design prompts for my customer journey" - "Map GSC keywords to my Peec prompts" - "Restructure Peec topics / tags" - Audit of an existing AI-visibility tracking setup ## Prerequisites - Peec AI MCP connected (`mcp__peec-ai__*`) - Visibly AI MCP connected (`mcp__visiblyai__*`) — optional, only for GSC - GSC + GA4 connected inside Visibly AI (check via `get_google_connections`) ## State This skill **owns** the setup state file. See [`_shared/SETUP_STATE.md`](../_shared/SETUP_STATE.md) for the full schema and protocol. - **Reads** `/growth_loop/setup_state.json` at Phase 0 to decide the run mode (`full | audit | partial | skip`). - **Writes** the same file at the end of Phase 9 with merged `phases_completed` and a fresh `snapshot`. All other skills in this repo refuse to run without this file — never bootstrap a setup from inside another skill. --- ## Phase 0 — State check & mode selection Always runs first. Cheap (single file read + at most one parallel Peec read in brownfield case). Determines whether the rest of the run is needed at all. ``` 1. Read /growth_loop/setup_state.json 2. If state file MISSING: 2a. Live-detect Peec content (parallel reads): list_brands(project) list_prompts(project, limit=5) list_topics(project) list_tags(project) 2b. If Peec is empty (≤2 brands AND ≤4 prompts AND ≤0 topics): → mode = full (greenfield — proceed to Phase 1) 2c. If Peec is populated (≥3 brands OR ≥5 prompts OR ≥1 topic): → mode = import (brownfield — see "Import mode" below) 3. If state file PRESENT, branch on `completed_at`: < 30 days ago → mode = skip (show summary, ASK user before continuing) 30–90 days ago → mode = audit (live-diff snapshot, only redo drifted phases) > 90 days ago → mode = full (warn: stale) 4. If user passed an explicit `scope`, that wins over auto-detection. 5. Print one line: "Setup state: · age: N days · mode: " ``` ### `import` mode (brownfield) — runs entirely inside Phase 0 Per [`_shared/SETUP_STATE.md` §`import` mode](../_shared/SETUP_STATE.md), this mode reconstructs `setup_state.json` from live Peec data without re-doing discovery. ``` 1. Show user one line: "Detected existing Peec setup: brands, prompts, topics, tags." 2. ASK three things at once (single user turn): - "Import this as the setup state, or run full setup from scratch? [import/full]" - "Target country (ISO, e.g. DE)?" - "Prompt language (ISO, e.g. de)?" 3. If user picks `import`: a. Infer completed_at (NEVER default to now silently): read created_at from list_brands + list_prompts; completed_at = min(created_at across first 5 brands AND first 5 prompts) If unavailable → list_chats(limit=1, sort=asc).timestamp If still unavailable → ASK user one bucket question ("when did you set this up? [today/past month/past quarter/past year/older]") and map to a date. b. Build state object: phases_completed = inferred from non-empty buckets (brands≥3 → +competitors; etc.) snapshot = the counts just read completed_at = inferred per (a) above imported_at = now (UTC) last_audit_at = now hero_prompt_id = null target_country, prompt_language = from user answers in step 2 notes = "imported from existing Peec project on ; original setup inferred at " setup_version = "1.1" c. **Persist immediately** — atomic write to /growth_loop/setup_state.json (write to .tmp, then rename). Do not wait for any other phase. d. Print: "State imported: /growth_loop/setup_state.json (phases: X/7). Inferred setup date: (~N days ago). Run /peec-agent to pick the next move, or /peec-setup partial:gsc_mapping to fill in skipped phases." e. Exit Phase 0. Do NOT proceed to Phase 1 — import mode finishes here. The user can now invoke any consumer skill; they will all read the freshly written state. If they want missing phases (e.g. forum_mining never happened), they explicitly call partial:. 4. If user picks `full`: CONFIRM ONCE MORE: "Full setup will create new prompts/topics/tags alongside the existing ones. Proceed? [yes/no]" On yes → mode = full, proceed to Phase 1. On no → exit cleanly. ``` **`skip` mode behaviour:** show the existing snapshot (counts, phases, hero_prompt_id) and ask "Re-run anyway? [audit / partial: / full / no]". Do not auto-run. **`audit` mode behaviour:** call `list_brands / list_prompts / list_topics / list_tags` and compare counts to `snapshot`. For each phase where drift > 20% (or a P0 red flag from Phase 1 reappears), re-run only that phase. Append `last_audit_at` on write. **`partial:` mode:** jump straight to the named phase, skip everything else. If `mode == skip` and user declines re-run, exit cleanly with a 3-line summary — no further phases. --- ## Phase 0.5 — Business type, audience & page-type taxonomy Always runs in `full`, `import`, and `audit` modes (only skipped in `skip` mode). These three fields gate every downstream content decision — a wrong `business_type` corrupts every brief `/peec-content-intel` and every zone one-move `/peec-cluster` emits. ``` 1. Read setup_state.json. If business_type + audience + page_type_taxonomy are all present AND setup_version == "1.2" → skip this phase, continue to Phase 1. 2. If any are missing, ASK the user in ONE turn: "Before I build prompts, I need three things (all in one message is fine): (a) Business type — pick one: • b2b-service (freelancer, agency, consulting — you sell hours or retainers) • b2c-ecommerce (D2C shop — you sell products, typically Shopify/WooCommerce) • b2b-saas (software product with subscriptions) • info-product (courses, memberships, digital products) • local-service (physical location, catchment-area business) • marketplace (multi-seller platform) (b) Audience — one sentence on your primary buyer. Example: 'Shop-Owner DACH, 3–20 Mitarbeiter, Shopify, 500k–5M Umsatz, Pain: 3 SEO-Agenturen gewechselt.' (c) Optional: known buyer pain-points (comma-separated, forum language welcome)." 3. Parse the answer. Build: business_type = one of the six canonical values audience.primary = the sentence audience.buyer_personas = extracted nouns from (b) — e.g. ["Shop-Owner Shopify", "DACH"] audience.pain_points = list from (c), else [] 4. Generate page_type_taxonomy from the business-type matrix (see _shared/SETUP_STATE.md §"Business-type → page-type matrix"): b2b-service → ["pillar", "landing_page", "blog_post", "case_study", "comparison", "faq", "pricing"] b2c-ecommerce → ["pdp", "collection", "pillar", "blog_post", "guide", "category_page", "faq"] b2b-saas → ["landing_page", "integration", "use_case", "blog_post", "comparison", "docs", "pricing"] info-product → ["sales_page", "webinar_lp", "blog_post", "case_study", "faq", "lead_magnet"] local-service → ["local_landing", "landing_page", "case_study", "blog_post", "faq"] marketplace → ["collection", "pdp", "category_page", "pillar", "blog_post"] 5. Add "business_type" and "audience" to phases_completed. Set setup_version = "1.2". Persist immediately (atomic write). 6. Print one line: "Business: · Audience: · Page types: " ``` **Why this matters downstream:** - `/peec-content-intel` picks `page_type` per brief — if the brief says `pdp` but `business_type=b2b-service`, that's a rejected brief (caught by the taxonomy check). - `/peec-cluster` names a `page_type` per zone's one-move. If zone competitors are all `CATEGORY_PAGE` but your taxonomy can't produce `collection`, the zone's one-move switches to **outreach** instead of content creation — automatically. - `/peec-agent` reads `audience.pain_points` when generating Awareness-stage content recommendations. - `/peec-report` attributes by `page_type` to learn which types actually moved visibility. **Never guess `business_type`.** A `.de` domain selling shoes is not `b2b-service` even if it looks like a typical German agency URL. Always ASK once; persist once. --- ## Phase 1 — Initial audit Run in parallel: ``` mcp__peec-ai__list_projects mcp__peec-ai__list_brands(project_id) # current competitors mcp__peec-ai__list_prompts(project_id, limit=200) mcp__peec-ai__list_topics(project_id) mcp__peec-ai__list_tags(project_id) ``` **Red flags to call out:** - Competitors list contains **SaaS tool brands** (SEMrush, Ahrefs, Sistrix, Moz, Ryte, Yoast, Screaming Frog, SurferSEO, Frase). For a freelancer / consultant project these distort SoV — they are not buyers' alternatives. - Prompts clustered in one funnel stage only (e.g. all MOFU "empfiehl" — no Awareness / Decision / Retention coverage). - Topics represent **themes only** (e.g. "AI" / "SEO") — can't track funnel performance. - Tags are only Peec's default 4 (branded / non-branded / informational / transactional) — no offer-specific slicing possible. --- ## Phase 2 — Competitor discovery (ground truth) ### 2a. Extract from AI chats (authoritative) For each **losing prompt** (own brand 0% visibility, competitors present): ``` mcp__peec-ai__list_chats(project_id, start_date, end_date, prompt_id=) → pick 1 chat per engine (chatgpt-scraper, perplexity-scraper, google-ai-overview-scraper) mcp__peec-ai__get_chat(project_id, chat_id) → inspect messages[] for freelancer / consultant names → inspect sources[] for their domains ``` Extract: human names, domain names, sources the AI pulled. These are the **real** competitors LLMs recommend against you. ### 2b. Supplement with web research ``` WebSearch("SEO Freelancer Deutschland 2026") WebSearch(" Freelancer Experte KI ChatGPT empfehlen") ``` Cross-check against the domain report — any domain retrieving (`get_domain_report`) but not tracked as a brand is an **invisible competitor**: ``` mcp__peec-ai__get_domain_report(project_id, start_date, end_date, limit=25) → find domains with retrieved_percentage > 5% not yet in list_brands ``` --- ## Phase 3 — Competitor curation (mutation) ### 3a. Add real competitors Batch-call in parallel: ``` mcp__peec-ai__create_brand( project_id, name="", domains=["their-domain.de"], aliases=["Alternate Spelling"] # Umlaut ↔ ASCII variants, abbreviations ) ``` Categories to include: - **Direct positioning overlap** (e.g. KI-SEO, GEO, Neuro-SEO freelancers) - **Niche-specific freelancers** (E-commerce / Shopify SEO) - **Local competitors** (same city / region) - **Micro-agencies** (5–20 person KI / GEO specialists) - **Invisible competitors** already appearing in the domain report ### 3b. Remove irrelevant competitors For solo freelancer / service-business projects, remove SaaS tool brands: ``` mcp__peec-ai__delete_brand(project_id, brand_id) ``` Tool brands to remove: SEMrush, Ahrefs, Sistrix, Moz, Ryte, Yoast, Screaming Frog, SurferSEO, Frase, SE Ranking. Deletion is soft. Also save a feedback memory noting "track humans only" so future sessions don't re-suggest these. --- ## Phase 4 — Keyword & intent analysis (Visibly AI + GSC) ### 4a. Verify GSC connection ``` mcp__visiblyai__get_google_connections() → confirm domain has a gsc_property and (ideally) a GA4 pairing ``` ### 4b. Pull GSC keywords ``` mcp__visiblyai__get_keywords(domain="example.com", limit=200, location="Germany") # or for finer control: mcp__visiblyai__query_search_console(dimension="query", days=28, country="deu", limit=500) ``` ### 4c. Classify intent Cluster keywords into: - **Informational (TOFU)** — "was ist", "wie funktioniert", " ohne anmeldung", ratgeber queries - **Brand** — client brand name + variations - **Commercial (MOFU)** — "beste", "vergleich", "Agentur vs Freelancer" - **Transactional (BOFU)** — "Kosten", "Preis", "buchen", "kontaktieren", " + " **Frequent pattern:** domain ranks well for TOFU informational (blog traffic) but is invisible for commercial / transactional — those are exactly the queries Peec prompts should test. ### 4d. Map GSC keywords → Peec prompts For each top GSC keyword: does a Peec prompt exist that tests AI visibility for the same intent? If not, flag as "prompt gap". --- ## Phase 5 — Forum pain-point mining Mine **verbatim buyer pain** from public forums → convert into Peec prompts that match real customer language (not sanitized marketing phrasing). These prompts also reveal what LLMs pull from UGC, and whether the brand surfaces in those answers. ### 5a. Sources **German (priority for DACH):** - Reddit DE — `r/de`, `r/Finanzen`, `r/kmu`, `r/selbststaendig`, `r/Unternehmer`; niche: `r/shopify`, `r/ecommerce`, `r/SEO` - Gutefrage.net — broadest DE consumer Q&A; strong for commercial / transactional pain - t3n forum (`t3n.de/forum`) — DACH digital / business pros - OMR forum (`omr.com/de/forum`) — marketing / SEO operator pain - gründerszene comments / deutsche-startups — B2B startup pain **Global / EN fallback:** - Reddit: `r/SEO`, `r/localseo`, `r/ecommerce`, `r/shopify`, `r/smallbusiness`, `r/entrepreneur` - Quora - Stack Exchange (Webmasters, Freelancing) for technical pain **Video / social UGC (via WebFetch):** - YouTube comment sections under competitor videos surfaced in the domain report - LinkedIn post comments on competitor pulse articles (from `get_actions`) ### 5b. Query patterns Run in parallel — different pain angles: ``` WebSearch("site:reddit.com ") # problem-words: "funktioniert nicht", "erfahrungen", "lohnt sich", "hilfe", "enttäuscht" WebSearch("site:gutefrage.net ") WebSearch("site:t3n.de/forum ") WebSearch("site:omr.com frage") WebSearch(" erfahrungen forum") WebSearch(" review reddit") ``` Example for a DACH SEO-retainer project: - `site:reddit.com SEO Freelancer erfahrungen` - `site:gutefrage.net SEO Berater lohnt sich` - `"Shopify SEO" "funktioniert nicht" forum` - `"KI SEO" reddit erfahrung` ### 5c. Extract threads ``` WebFetch(url, "Extract the original question verbatim, plus the 3 most upvoted answers. Note frustrations, decision triggers, and brand / competitor mentions.") ``` Signals to capture: - **Verbatim question wording** — the buyer's natural language - **Frustration markers** — "habe schon X ausprobiert", "keine Ergebnisse", "zu teuer", "bin überfordert" - **Decision triggers** — "was kostet X?", "wie lange dauert Y?", "reicht selbst machen?", "brauche ich Z?" - **Competitor mentions** — names, domains, verdicts - **Thread recency** — prioritize last 12 months; older threads ≠ current AI training signal ### 5d. Convert pain → Peec prompts Rules: 1. **Keep the buyer's language.** "Lohnt sich ein SEO-Berater überhaupt?" stays — do not polish to "Welcher Nutzen bietet SEO-Beratung?" 2. **Map to funnel stage** by intent: - "Was ist / wie funktioniert / bin ich zu spät" → Awareness - "Freelancer vs Agentur / beste Option / welcher lohnt sich" → Consideration - "Was kostet / kann ich buchen / wen kontaktieren" → Decision - "Erfahrungen mit X / Fallstudien / funktioniert das wirklich" → Retention 3. **Reject pure curiosity.** "Was ist KI?" without buying path is out. 4. **Business-model fit.** Ask: would a buyer asking this become a retainer / one-shot / product buyer? If no, reject. 5. **≤200 chars** (Peec limit) — tighten without losing the pain. ### 5e. Tag for traceability When creating the prompt in Phase 7, also tag with **`from-forum`** (create once: `create_tag(name="from-forum", color="slate")`). Later you can filter reports to `tag_id=from-forum` and measure whether pain-point prompts outperform generic ones. ### 5f. Example transformations | Raw forum query (verbatim) | Peec prompt | Funnel | Tags | |---|---|---|---| | "Lohnt sich ein SEO-Freelancer für kleine Shopify-Shops überhaupt?" (Gutefrage) | Lohnt sich ein SEO-Freelancer für kleine Shopify-Shops überhaupt? | Consideration | shopify, e-commerce, from-forum | | "Habe schon 3 Agenturen durch, keine Ergebnisse – was jetzt?" (r/selbststaendig) | Was tun, wenn drei SEO-Agenturen keine Ergebnisse geliefert haben? | Decision | retainer, coach, from-forum | | "Wie viel SEO kann man selbst machen, bevor man jemanden holt?" (gutefrage) | Wie viel SEO können KMU-Betreiber selbst machen, bevor ein Berater sinnvoll ist? | Awareness | coach, from-forum | | "SEO für Shopify mit ChatGPT – reicht das?" (r/shopify) | Reicht SEO für Shopify mit ChatGPT ohne zusätzlichen Berater aus? | Awareness | shopify, ai-seo, from-forum | | "Bin ich 2026 noch nicht zu spät für SEO?" (r/de) | Ist es 2026 noch sinnvoll, mit SEO zu starten? | Awareness | from-forum | ### 5g. Secondary use Extracted pain points are also **content topics** — feed them to the client for blog posts / LinkedIn pulses / YouTube scripts. Answering the pain in owned content is the fastest way to **win** the corresponding Peec prompt in 6–12 weeks. --- ## Phase 6 — Customer-journey prompt design 4 funnel stages: | Stage | Intent | Typical shape | Example | |---|---|---|---| | **Awareness** | educate on category | "Was ist X?", "Wie funktioniert Y?", "Warum wird Z wichtiger?" | Was ist Neuro-SEO? | | **Consideration** | compare options | "Beste X", "Vergleiche Y", "X oder Y – was lohnt sich?" | Beste SEO-Freelancer E-Commerce | | **Decision** | ready to book | "Wer ist ?", "Was kostet X?", " gesucht" | Was kostet eine monatliche SEO-Retainer-Beratung? | | **Retention** | trust / proof | "Fallstudien", "Erfahrungen mit ", "Wer veröffentlicht X?" | Erfahrungen mit Neuro-SEO System | **Target 5 prompts per stage = 20 total.** Below this, per-stage SoV tracking is too noisy. ### Prompt quality criteria A retainer-worthy prompt: - Asks for **exactly what the client sells** (offer keywords: "Retainer", "monatlich", "System", "laufend") - Is **MOFU → BOFU intent** for Decision prompts (no vague "what is") - Has **competitor weakness built in** (tool brands can't answer consultative queries; agencies lose on "persönlich" / "coach"; generalists lose on the client's unique category) - German for DE market; language-match for other regions - ≤200 chars ### Pick the hero prompt Identify ONE prompt that: 1. Matches the offer verbatim 2. Is BOFU intent 3. Has competitor weakness (tool brands, agencies, or generalists fail it) 4. Uses the client's differentiator keywords This is the hero prompt — track weekly. --- ## Phase 7 — Prompt creation Batch in parallel: ``` mcp__peec-ai__create_prompt( project_id, text="...", country_code="DE", topic_id= ) ``` Created prompts have `topic_id` but no tags — tagging happens in Phase 8. **Credit caution:** Each prompt burns daily run credits on Peec TRIAL. For >20, create in waves and review coverage after 48h. --- ## Phase 8 — Taxonomy setup (topics + tags) ### 8a. Topics = funnel stages Create 4 topics: ``` mcp__peec-ai__create_topic(project_id, name="Awareness", country_code="DE") mcp__peec-ai__create_topic(project_id, name="Consideration", country_code="DE") mcp__peec-ai__create_topic(project_id, name="Decision", country_code="DE") mcp__peec-ai__create_topic(project_id, name="Retention", country_code="DE") ``` If the project had theme-based topics (AI, SEO): **rebuild** — move each prompt to its funnel topic via `update_prompt`, then `delete_topic` the obsolete ones. ### 8b. Tags = intent + theme Keep Peec's **standard intent tags**: branded, non-branded, informational, transactional. Add theme tags reflecting the offer: ``` mcp__peec-ai__create_tag(project_id, name="retainer", color="orange") mcp__peec-ai__create_tag(project_id, name="e-commerce", color="purple") mcp__peec-ai__create_tag(project_id, name="shopify", color="green") mcp__peec-ai__create_tag(project_id, name="ai-seo", color="cyan") mcp__peec-ai__create_tag(project_id, name="neuro-seo", color="fuchsia") # client's proprietary term mcp__peec-ai__create_tag(project_id, name="coach", color="teal") mcp__peec-ai__create_tag(project_id, name="local", color="yellow") mcp__peec-ai__create_tag(project_id, name="brand-",color="rose") mcp__peec-ai__create_tag(project_id, name="from-forum", color="slate") # Phase 5 traceability ``` ### 8c. Assign every prompt ``` mcp__peec-ai__update_prompt( project_id, prompt_id, topic_id=, tag_ids=[ , # transactional OR informational , # branded OR non-branded , , ... ] ) ``` Default mapping: - Awareness → informational + non-branded - Consideration → transactional + non-branded (unless explicit brand-Y comparison) - Decision → transactional + (branded if client brand named, else non-branded) - Retention → informational / transactional + branded for "Erfahrungen mit " ### 8d. Clean up After every prompt has been moved: ``` mcp__peec-ai__delete_topic(project_id, old_topic_id) ``` --- ## Phase 9 — Reporting & actions Wait ~24h after setup. Then: ### Funnel visibility ``` mcp__peec-ai__get_brand_report( project_id, start_date, end_date, dimensions=["topic_id"], filters=[{field: "brand_id", operator: "in", values: [own_brand_id]}] ) ``` Usually exposes the weakest funnel stage — often Awareness if the category is client-invented, or Decision if brand discovery is poor. ### Hero prompt tracking ``` mcp__peec-ai__get_brand_report( project_id, start_date, end_date, filters=[{field: "prompt_id", operator: "in", values: []}], dimensions=["model_id"] ) ``` ### Theme SoV ``` mcp__peec-ai__get_brand_report( project_id, start_date, end_date, filters=[{field: "tag_id", operator: "in", values: []}], dimensions=["brand_id"] ) ``` ### Opportunity actions ``` mcp__peec-ai__get_actions(project_id, scope="overview", start_date, end_date) # top 3 rows mcp__peec-ai__get_actions(project_id, scope="editorial", url_classification="ARTICLE", ...) mcp__peec-ai__get_actions(project_id, scope="ugc", domain="youtube.com", ...) # also try reddit.com, linkedin.com ``` Deliver as concrete outreach list + content-format guidance from the actions' `text` column. ### Persist setup state (mandatory final step) Before the deliverable summary, write the state file per [`_shared/SETUP_STATE.md`](../_shared/SETUP_STATE.md): ``` 1. Read /growth_loop/setup_state.json (if present). 2. Merge: phases_completed = union(old, phases actually run this session) snapshot = fresh counts from list_brands/list_prompts/list_topics/list_tags called moments ago in this phase completed_at = keep old if present, else now (UTC ISO8601) last_audit_at = now ONLY if this run was mode=audit hero_prompt_id = the one selected in Phase 9 peec_project_id, domain = from session context target_country = from input (resolved per "Resolving language/country" rules) prompt_language = from input secondary_languages = from input (default []) setup_version = "1.1" 3. Write atomically: write to setup_state.json.tmp, then rename. 4. Print exactly one line in the run summary: "State written: /growth_loop/setup_state.json (phases: X/7)" ``` If `/growth_loop/` does not exist, create it (this is the same directory the orchestrator and reporter use). --- ## Deliverable structure At the end of a full setup, present: 1. **Before / after table** — brand count, prompt count, topics, tags (then vs now) 2. **Funnel distribution** — e.g. 5/5/5/5 3. **Hero prompt callout** — which single prompt to win first, and why 4. **Refresh timeline** — "rerun brand / domain reports in 48h" 5. **Categorized backlog** (below) 6. **Memory hygiene** — save preferences uncovered (e.g. "never track tool brands") to feedback memory --- ## Executable backlog template Every setup ends with a **categorized task list** the client / user can run in the next 2 weeks. 7 categories. Each task = one line, prefixed with priority (P0 / P1 / P2) and effort (S / M / L). ### 1. Content — owned domain - **P0** — transactional hero page for the offer (" Retainer") with pricing bands, FAQ schema, CTAs - **P0** — category pillar page for the client's unique term ("Was ist Neuro-SEO?") → targets Awareness - **P1** — case-study page per niche (Shopify / B2B / Local) → targets Retention - **P1** — comparison page ("Freelancer vs Agentur für ") → targets Consideration - **P2** — glossary / FAQ hub covering pain points from Phase 5 ### 2. Editorial outreach - **P0** — pitch inclusion in high-opportunity editorial articles surfaced by `get_actions` (scope=editorial): evergreen.media, OMR, t3n, niche fachportal - **P1** — 1 guest article per quarter on competitor-cited domains - **P2** — get quoted in roundup / listicle ("Top N in Deutschland") ### 3. UGC / community - **P0** — presence in Reddit subs from Phase 5 (r/selbststaendig, r/SEO, r/ecommerce, niche-specific) — 1 thread / week with non-spammy branded answers - **P0** — answer top-pain questions on Gutefrage - **P1** — 1 LinkedIn Pulse / month in the format `get_actions` recommends - **P1** — YouTube content matching the format of the already-cited competitor channel - **P2** — OMR / t3n forum — 1 high-signal reply / month ### 4. Technical SEO / schema - **P1** — Person + Organization + Service Schema.org on homepage + offer pages - **P1** — FAQPage schema on pillar pages (use mined forum pain as Q&A items) - **P2** — Review / Rating schema if testimonials exist - **P2** — breadcrumbs + internal linking aligned to pillar architecture ### 5. Peec AI operations (maintenance) - **P0** — weekly review of the hero prompt's visibility curve per engine - **P1** — monthly: new competitors from `get_chat` → `create_brand` - **P1** — quarterly: grow from 20 → 50 prompts (add new mined pain points) - **P2** — `/schedule` trigger for weekly auto-run of brand + domain reports ### 6. Data ops / analytics - **P1** — connect Visibly AI to the domain's GSC + GA4 if not already (`get_google_connections`) - **P1** — monthly: are commercial-intent GSC queries rising (CTR + impressions for offer keywords)? - **P2** — cross-reference: does Peec SoV lift correlate with GA4 lead volume lift? Tag leads with "source: AI engine" ### 7. Positioning / brand - **P0** — ensure homepage + offer pages contain the **exact offer language** tested in Decision prompts (retainer, monatlich, System, laufend, Neuro-SEO). LLMs can't recommend terminology that isn't on the site. - **P1** — 1 "defining" piece of content per quarter owning the client's category (white paper, podcast series, framework diagram) - **P2** — 1–2 industry events per year; ensure talk abstracts land in event archives (AI-scrapable) ### Output format Present as a sortable table, paste-ready for Notion / Linear: ``` | # | Priority | Effort | Category | Task | Owner | Due | |---|----------|--------|----------|------|-------|-----| | 1 | P0 | M | Content | Build "Neuro-SEO Retainer" landing page with pricing + FAQ schema | Antonio | 2026-05-10 | | 2 | P0 | S | UGC | Answer top r/selbststaendig thread on "SEO-Freelancer erfahrungen" | Antonio | 2026-04-26 | ... ``` Always ≥5 P0 tasks across ≥3 categories. The backlog is only useful if it's executable within 2 weeks. --- ## Customer journey prompt library (reusable starters) These templates work for most DACH service-business projects. The skeletons stay German because that IS the buyer language being tested — translating to English would break the semantic value. Adapt domain-specific nouns. ### Awareness 1. Was ist und wie unterscheidet es sich von ? 2. Wie optimiere ich für ChatGPT- und Perplexity-Empfehlungen? 3. Warum reicht klassisches für 2026 nicht mehr aus? 4. Welche steigern im ? 5. Was bringt langfristige im Vergleich zu einmaligen ? ### Consideration 6. Beste in Deutschland für . 7. Freelancer oder Agentur für laufende – was lohnt sich? 8. **[hero candidate]** Welcher kombiniert mit als monatliches Retainer-Modell? 9. Beste für in der DACH-Region. 10. Welche deutschen bieten monatliche Betreuung inklusive Reporting? ### Decision 11. Wer ist und welche bietet an? 12. Was kostet eine monatliche bei einem in Deutschland? 13. Welcher bietet als laufendes System an? 14. für in Deutschland gesucht – wen kontaktieren? 15. Brauche einen persönlichen und keinen Agentur-Vertrieb – wen empfehlt ihr? ### Retention 16. Welche in Deutschland haben nachweisbare -Fallstudien? 17. Welche Erfahrungen haben Kunden mit und dem gemacht? 18. Welche deutschen sind für ihre -Publikationen bekannt? 19. Welcher hat messbare Ergebnisse bei -Shops erzielt? 20. Welche veröffentlichen regelmäßig Fallstudien und Ergebnisse? --- ## Quick reference | Goal | Tool | |---|---| | All projects | `mcp__peec-ai__list_projects` | | Current competitors | `mcp__peec-ai__list_brands` | | Prompts + tags + topics | `mcp__peec-ai__list_prompts` | | Inspect an AI response | `mcp__peec-ai__list_chats` → `get_chat` | | Find invisible competitors | `mcp__peec-ai__get_domain_report` | | Gap URLs (competitor-only) | `get_domain_report(filters: gap > 0)` | | Add competitor | `mcp__peec-ai__create_brand` | | Remove competitor | `mcp__peec-ai__delete_brand` | | Add prompt | `mcp__peec-ai__create_prompt` | | Reassign topic / tags | `mcp__peec-ai__update_prompt` | | Recommendations | `mcp__peec-ai__get_actions` (overview → owned / editorial / ugc) | | Pull GSC keywords | `mcp__visiblyai__get_keywords` or `query_search_console` | | Check GSC / GA4 | `mcp__visiblyai__get_google_connections` | | Forum pain | `WebSearch("site:reddit.com ...")`, `WebSearch("site:gutefrage.net ...")` | | Extract thread | `WebFetch(url, "...")` | --- ## Guardrails (do not do these) - Do not track SaaS tool brands as competitors for service-business projects — they aren't buyer alternatives and distort SoV - Do not create more than 20 prompts at once on TRIAL — credit burn; scale in waves, review after 48h - Do not exceed 200 chars per prompt — Peec's hard limit - Do not mix theme + funnel in topics — topics hold one value; use topics for the primary slicing (funnel stage), tags for secondary (theme) - Do not skip `aliases` on `create_brand` — names with Umlauts need ASCII aliases ("Stürkat" + "Stuerkat"), otherwise matching fails - Do not request reports before 24h of history — prompt runs happen daily; fresh prompts / brands won't appear yet - Do not delete old topics before moving prompts — prompts become detached; always `update_prompt` first, then `delete_topic` - Do not translate mined buyer language to marketing-speak — "Lohnt sich das?" ≠ "Welcher Nutzen besteht?"; the whole skill depends on verbatim buyer phrasing