/01-intake

description: Build the GEO brand context for a client by ingesting provided materials (PDF/DOCX/PPTX/XLSX, URLs, raw notes) and conducting structured AI-perception + competitor + community research. Writes brand_context.json validated against schemas/brand_context.schema.json. Use when starting a new client (clients/<slug>/inputs/ has materials but no brand_context.json), or when brand_context.json is marked status=stale. Downstream consumers: 02-audit, 03-gap, 04-content-brief, 05-production.
argument-hint: "<client-folder, e.g. clients/acme>"

01 · Intake — Build the Brand Context

The single source of truth for the entire pipeline. Every downstream skill reads `brand_context.json`. If this is wrong or thin, every later skill amplifies the error.

**Core principle**: AI recommends whoever best answers the question. The richer and more precise this profile, the more accurately the rest of the pipeline can identify the exact questions this business should own.

---

Inputs

• `clients/<slug>/inputs/` — materials provided by the client (any of: PDF,

DOCX, PPTX, XLSX, image, plaintext notes, URL list).

• The user may also provide context conversationally during this command run.

Output

• `clients/<slug>/brand_context.json` — validates against

`schemas/brand_context.schema.json` (3 layers + extended).

• `clients/<slug>/intake-log.md` — append-only human-readable log of what

was extracted from which source.

---

Procedure

Run sequentially. Do not signal readiness to 02-audit until every gate passes.

Step 1 — Ingest materials

For each file in `clients/<slug>/inputs/`:

| Input type | Approach | |------------|----------| | `*.pdf` | Read with the `Read` tool (built-in PDF support up to 10 pages; for larger PDFs, pass `pages` ranges) | | `*.docx` | `unzip -p file.docx word/document.xml \| sed 's/<[^>]*>/ /g'` then `tr -s ' ' '\n'` | | `*.pptx` | Same as docx but `ppt/slides/slide*.xml` | | `*.xlsx` / `*.csv` | `python3 -c "import pandas; print(pandas.read_excel(...).to_csv())"` | | URL list | `WebFetch` each URL with extraction prompt | | `*.png` / `*.jpg` | `Read` (multimodal) — extract text and structural info | | Notes | Read directly |

**Do not dump raw content into brand_context.json.** Apply the extraction filter: keep only signals that map to a schema field. Discard the rest.

Step 2 — Layer 1 (Business Identity) — 5 hard-required fields

Fill these from materials. If any is missing or thin (single word, "TBD", generic-quality phrase), ASK the user a targeted question. Don't continue with placeholder values.

1. `company.name` + URL + one-line description 2. `product_or_service.category` + description + core_offerings 3. `target_customer.primary_icp` — Forrester-style: persona_label, demographics (role/segment, geography, size/age), jobs_to_be_done (≥1), pain_points, search_behavior 4. `competitors[]` — 1–3 entries. **Verify each via `WebFetch` on their public site** before recording. Don't trust user-named competitors blindly. 5. `core_differentiator` (concrete, ≥10 chars, not "we provide quality") 6. `competitive_moat` (one-sentence why-AI-should-recommend-us)

Step 3 — Layer 2 (Market Reality) — three-dimensional scan

Do all three independently. Don't ask the user; the user doesn't have this data.

**3a. AI Perception Scan**

• Pick 5–8 representative queries from the category (definition, top-X,

comparison, how-to, status). Use `target_queries` if present, else generate from category + ICP.

• Run a Claude-native scout: spawn a sub-agent with NO client context

and ask it each query. Same mechanism as 02-audit Step 2 (see `skills/02-audit/SKILL.md`). For multi-engine coverage you may optionally invoke `seo-geo-optimizer` (vendored).

• Record into `layer_2_market_reality.ai_recommends[]`: model, query,

recommended brands, whether us mentioned, position, captured_at.

• For every URL the AI cites, `WebFetch` it. Verify it's real, recent,

on-topic. Record into `citation_sources[]`.

**3b. Competitor Reality**

• For each competitor named in Layer 1, `WebFetch` their homepage + key

pages. Record core_approach, strengths, weaknesses, ai_visibility_notes, key_differentiator_vs_us, verified_at.

• **Cross-check**: if the AI in 3a recommends competitors the user didn't

name, surface this: "You named X, Y, but AI recommends A, B for this category. Track both sets or focus on one?" Then update Layer 1 competitors accordingly.

**3c. Community Voice**

• `WebSearch`: `"<category> reddit recommendation"`, `"<category> vs

<competitor> forum"`, `"<category> review hacker news"`.

• `WebFetch` top 2–3 threads. Read actual user language.
• Record into `real_user_questions[]` with source URL and intent

classification.

Step 4 — Layer 3 (Value Gaps) — synthesize opportunities

From Layer 1 + Layer 2, identify question types where this business CAN win but currently isn't. At least one entry. Each entry must include `opportunity` + `rationale` + supporting_evidence (cite specific Layer 2 findings).

Step 5 — Target queries

Build `target_queries[]`. Sources:

• Queries from 3a that the brand currently loses on but matches Layer 3.
• Queries surfaced in 3c real_user_questions.
• Long-tail derived from `product_or_service.category` × ICP `jobs_to_be_done`.

Each entry needs query, intent, priority (P0/P1/P2), rationale, expected_answer_shape.

Minimum 10 entries. Aim for 25–40 to give 02-audit a robust baseline.

Step 6 — Voice samples + content assets

• Pick 2–3 canonical voice samples from materials (homepage hero, top blog

post, founder note). Record source + excerpt + voice_attributes.

• Walk the client site (or list of provided URLs). Catalog every existing

asset into `content_assets[]` with type + freshness.

Step 7 — Confirm with user before writing

Before persisting brand_context.json, present to user:

• One-sentence competitive_moat
• AI's current view of the brand (from 3a)
• Information