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Brief Outline Generator

infrasity-labs/dev-gtm-claude-skills

  • 77 installs
  • 93 repo stars
  • Updated June 28, 2026
  • infrasity-labs/dev-gtm-claude-skills

Brief Outline Generator is an agent skill that produces a structured go-to-market brief outline solo builders can approve before full copy or campaign work.

About

Brief Outline Generator is an agent skill from the infrasity-labs dev-gtm-claude-skills collection that helps solo and indie builders produce a structured go-to-market brief outline before they invest time in full copy, creative assets, or launch plans. Instead of asking a coding agent to improvise positioning in chat, you invoke the skill when you know you need a brief—product announcement, campaign one-pager, landing narrative, or partner pitch—but only have rough inputs. The skill is aimed at Claude Code and similar agent environments where reusable SKILL.md capabilities beat one-off prompts. Use it when you are still scoping what the brief must cover: audience, promise, proof points, channels, and calls to action. It matters because outlines set the contract between your idea and downstream generators; a clear scaffold makes later validate, launch, and grow tasks faster and easier to review. Catalog metadata for the readable SKILL.md body was unavailable at ingest, so capability wording follows the skill slug and repository context.

  • Generates a structured outline for marketing or product briefs instead of free-form chat
  • Fits infrasity-labs dev-gtm-claude-skills for indie go-to-market workflows
  • Useful before writing landing pages, launch posts, or campaign copy with an agent
  • Outputs section-ready scaffolding agents can expand into full briefs or docs
  • Reduces rework by agreeing on outline structure before creative execution

Brief Outline Generator by the numbers

  • 77 all-time installs (skills.sh)
  • +4 installs in the week ending Jul 25, 2026 (Skillselion tracking)
  • Ranked #492 of 854 Sales & Marketing skills by installs in the Skillselion catalog
  • Data as of Jul 26, 2026 (Skillselion catalog sync)
npx skills add https://github.com/infrasity-labs/dev-gtm-claude-skills --skill brief-outline-generator

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Installs77
repo stars★ 93
Last updatedJune 28, 2026
Repositoryinfrasity-labs/dev-gtm-claude-skills ↗

What it does

Scaffold a go-to-market brief outline before your agent writes landing copy, campaign assets, or launch messaging.

Who is it for?

Best when you're preparing a product or launch brief and want a repeatable outline before handing work to other GTM or content skills.

Skip if: Skip if you already have an approved brief, spec, or creative deck and only need light edits or formatting.

When should I use this skill?

You need a structured go-to-market or product brief outline and do not yet have approved sections for messaging, proof, or channels.

What you get

You get a sectioned brief outline your agent can expand into landing copy, launch posts, or campaign docs after you approve the scaffold.

  • Structured brief outline with named sections
  • Scaffold ready for agent expansion into full GTM copy
  • Approved section list for downstream launch or content work

Files

SKILL.mdMarkdownGitHub ↗

Brief Outline Generator

Generates a content outline as a formatted .docx file. The output is a skeleton — section headings, short topic prompts, angles for each section — that a writer fills in with their own conclusions, numbers, and prose.

This is an outline generator, not a brief generator. If your output reads like an article in note form, you've gone too far. Read references/section-rules.md before generating anything.

The DOCX is always produced by running `scripts/generate-brief.py`. Do not reimplement the renderer. Do not write inline docx code. Assemble the config JSON and run the script.

---

Inputs — collect from user before proceeding

FieldRequiredNotes
title✅Blog post title. Warn if > 70 chars.
focus_keyword✅Primary keyword
sitemap_url✅Full sitemap URL, e.g. https://firefly.ai/sitemap.xml. domain_url is derived from this automatically.
word_count_range✅e.g. 1500-2000
target_intent✅Informational, Commercial, Transactional, or Navigational
target_product⬜Product name — triggers a product integration section if provided
secondary_keywords⬜Pre-supplied list; skip generation if provided

---

Execution workflow — follow these steps in order

Step 1 — Validate inputs

  • title: non-empty; warn (don't block) if > 70 chars
  • focus_keyword: non-empty
  • sitemap_url: non-empty, starts with http:// or https:// (accept any path — .xml, .xml.gz, or extensionless dynamic URLs are all valid)
  • word_count_range: two positive integers separated by - (e.g. 1500-2000)
  • target_intent: one of Informational, Commercial, Transactional, Navigational (case-insensitive)

Derive two values from sitemap_url:

  • domain_url — strip the path to get the origin, e.g. https://firefly.ai/sitemap.xml → https://firefly.ai. Used in the config JSON.
  • domain_hostname — strip the protocol too, e.g. https://firefly.ai → firefly.ai. Used in tool calls that require a bare hostname (e.g. site:firefly.ai).

Stop and report all validation errors before proceeding.

---

Step 2 — Read the rules

Read `references/section-rules.md` in full now. It contains:

  • The outline-vs-brief distinction (the most important rule)
  • Hard bullet rules (≤ 12 words, no invented numbers, no conclusions, no em-dash clauses)
  • Hard structure rules (no topic_summary, no Writer Directives box, TLDR carries 2–3 topic pointers)
  • The four archetypes (Listicle, Comparison, How-to, Concept/Explainer) and their section sets
  • Per-section rules and good/bad bullet examples
  • A final quality check to run before generating the DOCX

Do not skip this step. Generating without reading the rules produces brief-style output every time.

---

Step 3 — Domain analysis

DataForSEO tools are deferred — load them before calling. Call tool_search(query="on_page content parsing") at the start of this step.

3a — Discover site URLs

Call web_fetch on sitemap_url.

  • Readable XML (response contains <loc> tags) → extract every <loc> URL. These are your site URLs.
  • Binary / compressed (unreadable response) → fall back: call dataforseo:serp_organic_live_advanced with keyword="site:{domain_hostname}" (e.g. site:firefly.ai) to get all indexed URLs.

Cap at 20 URLs. If more exist, prioritise: homepage → product/use-case pages → blog/resource pages.

3b — Read full page content

For each URL from 3a, call dataforseo:on_page_content_parsing with enable_javascript: true.

This returns fully rendered page text — headings, body copy, product descriptions, etc. Collect all output.

If a page fails (bot protection, timeout), skip it and continue — do not abort.

3c — Extract meta title and description

From the homepage content parsed in 3b, extract:

  • Page <title> → meta_title
  • <meta name="description"> content → meta_description

Surface character count advisories if outside recommended ranges:

  • meta_title outside 50–60 chars → "Note: fetched meta title is N chars (recommended: 50–60)."
  • meta_description outside 150–160 chars → "Note: fetched meta description is N chars (recommended: 150–160)."

These are passed into the config as meta_title and meta_description and rendered in the metadata table.

3d — Compile domain_context

From all parsed page content, extract and store:

  • Product name and core value proposition
  • Key technical terms and vocabulary used on the site
  • Target audience signals (roles, team types, use cases mentioned)
  • Existing content topics — used to avoid duplication in the outline

Store as domain_context. Do not render `domain_context` in the output document. If all fetches fail, set domain_context = null and continue.

---

Step 4 — Classify the title's archetype

Based on the title pattern, pick one of:

  • Listicle / Tool Roundup — "Top N", "Best X", "X Alternatives"
  • Comparison / Versus — "X vs Y", "X or Y"
  • How-to / Implementation Guide — "How to X", "How do X teams Y", "Implementing X"
  • Concept / Explainer — "What is X", "Designing X", "[Function/Pattern]: Designing X"

If multiple seem to fit, use the defaults from section-rules.md. If still unclear, ask the user.

Announce the chosen archetype to the user before generating — one line, e.g. "Detected archetype: How-to. Generating outline with intro → strategies → case studies → implementation → FAQs." Let them override if they disagree.

---

Step 5 — Generate keyword volumes

Fetch USA monthly search volumes for the focus keyword and each secondary keyword using DataForSEO.

DataForSEO tools are deferred — load them before calling.

1. Call tool_search(query="keyword search volume google ads") to load the DataForSEO keyword tools. 2. Call dataforseo:kw_data_google_ads_search_volume with the full list of keywords (focus + all secondaries) in a single call. Use location_code for the USA (2840). 3. From the response, extract the search_volume field for each keyword. 4. Format each volume as a thousands-separated string (e.g. 3400 → "3,400"). Volumes under 1,000 stay as plain digits (e.g. "500", "30"). Volume of 0 should be rendered as "0", not "N/A" — it's a real datapoint. 5. If a keyword returns no data, set its volume to "N/A" and continue. Don't abort the whole run. 6. If tool_search returns no DataForSEO tools (connector not installed), set every volume to "N/A" and surface a one-line warning: "DataForSEO connector not available — keyword volumes set to N/A."

Never omit the volume field on any keyword. Every row must have one.

Flag volume mismatches. If the focus keyword's volume is more than 10× smaller than any secondary keyword's volume, tell the user before generating: "Note: your focus keyword has volume X, but secondary keyword Y has volume Z. Consider whether Y should be the focus." Let them decide; don't auto-swap.

Store as:

"focus_keyword_volume": "2,400",
"secondary_keywords": [
  { "keyword": "disaster recovery plan", "volume": "1,900" },
  { "keyword": "cloud DR strategy",      "volume": "N/A"   }
]

If no secondary keywords were supplied, generate 5 by combining base terms from the focus keyword, top domain key terms, and modifiers ("best practices", "guide", "checklist", "for teams", current year). Then fetch volumes for them the same way.

---

Step 6 — Build the outline using the archetype's section set

Use the section set for the archetype you chose in Step 4. Do not force every topic into a how-to template. A listicle has no Problem or Case Studies section. A comparison has no Implementation steps.

Each section object shape:

{
  "heading": "H2",
  "title": "Section Title",
  "rules": ["short topic prompt 1", "short topic prompt 2"],
  "subsections": [
    {
      "heading": "H3",
      "title": "Subsection Title",
      "rules": ["..."],
      "subsections": []
    }
  ]
}

Fields removed from the previous schema (do not use):

  • ~~topic_summary~~ — removed. Outlines don't have abstracts.
  • ~~directives~~ — removed. The bullets themselves carry the direction.
  • ~~visual~~ — removed. If a visual matters, write it as a bullet prompt: "Include a comparison table: dimension × tool".
  • ~~faqs~~ — removed. FAQs are questions only (in rules); the writer drafts answers.

Bullets in `rules` follow the hard rules in `section-rules.md`:

  • ≤ 12 words each
  • Must be specific and a complete thought
  • No invented numbers
  • No conclusions — topic prompts only
  • No em-dash explanatory clauses

Length follows substance, not a fixed cap. Use as many bullets as the section honestly needs — 2 if 2, 9 if 9. The "typical ranges" in section-rules.md are orientation, not limits. Padding to hit a target number is wrong; truncating to hit one is also wrong.

TLDR section: include a TLDR heading with 2–3 short topic pointers (each ≤ 12 words) that name what the writer should cover in the TLDR. Pointers are topics, not finished takeaways. Pull from the article's central tension, the main shift the reader should make, and the practical next step. See the TLDR section in references/section-rules.md for good/bad examples.

FAQs section: Use the rules field with a list of question strings — questions only, no answers. The writer drafts the actual answers. Each question is one bullet. Focus on how questions are framed: concrete and action-oriented ("How do I install a Claude skill?"), not abstract ("How does Claude decide which skill to invoke?"). All user-supplied keywords (focus + each secondary) must appear naturally across the question set — paraphrased into something a real searcher would type, never keyword-stuffed. Typically 5–8 questions but no fixed cap. See the FAQs section in section-rules.md for examples.

---

Step 7 — Run the final quality check

Before assembling the config, walk the outline and verify (this is from section-rules.md):

1. Could a writer publish this by adding transition words? → If yes, strip back. 2. Does every bullet read as a topic prompt, not a sentence? (except FAQ questions, which are naturally full sentences) → If no, rewrite. 3. Could a writer guess each bullet's meaning in 3 different ways? → If yes, the bullet is too abstract. Name the actual thing (files, components, technical terms). 4. Is every bullet a complete thought when read aloud? → If a bullet is a sentence fragment with no meaning ("Who this is for: builders doing X"), finish the thought. 5. Is every technical claim accurate? → Skills are packages not folders; Claude reads SKILL.md, not the whole skill eagerly. If unsure, rewrite to avoid the claim. 6. Any invented numbers? → Remove. 7. Any sub-bullet that feels like its own H2? → Promote it. 8. Are H2 titles specific to the article's topic (not generic verb phrases)? → "Define the task narrowly" → "Define what your skill should do." 9. Section order matches the archetype? → Verify. 10. No topic_summary, directives, or faqs fields present? → Strip if present. FAQ questions live in rules as flat bullets. 11. Do all user-supplied keywords (focus + each secondary) appear naturally across the FAQ questions? → If any is missing, rewrite a question to incorporate it. 12. Are FAQ questions concrete and action-oriented ("How do I install...", "What is the difference between..."), not abstract ("How does Claude decide...")? → Rewrite vague ones.

If all 12 pass, proceed.

---

Step 8 — Assemble the config JSON

Write the complete config to /home/claude/brief-config.json:

{
  "title": "...",
  "focus_keyword": "...",
  "focus_keyword_volume": "N/A",
  "domain_url": "...",
  "word_count_range": "...",
  "target_intent": "...",
  "target_product": "...",
  "archetype": "how_to",
  "meta_title": "...",
  "meta_description": "...",
  "secondary_keywords": [
    { "keyword": "...", "volume": "N/A" }
  ],
  "output_path": "/mnt/user-data/outputs/outline-{file_slug}.docx",
  "outline": [ ... ]
}

Slug note: Two slugs are generated internally:

  • URL Slug (shown in the metadata table) — derived from focus_keyword, e.g. "cloud disaster recovery" → cloud-disaster-recovery.
  • File slug (used in the filename) — derived from title, e.g. "How Do Platform Teams Implement Cloud Disaster Recovery" → outline-how-do-platform-teams-implement-cloud-disaster-recovery.docx.

The output_path in the config should reflect the title-based file slug.

Do not include `domain_context` in the config. It informs generation upstream; it never appears in the rendered doc.

---

Step 9 — Run the generator script

python /path/to/skill/scripts/generate-brief.py --config /home/claude/brief-config.json

The script handles all DOCX rendering. Do not write any docx code yourself.

If the script exits non-zero, report the error from stdout/stderr and show the outline as plain text as a fallback.

---

Step 10 — Present the file

Call present_files with the output path. Report:

✅ Outline generated: outline-{file_slug}.docx
   Archetype:     {archetype}
   Slug (URL):    {url_slug}  (from focus keyword)
   Slug (file):   {file_slug}  (from title)
   Audience:      {audience}
   Focus KW:      {focus_keyword} ({volume})
   Secondary KWs: {kw} ({vol}), ...
   Sections:      {N} sections

---

What the script renders (reference only — do not reimplement)

The script produces a .docx with:

1. Metadata + keyword table — 3 columns: Field | Value | USA Search Volume. Standard rows (Title, URL Slug, Word Count, Target Intent, Target Audience, Meta Title (50-60 chars), Meta Description (150-160 chars)) span cols 2+3. URL Slug is derived from `focus_keyword`, not the title. Focus keyword and each secondary keyword get their own row with volume in col 3 (amber background). Domain and Domain Context rows are NOT included in the table. 2. H1 title 3. Sections — each heading field renders as [H2] or [H3] grey label prefix + heading text. rules render as bullet points. No `topic_summary` block, no Writer Directives box — those have been removed from the format.

---

Reference files

  • references/section-rules.md — Outline-first rules, archetypes, per-section rules, good/bad examples. Read in Step 2 before doing anything else.
  • scripts/generate-brief.py — The DOCX renderer. Always run this. Never reimplement it.
  • examples/ — Canonical good outlines, for reference only. Read these to calibrate bullet length, density, and tone — not to copy section structure, headings, or topics. The archetype rules in references/section-rules.md decide structure for the current topic. Never use an example outline as a template for the user's outline, even if the topic looks similar.

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How it compares

Use instead of asking the agent to draft full marketing copy from a blank prompt when you still need to agree on brief structure first.

FAQ

Who is brief-outline-generator for?

Brief Outline Generator is for developers using Claude Code, Cursor, Codex, or similar agents who ship their own go-to-market work and want a structured brief before creative execution.

When should I use brief-outline-generator?

Use it in Validate when scoping messaging and deliverables, in Launch when planning distribution narratives, and in Grow when outlining content series—whenever you need a brief skeleton before full drafts.

Is brief-outline-generator safe to install?

Treat it like any third-party agent skill: review the Security Audits panel on this Prism page and the source repo before enabling it in projects with sensitive customer or revenue data.

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