Content Brief

Content Brief — Keyword-to-Notion Brief Generator

Generates a structured SEO content brief from a target keyword. Output follows the mandatory Direction prompt format so the text_parser.py script can extract every field for Notion without manual cleanup.

Invocation Triggers

Explicit phrases (any of):

• "create a content brief for [keyword]"
• "brief this keyword: [keyword]"
• "content brief on [topic]"
• "generate a brief for [keyword]"
• "write a brief for [keyword]"
• "run the content brief skill"

Implicit signals:

• User provides a keyword and asks for an SEO spec, editorial spec, or writing assignment
• User pastes a keyword list and asks for briefs

When triggered, run immediately — no upfront intake beyond the keyword itself.

Grill-Me Intake (One Question, Optional)

Run the brief without questions when the keyword is clear.

Ask one clarifying question only when both are true:

1. The target audience or client is not inferrable from context
2. Audience changes the content angle meaningfully (e.g., "project management software" could target PMs or developers)

Quick clarification — who is the primary audience for "[keyword]"?

1. [Inferred persona A — e.g., HR Director]
2. [Inferred persona B — e.g., Operations Manager]
3. Tell me

Why I'm asking: audience shapes the H1, content angle, and writer notes. One question prevents a wrong brief.

Max one question. If audience is inferable, skip and proceed.

Data Gathering (Before Writing the Brief)

Run these steps before generating the Direction prompt output:

Step 1 — Keyword Metrics

Preferred: Ahrefs MCP (keywords_explorer_overview) → volume, KD, CPC, SERP data.

Fallback (no Ahrefs MCP): Use WebSearch to estimate:

• Search [keyword] search volume KD CPC site:ahrefs.com OR site:semrush.com OR site:moz.com
• Extract available volume/KD/CPC estimates
• If no data found, set VOLUME/CPC/DIFFICULTY to [not available — add manually]

Step 2 — Competitor H2/H3 Analysis

• WebSearch [keyword] → identify top 3 organic results (skip ads, maps, featured snippets)
• WebFetch each URL → extract all H2 and H3 headings
• Note: competitor structure informs the H2_OUTLINE. Do not copy — use as gap analysis.

Step 3 — Search Intent Classification

Based on keyword + SERP type, classify as one of exactly four options:

• Informational — user wants to learn
• Commercial — user is comparing options before buying
• Transactional — user is ready to act/purchase
• Navigational — user seeks a specific brand/site

Step 4 — Priority Scoring

Apply routing logic from references/routing-logic.md:

• HIGH: Volume > 200 AND KD < 40 AND intent is Commercial or Transactional
• MEDIUM: Doesn't meet HIGH criteria but has meaningful volume or strategic importance
• LOW: Low volume, high difficulty, or informational with limited conversion value

Direction Prompt Output Format (Mandatory)

This format is non-negotiable. The text_parser.py script performs label-exact extraction. If any label deviates from the format below — wrong case, extra space, missing underscore — the Notion property will arrive empty.

After data gathering, output the brief using EXACTLY this structure:

TARGET_KEYWORD: [keyword]
VOLUME: [number or "not available"]
CPC: [decimal or "not available"]
DIFFICULTY: [0-100 integer or "not available"]
SEARCH_INTENT: [Informational | Commercial | Transactional | Navigational]
AUDIENCE: [persona — job title or role]
RECOMMENDED_H1: [final proposed title]
CONTENT_ANGLE: [one paragraph describing the unique angle, why this beats competitors, what the post must do]
WORD_COUNT: [number]
SCHEMA: [schema type — e.g., FAQ, HowTo, Article, FAQ + HowTo]
PRIORITY: [HIGH | MEDIUM | LOW]
H2_OUTLINE:
- H2: [heading]
  - H3: [subheading]
  - H3: [subheading]
- H2: [heading]
  - H3: [subheading]
FAQ:
- Q: [question the audience actually searches]
- Q: [question]
- Q: [question]
INTERNAL_LINKS:
- [anchor text] → [relative URL or page title if URL unknown]
WRITER_NOTES:
[one paragraph of specific guidance: tone, POV, what to avoid, key differentiators to emphasize, CTAs, any client-specific requirements]

Critical rules:

• Every field label must appear exactly as shown (ALL_CAPS with underscores)
• SEARCH_INTENT value must exactly match one of the four options (capitalized)
• PRIORITY value must be exactly HIGH, MEDIUM, or LOW
• H2_OUTLINE, FAQ, INTERNAL_LINKS, WRITER_NOTES are block fields — content follows on the next line(s)
• No additional text before TARGET_KEYWORD or after WRITER_NOTES block

Workflow Architecture (9 Nodes)

In Claude Code without MCP connectors: Nodes 1–5 run automatically. Nodes 6–9 produce a ready-to-paste summary and instruct the user on manual Notion entry.

Batch Processing Mode

Trigger: user provides a CSV with a Target Keyword column (optional Priority Override column).

Process:

1. Confirm CSV is readable and Target Keyword column exists
2. Run each keyword through the full pipeline sequentially
3. Output all briefs in sequence, separated by ---
4. After all briefs: output a summary table (keyword | priority | word count | schema)

Expected throughput: 20 briefs in 20–25 minutes (60–75 seconds per brief).

If any keyword fails (no SERP data, ambiguous intent): note it in the summary table as [FAILED — reason] and continue processing remaining keywords.