Seo machine

SEO Machine

End-to-end engine for building organic search traffic. Ships dozens of programmatic landing pages — alternatives, comparisons, use-cases, playbooks — backed by real keyword research, with a persistent roadmap the user resumes across sessions.

Conversion-first (alternatives + comparisons before blogs). Publish-early (a thin page indexed today beats a perfect page indexed in three weeks). Stack-native (writes into the user's framework — Next.js, Astro, Rails+Inertia, or a portable markdown fallback).

Two modes, auto-detected

Mode	When	What it produces
Initialize	docs/seo-machine.md does not exist	Stack detection → brand context → keyword research → tech audit → roadmap + link inventory
Resume	docs/seo-machine.md exists	Reads the tracker, picks the next pending phase (or one the user names), executes end-to-end through the quality gates

Detection rule on every invocation: check docs/seo-machine.md at repo root (or the path recorded in .seo/config.json). No file → Initialize. File exists → Resume. The user does not need to remember which mode they're in.

Publishing Order (the load-bearing decision rule)

The roadmap orders patterns by conversion intent first, traffic intent second. This is the single most important decision the skill makes — it's why the alternatives + comparison pages ship before the playbooks + blog posts.

Order	Pattern	Why	Cost
1	/alternatives/[competitor] (Pattern A)	Highest conversion intent — the reader is already in market	~4 hours per page
2	/compare/[a]-vs-[b] (Pattern D)	Be the third option in any two-vendor comparison	~3 hours per page
3	/for/[use-case] and /for/[audience] (Pattern B/C)	JTBD without competitor name in query — high TP/volume ratio	~4-5 hours per page
4	/playbooks/[topic] (Pattern E)	Authority + AI-citation surface area + inbound link draw	~2-3 days per page
5	/blog/... (out of scope here)	Lowest conversion intent; defer until ~10 indexed pages exist	—

Why this order matters: alternatives pages convert 5–20× blog posts at 1/10 the search volume. Shipping one alternatives page is worth ten generic blog posts on the same topic. The roadmap should reflect this — Phase 0 (tech audit) → Phases 1–N (alternatives, then comparisons, then use-cases) → late phases (playbooks) → never (blogs, unless explicitly requested by the user).

Striking-distance boosts (pages already ranking pos 5–20 in GSC) jump the queue when they exist — fastest wins per hour of work.

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On Activation

1. Load brand context where present: brand/keyword-plan.md, positioning.md, competitors.md, audience.md, stack.md. Missing files are fine.
2. Surface what loaded:

Backend Selection

Validate KD/competition with OpenSEO get_keyword_metrics / get_serp_results before generating pages; consume openseo-keyword-clustering outputs when present. Qualitative research uses Exa-stack (or manual) per the shared ordered resolver — record research_backend in .seo/config.json. Full contract: skills/openseo/references/backend-contract.md.

Brand context loaded:
├── Keyword Plan    ✓/✗  (seeds the roadmap; ✗ → generate during Initialize)
├── Positioning     ✓/✗  (drives /compare and /alternatives angles)
├── Competitors     ✓/✗  (target list for /alternatives pages)
├── Audience        ✓/✗  (intent buckets for /for/<persona> pages)
└── Stack           ✓/✗  (selects references/stacks/<framework>.md)

Progressive enhancement: with zero brand files the skill still works — it derives signal from the repo during Initialize Step 2. With full brand context it skips re-discovery and goes straight to research + roadmap.

Exa recipes: references/exa-recipes.md + references/api-stack-recipes.md. Manual fallback: references/manual-research.md. Ahrefs is an optional paid overlay only (see Exa recipes appendix).

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Hard prerequisites

#	Check	Failure mode
1	git rev-parse --is-inside-work-tree succeeds	Stop. Tell the user this skill writes a persistent roadmap; initialize git or run from a repo.
2	A stack is detectable — one of package.json, Gemfile, composer.json, requirements.txt, astro.config.*, next.config.*, nuxt.config.*, gatsby-config.*, _config.yml, config.toml, pyproject.toml	Ask the user what stack they're on before continuing.
3	Research backend resolved (skills/openseo/references/backend-contract.md)	Never refuse to run — fall through to manual mode.

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Initialize mode

Goal: end the run with a written docs/seo-machine.md containing keyword research, a phase tracker grouped by pattern, and technical-audit findings — plus .seo/brand.md and .seo/link-inventory.md.

Step 1 — Detect stack + frontend convention

Read references/stacks/detection.md. Identify framework family, routing convention (file-based vs controller-based), component language, and existing marketing pages (git ls-files | grep -iE 'marketing|landing|pages/(home|about|pricing)'). Resolve ambiguity with AskUserQuestion. Persist to .seo/config.json.

Step 2 — Detect brand + product context

Read every signal first, propose .seo/brand.md, then ask only about gaps. Signals: CLAUDE.md, README.md, package.json / Gemfile.lock, tailwind.config.* or design-token CSS, app/views/marketing/* or pages/index.*, pricing page.

If brand/voice-profile.md and brand/positioning.md already exist (mktg-native project), read them and skip 80% of the questions — those files already say who the buyer is and what the brand sounds like.

Otherwise use a single AskUserQuestion (3–4 questions max) to fill gaps. Required to know:

• Product one-liner (≤20 words)
• Primary persona (e.g. "B2B SaaS founder", "indie agency owner")
• 3–7 direct competitors by name
• Brand voice tags (e.g. "honest, technical, no-jargon")
• Free tier? (drives the "is [brand] free" keyword strategy)
• Anti-positioning — what the product does NOT do (used in honest comparison sections)

Write to .seo/brand.md using assets/brand-template.md as the skeleton.

Step 3 — Keyword research (Exa-native stack)

Follow references/exa-recipes.md — 7 Exa-native recipes that replace the Ahrefs cookbook:

Recipe	Purpose	Primary API
A. Domain baseline	DR estimate from indexed-page count + brand mentions + GitHub stars (OSS)	Exa company_research_exa + web_search_advanced_exa + gh
B. Competitor reverse-lookup	What competitors rank for	Exa web_search_advanced_exa site:<competitor> + AI summary
C. Use-case sweep	/for/ and /playbooks/ topic discovery	Exa deep_search_exa + Firecrawl autocomplete + /last30days
D. Comparison volume	Validate demand for /compare/[a]-vs-[b] before writing	Exa web_search_advanced_exa + Firecrawl SERP scrape
E. SERP saturation	Replaces Ahrefs KD with SERP-composition-derived signal	Exa search + per-result company_research_exa
F. Backlink prospecting	Referring-domain proxy via Exa	Exa search + DR proxy (see off-page-seo skill)
G. Content gap	Their indexed keywords − ours, ranked by DR-cap fit	Recipes B + own-site Exa lookup

Then pull cross-API compound recipes from references/api-stack-recipes.md for moves Ahrefs alone can't do — pain-point cluster discovery (/last30days + mktg-x + Exa deep search), OSS competitor teardown (gh + DeepWiki + Exa), outreach prospect discovery (Exa Websets + people-research), newcomer surveillance (Exa Websets cron).

When Exa is unavailable, fall back to references/manual-research.md (paste-from-UI workflow). When the user has paid Ahrefs, layer it on top per the appendix in exa-recipes.md — don't replace.

Cache the raw output to .seo/keyword-research.json. Curate the decision-ready summary into the Keyword Research Appendix section of docs/seo-machine.md.

Step 4 — Technical foundations audit

Run scripts/tech_audit.py (sitemap, robots, meta-tag uniqueness, schema). Whatever it finds becomes Phase 0 in the roadmap. Day-0 crawl shapes Google's understanding of the site for months — retroactive fixes are harder.

Step 5 — Generate the roadmap

Fill assets/roadmap-template.md with:

• Site facts (domain, DR, stack, brand colors, fonts) from steps 1–2
• Reference data — paths to controllers / page files this skill will edit
• Keyword Research Appendix — curated, grouped by pattern
• Phase Status Tracker — auto-populated:
  • Phase 0: technical foundations fixes
  • Phase 1+: one row per page candidate
  • Group by pattern. Within a pattern, order by traffic_potential desc → volume desc → KD asc
  • Striking-distance boosts near the front (fastest wins on existing sites)
  • Off-page checklist (directories + outreach) tail-end, one phase per category

Write to the configured path (default docs/seo-machine.md). Print the path back and recommend a human review pass before Phase 0.

Step 6 — Generate .seo/link-inventory.md

Every phase reads this file to pick internal links. Use assets/link-inventory-template.md. Pre-populate from existing routes (features, tools, pricing, blog posts). Each phase appends to it as new pages ship.

Step 7 — Hand off

✓ Initialize complete

  Roadmap:        docs/seo-machine.md
  Brand context:  .seo/brand.md
  Keyword cache:  .seo/keyword-research.json
  Link inventory: .seo/link-inventory.md
  Config:         .seo/config.json

Next: review docs/seo-machine.md, then run me again to execute Phase 0.

Do not auto-execute Phase 0. Pattern priorities and competitor lists are decisions worth a human pass.

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Resume mode

Goal: pick the next phase from the tracker, execute end-to-end with quality gates, hand back so the user can commit/PR.

Step 1 — Read state

Load docs/seo-machine.md, .seo/brand.md, .seo/link-inventory.md, .seo/config.json. Find the Phase Status Tracker, identify the next pending phase with the lowest number, print it back along with the two phases that follow.

Step 2 — Confirm scope

Single AskUserQuestion:

• "Continue with Phase N: [title]?"
• Options: "Yes, start Phase N" / "Pick a different phase" / "Re-audit (refresh research)" / "Just show the tracker"

"Different" → list pending phases. "Re-audit" → loop back into Initialize steps 3–5. "Show" → print tracker and stop.

Step 3 — Execute

Pattern → reference mapping:

Phase type	Reference
Phase 0 — technical foundations	references/technical-audit.md
Pattern A — /alternatives/[competitor]	references/patterns/alternatives.md
Pattern B/C — /for/[use-case] or /for/[audience]	references/patterns/use-case.md
Pattern D — /compare/[a]-vs-[b]	references/patterns/compare.md
Pattern E — /playbooks/[topic] long-form	references/patterns/playbooks.md
Striking-distance boost	references/striking-distance.md
Off-page checklist phase	references/off-page.md
Internal-link spine audit	references/quality-bars.md (link-audit section)

For page-generating phases, the flow is always:

1. Re-research — current competitor pricing, feature changes. Don't trust 60-day-old cached data on commercial-intent terms.
2. Generate the page payload — output format follows the stack (references/stacks/<framework>.md) or references/stacks/markdown-fallback.md.
3. Verify against the quality bar (word count, internal links, schema, honesty section on alts). Run scripts/word_count.py and scripts/link_audit.py. If a check fails, fix it — don't ship under-spec work.
4. Update .seo/link-inventory.md with the new page.
5. Update the tracker row in docs/seo-machine.md — status completed, PR ref or commit SHA. Same edit batch as the page work so reviewers see both in one diff.

Step 4 — Hand off (do NOT auto-commit)

✓ Phase N complete: [title]

Files changed:
  app/controllers/marketing_controller.rb  (added entry)
  app/frontend/pages/Alternatives/Show.tsx (no change — uses existing layout)
  docs/seo-machine.md                      (tracker updated)
  .seo/link-inventory.md                   (new page registered)

Quality gates:
  ✓ Word count: 712 / 600 min
  ✓ Internal links: 2 alts, 1 feature, 1 tool
  ✓ FAQ JSON-LD attached
  ✓ Honesty section present (3 rows)

Suggested commit: "SEO Phase N: ship /alternatives/[slug]"

Next phase pending: Phase N+1 — [title]

Open a PR only if the user has expressed they want that cadence. Otherwise let them drive git.

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Interactive principles

Rule	Why
Ask before you guess on positioning calls	The user's gut on which competitor or use-case to ship next often beats the data
Show the numbers, not the conclusion	"vol 400, KD 12, TP 1,800" builds more trust than "the data is good"
Be honest about what won't work	KD 80 at DR 8 = doomed. Say so, offer the closest winnable alternative
One-sentence theory max per phase	The user doesn't need a recap of SEO theory every run
Surface tradeoffs, not opinions	"5 thin alternatives or 2 deep ones first?" — state the tradeoff, ask

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Adjacent skills (route here vs there)

Skill	When	When NOT
seo-audit	One-off diagnostic on an existing site	Forward execution — use this skill
seo-content	Writing a single rankable article	A whole sprint — use this skill
competitor-alternatives	One /alternatives/<x> page, ad-hoc	Pattern A as part of a sprint — use this skill
keyword-research	Producing brand/keyword-plan.md	This skill consumes that artifact — chain them
ai-seo	LLM-citation optimization for a few pages	Run after this skill ships ~10 indexed pages

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Anti-patterns

Don't	Why
Target head terms when DR is low	"Social media management tool" at DR 8 is wasted work. KD ≤ DR+10 while DR is low.
Ship pages with no inbound internal links	A new page nobody links to is an island. ≥2 inbound links from existing pages, same phase.
Ship /alternatives pages without an "honesty" section	Three honest tradeoffs (where the competitor wins) is non-negotiable. Brand signal AND Google quality signal.
Skip schema markup	FAQPage JSON-LD captures snippets. Article for playbooks. SoftwareApplication for homepage + use-case. Quality gate fails without them.
Auto-commit	Always show diff summary; let the user commit (local conventions, hooks, branch naming).
Restart Initialize when partial state exists	If .seo/config.json exists without the roadmap, prompt before wiping — they may have aborted mid-run.

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File map

File	Purpose
references/methodology.md	Why-this-works theory + lessons from real phases
references/exa-recipes.md	7 Exa-native research recipes (replaces the old Ahrefs cookbook). Ahrefs appendix at the bottom for paid-precision escalation.
references/api-stack-recipes.md	Cross-API compound recipes — pain-point mining, OSS competitor teardown, outreach prospect discovery, newcomer surveillance. Uses Exa + Firecrawl + gh + mktg-x + DeepWiki + last30days.
references/manual-research.md	L0 fallback when no MCP available
references/patterns/alternatives.md	Pattern A spec, data shape, quality bar, example
references/patterns/use-case.md	Pattern B+C spec
references/patterns/compare.md	Pattern D spec
references/patterns/playbooks.md	Pattern E spec (2,500-word bar)
references/stacks/detection.md	Stack-detection signal table
references/stacks/rails-inertia.md	Rails + Inertia adapter (reference implementation)
references/stacks/nextjs.md	Next.js App Router adapter
references/stacks/astro.md	Astro content-collection adapter
references/stacks/markdown-fallback.md	Universal markdown output format
references/technical-audit.md	Phase 0 recipes (sitemap, robots, meta, schema)
references/striking-distance.md	GSC pos 5–20 audit + boost recipe
references/off-page.md	Backlink checklist + Ahrefs referring-domains research
references/quality-bars.md	Verification spec per pattern
scripts/word_count.py	Strip markup → word count
scripts/link_audit.py	Verify internal-link minimums per page
scripts/tech_audit.py	Sitemap.xml + robots.txt + meta-tag scanner
assets/roadmap-template.md	docs/seo-machine.md skeleton
assets/brand-template.md	.seo/brand.md skeleton
assets/link-inventory-template.md	.seo/link-inventory.md skeleton

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Outputs

The skill writes to project paths, not brand/. This is why writes: in skills-manifest.json is [] — the manifest declares only brand/*.md writes, and seo-machine's outputs live in the user's repo, not in brand memory.

Output	Mode	Purpose
docs/seo-machine.md	Initialize (write), Resume (update phase row)	Persistent sprint roadmap — Phase Status Tracker + Reference Data + Keyword Research Appendix. Single source of truth across sessions.
.seo/brand.md	Initialize	Product context derived from repo + user answers (one-liner, persona, competitors, voice tags, anti-positioning).
.seo/link-inventory.md	Initialize (create), Resume (append per phase)	Every internal-link target the skill can use. Each phase appends new pages.
.seo/keyword-research.json	Initialize	Cached raw output from Ahrefs / Exa / manual research. Read by every phase, refreshed every 30 days.
.seo/config.json	Initialize	Stack info, research backend choice, project IDs. Persists machine-readable state.
Phase-specific page files	Resume (per phase)	Stack-native page files (e.g. app/controllers/marketing_controller.rb + app/frontend/pages/Alternatives/Show.tsx for Rails+Inertia; app/[pattern]/[slug]/page.tsx for Next.js App Router; etc.). Paths follow references/stacks/<framework>.md.

This follows the Long-Arc Sprint Persistence Pattern documented in AGENTS.md — docs/seo-machine.md is the canonical doc that survives session interruption.

/cmo integration

/cmo routes here under Playbook #9 — SEO Authority Build, Path B (programmatic sprint). On a cold project /cmo first spawns mktg-brand-researcher, mktg-audience-researcher, mktg-competitive-scanner in parallel (foundation), runs keyword-research to populate brand/keyword-plan.md, then hands off here. After every phase, /cmo spawns mktg-content-reviewer + mktg-seo-analyst in one message to score the page batch before the user commits.

See skills/cmo/rules/playbooks.md and skills/cmo/rules/sub-agents.md for the full handoff contract.