Peec content intel

Peec Content Intel

Role

For one Peec prompt that the brand is losing, produce one publish-ready content brief: sub-queries, verbatim buyer pains, competitor breakdown, outline, focus keywords, and outreach targets.

Input

• project_id — Peec project (read from setup_state.json per pre-flight; do not re-resolve)
• prompt_id — the target prompt (must have ≥24h of data)
• optional date_range — default last 28 days
• language — read from setup_state.json (prompt_language); user can override per-run, but never default to en silently
• target_country — read from setup_state.json; drives forum source picks (DE→reddit/r/de+gutefrage+t3n, AT→reddit+derstandard, US→reddit+quora, CH→reddit/r/de+r/fr) and SERP/GSC market filters
• page_type — required. Must be one of the values in setup_state.page_type_taxonomy. If the user doesn't pass it, the skill infers the best fit from the funnel stage of the target prompt + competitor top-url classifications, then ASKS the user to confirm ("Suggested page_type: landing_page (Decision-stage, competitors use PRODUCT_PAGE). OK? [y/n/override]"). Never guess silently — a brief with the wrong page_type is a wasted publish cycle.
• business_type — read from setup_state.json. Used to validate page_type selection against the allowed taxonomy matrix.
• audience — read from setup_state.json. audience.primary and audience.pain_points feed the brief's "Why this page wins" + "Voice / tonality" sections directly; do not restate them in the brief prose, USE them.

Output

One markdown brief per prompt, saved at briefs/<YYYY-MM-DD>_<prompt-slug>/brief.md, plus the raw data next to it (competitor-urls.json, forum-pains.json, scoring.json). No dashboards.

Every brief starts with a front-matter block that downstream skills (peec-report, peec-learn) consume for attribution:

---
brief_id:      <YYYY-MM-DD>_<prompt-slug>
prompt_id:     pr_xxxxxx
business_type: b2b-service
page_type:     landing_page           # MUST be in setup_state.page_type_taxonomy
target_url:    /seo-retainer          # planned publish path
funnel_stage:  Decision                # from the prompt's topic
audience:
  primary:     "Shop-Owner DACH, 3-20 MA, Shopify"
  pain_hook:   "3 Agenturen gewechselt, keine Ergebnisse"
success_metric_4w:
  prompt_visibility: "0% → ≥15%"
  zone_visibility:   "4% → ≥20%"
---

A brief without this front-matter block is invalid and will be rejected by peec-report.

When to use

• "Which content wins Peec prompt X?"
• "Analyze the sources competitors get cited for on prompt X"
• "What's the content gap between me and competitor.de?"
• Runs after peec-setup — the project must exist with structured prompts.

Do not use when:

• Prompt has <24h of Peec data (no chats / sources yet)
• No own-brand absence on the prompt (nothing to close)

---

Pipeline

0. Pre-flight — setup state required

Per _shared/SETUP_STATE.md, this skill refuses to run without a completed setup:

Read <project>/growth_loop/setup_state.json
If missing OR completed_at missing OR phases_completed lacks
   {competitors, prompts, topics, tags}:
     STOP. Output:
       "No Peec setup state found at <project>/growth_loop/setup_state.json.
        Run /peec-setup first."
If completed_at older than 90 days: WARN once, continue.
Use peec_project_id from state — don't re-resolve via list_projects.

1. Find the gap URLs

mcp__peec-ai__get_brand_report(
  project_id, start_date, end_date,
  dimensions=["prompt_id"],
  filters=[{field: "prompt_id", operator: "in", values: [prompt_id]}]
)
mcp__peec-ai__get_url_report(
  project_id, start_date, end_date,
  dimensions=["prompt_id"],
  filters=[
    {field: "prompt_id", operator: "in", values: [prompt_id]},
    {field: "gap", operator: "gt", value: 0}
  ],
  limit=25
)

Output: up to 25 URLs, sorted by retrieval frequency, each with a classification (LISTICLE / ARTICLE / COMPARISON / HOW_TO_GUIDE / PROFILE / DISCUSSION).

Interpretation:

• LISTICLE / COMPARISON → outreach targets (get included)
• HOW_TO_GUIDE / ARTICLE → own-content targets (write and publish)
• PROFILE / DISCUSSION (reddit, youtube) → community targets (participate)

2. Query Fan-Out

Primary path — mcp__visiblyai__query_fanout (Visibly MCP ≥ v0.6.0, ~3–5 credits):

mcp__visiblyai__query_fanout(
  url="https://<own-or-competitor-domain>/<page>",
  keyword="<focus_keyword>",
  data_source="dataforseo",   # or "gsc" / "both"
  gsc_property=null,          # required if data_source includes gsc
  language="de"               # or "en"
)

Returns: fanout_queries[], coverage_score (0–1), covered_count / total_count, gaps[] (sub-queries not addressed on the URL — this is the content backlog), coverage_details[].

Fire once per top gap URL from Phase 1. Replaces sub-query generation + crawling + semantic match in one call.

Fallback — inline heuristic (if Visibly MCP unavailable or credits tight):

Generate 6 sub-queries along fixed intent axes:

1. Synonym (same intent, different wording)
2. Decision ("what does it cost", "when to switch")
3. Comparison ("X vs Y")
4. Problem ("why doesn't X work")
5. Long-tail (narrow niche)
6. Forum / community (informal phrasing)

Coverage matching is then skipped — flag that explicitly in the brief.

3. Mine forum pain per sub-query

Reddit (tested 2026-04-19): WebFetch against reddit.com is blocked. The workaround that works: Peec has already scraped Reddit threads, so use

mcp__peec-ai__get_url_content(project_id, url="https://reddit.com/r/<sub>/comments/<id>/...")

Procedure:

1. From Phase 1, list all classification=DISCUSSION + domain=reddit.com URLs
2. For the top 3 by retrieval, call get_url_content
3. Extract: verbatim pain quotes, tool mentions, competitor mentions, sentiment

Gutefrage / t3n / OMR:

WebSearch("site:gutefrage.net <query>")
WebSearch("site:t3n.de/forum <query>")
WebSearch("site:omr.com <query>")
WebFetch(url, "Extract the original question verbatim, plus top 3 answers. Note frustrations, decision triggers, competitor/brand mentions.")

Gutefrage blocks WebFetch with 403. Fallback: Google search snippets + archive.org.

Per sub-query, aggregate: 3–5 verbatim pain quotes, competitor mentions with sentiment, top 2–3 thread URLs for later engagement.

4. Score competitor URLs (Visibly deep-dive)

Per top gap URL from Phase 1:

mcp__visiblyai__get_backlinks(domain="<competitor-domain>", limit=10, location="Germany")
mcp__visiblyai__onpage_analysis(url="<competitor-url>", keyword="<focus-keyword>")   # 15 credits, top 3 only
mcp__peec-ai__get_url_content(project_id, url="<competitor-url>")                    # for outline mining
mcp__visiblyai__get_keywords(domain="<competitor-domain>", limit=200, location="Germany")

Backlinks caution: get_backlinks can return 260 KB+ (2,836 backlinks for noahlutz.de). Always pass limit: 10-20 or delegate to a subagent. For headline metrics (total_count, rank, domain_from_rank), limit=1 is enough.

DR interpretation:

• DR <20 → organic authority, realistic to overtake
• DR 20–50 → established, 6–12 months of content + targeted links
• DR >50 → hard to beat head-on; flank with long-tail instead

5. Opportunity scoring

score = (retrieval_freq × gap_size × forum_pain_density)
        / (domain_DR × (1 + content_quality_diff))

Tiers:

• Tier 1 (score >50) — attack now: write + outreach
• Tier 2 (20–50) — 3–6 month horizon
• Tier 3 (<20) — park or flank with long-tail

6. Render brief

Save to briefs/<YYYY-MM-DD>_<prompt-slug>/brief.md. Schema below.

---

Brief schema

## Content Brief: <prompt text>

### Goal
Win Peec prompt: "<prompt_text>"
Funnel stage: Awareness | Consideration | Decision | Retention
Current own visibility: X%  →  Target: Y% in 90 days

### Buyer language (forum pain, verbatim)
- "<verbatim quote>"  (source: r/selbststaendig, 2026-03)
- "<verbatim quote>"  (source: Gutefrage)
- "<decision trigger>"

### Sub-queries (Query Fan-Out)
1. <sub-query>
2. <sub-query>
...

### Competitor landscape
| URL | Class | Retrieval | DR | Opp score | Note |
|---|---|---|---|---|---|
| evergreen.media/ki-seo | ARTICLE | 22% | 47 | 35 | high DR, pitch as contributor |
| noahlutz.de/ki-seo | LISTICLE | 9% | 22 | 68 | direct attack, similar DR |

### Recommended format
<LISTICLE | COMPARISON | HOW-TO | DEFINITION> — based on dominant URL class

### Title (draft, ≤60 chars)
<title>

### Meta description (≤155 chars)
<description using buyer language>

### Outline (H2 / H3)
1. <opening pain>
2. <definition / framework>
3. <decision matrix or checklist>
4. <practice examples>
5. <pitfalls>
6. <CTA>

### Focus + secondary keywords
- Focus: <keyword>  (volume, intent)
- Secondary: <kw2>, <kw3>, <kw4>   (from keyword gap)

### Backlink strategy
- <domain 1>: editorial pitch (template)
- <subreddit>: join thread (URL)
- <editorial>: contribute to existing article

### KPI
- Prompt visibility after 90 days: Y%
- GSC position for focus keyword: top 10
- ≥3 backlinks from DR >30

---

Quick reference

Step	Tool
Gap URLs	mcp__peec-ai__get_url_report(filters: gap>0)
AI response + sources	mcp__peec-ai__list_chats → get_chat
Scraped competitor content	mcp__peec-ai__get_url_content
Query Fan-Out + coverage	mcp__visiblyai__query_fanout (≥v0.6.0)
Backlink profile	mcp__visiblyai__get_backlinks
24-point onpage audit	mcp__visiblyai__onpage_analysis (15 cr)
Competitor keywords	mcp__visiblyai__get_keywords
Intent classification	mcp__visiblyai__classify_keywords
Full-site crawl	mcp__visiblyai__crawl_website (15–60 cr)

Credit budget (Visibly)

Per prompt analysis: ~45–75 credits.

• 1× get_backlinks per top-3 competitor domain (cheap, often ~0)
• 3× onpage_analysis → 45
• 1× get_keywords per top-2 competitor (cheap)

Batch of 10 prompts: ~500–750 credits.

---

Guardrails (do not do these)

• Do not analyze prompts with <24h of Peec history — phase 1 returns empty
• Do not WebFetch Gutefrage (403) — use WebSearch snippets instead
• Do not run onpage_analysis on more than the top-3 URLs — credit drain
• Do not mix funnel stages in the fan-out — a MOFU parent must not pull in TOFU sub-queries, or the brief dilutes
• Do not judge a competitor by DR alone — weak content at high DR is still attackable; read the actual content via get_url_content
• Do not run this skill before peec-setup — depends on structured prompts/topics/tags