morphiq-build

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This skill should be used when the user asks to "fix the issues", "optimize existing content", "create new content for AI visibility", "run Morphiq Build", "generate schema markup", "create an llms.txt file", "run the content lab", or mentions building content fixes, generating schema, rewriting content for AI citations, or creating policy files. Consumes a Prioritized Roadmap (or user prompt, or existing content) and produces build artifacts through a 6-step content lab pipeline.

2installs

Sourcemorphiqlabs/morphiq-labs-skills

Added on2026-03-30

NPX Install

npx skill4agent add morphiqlabs/morphiq-labs-skills morphiq-build

SKILL.md Content

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Pipeline Position

Step 3 of 4 — consumes morphiq-rank output.

Input: Prioritized Roadmap (JSON) OR user prompt OR existing content.
Output: Build Output (JSON + artifacts) → consumed by morphiq-track.
Data contract: See
```
PIPELINE.md
```
§3 for the Build Output schema.

Purpose

Morphiq Build fixes issues from morphiq-rank. It creates new content, optimizes existing content, generates schema markup, builds policy files, and produces artifacts to improve AI visibility. The core engine is a 6-step content lab pipeline.

Entry Points

Path A — From Prioritized Roadmap: Process issues by tier and priority. Route each to the appropriate fix workflow.

Path B — From User Prompt: Accept topic, optional source URLs (up to 5), optional ICP/brand context. Route to content lab pipeline.

Path C — From Existing Content: Accept content URL or raw text. Route to quality rewrite workflow.

Content Lab Pipeline (6 Steps)

Step 1: Ingest Sources

Validate URLs, filter blocked domains, deduplicate, cap at 10. Accept raw text or PDF alternatives. Halt if zero valid sources.

Step 2: Extract Content

Crawl each URL → clean markdown. Extract title, content, outbound links, publish date. Halt if zero successful extractions.

Step 3: Analyze Gaps

Analyze against query space. Identify 5 gap types:

Gap Type	What Is Missing
Content	Unanswered questions, missing perspectives
Data	Missing statistics, quantitative evidence
Format	Wrong format for LLM retrieval
Depth	Surface-level, no expert insight
Fanout coverage	Sub-queries AI would chain but site cannot answer

Detect comparative intent. Evaluate fanout coverage using content type → sub-query rules. Generate up to 5 search queries.

For gap taxonomy and severity, read

references/gap-taxonomy.md

.

Step 4: Research to Fill Gaps

Run up to 5 live web searches. Collect authoritative sources, statistics (number + source + URL), expert quotes (speaker + credential), industry insights. If comparative intent, dedicate 1 search to brand data.

For citation rules, read

references/enrichment-sources.md

.

Step 5: Generate / Rewrite

Produce final content applying Morphiq standard:

E-E-A-T signals, name-drop citations, expert quotes
Heading hierarchy, 50–75 word paragraphs, direct-answer blocks
Brand positioning (comparative or authority mode)
1,200–1,600 words, 5–7 H2 sections, FAQ with 3–5 Q&As
Minimum 3 statistics, 1 expert quote, sources section
No fabricated case studies

For full pipeline spec, read

references/content-lab-pipeline.md

.

Step 6: Validate Fanout Coverage (Fanout Issues Only)

For

fanout-*

issues with

fanout_context

: validate generated content addresses all triggering sub-queries and meets the competitive quality floor. If coverage < 80% or quality floor not met, revise once. Skip for non-fanout content.

Run

scripts/validate-coverage.py

with generated content + triggering sub-queries + quality floor.

Post-Pipeline Processing

Process	What It Does	Reference
Schema Injection	Classify content type, generate JSON-LD. New content: embed schema in content artifact. Existing content: separate schema artifact with implementation tracking.	`references/schema-templates.md`
Metadata Optimization	Meta description, slug, OG tags	`references/metadata-patterns.md`
llms.txt Generation	Full autonomous pipeline: scrape → LLM → validate → repair → template fallback	`references/llms-txt-spec.md`
Content Restructuring	Fix headings, split paragraphs	—
Internal Linking	Link related pages for `site:` coverage	`references/content-lab-pipeline.md`
Enrichment	Additional search for missing stats/citations	`references/enrichment-sources.md`
FAQ Generation	Generate FAQ from gap analysis	`references/faq-guidelines.md`

Issue Type → Fix Routing

Issue Category	Fix Approach
`agentic-*` schema	Schema Injection — generate JSON-LD
`agentic-*` metadata	Metadata Optimization — generate tags
`content-*` quality	Quality Rewrite — Step 5 pipeline
`chunking-buried-answer`	Quality Rewrite — Step 5 pipeline (Claude-driven rewrite to answer-first structure)
`chunking-*` structure (other)	Content Restructuring
`policy-*` files	Policy file generation
`fanout-*` coverage	Full 6-step pipeline for new content. When `fanout_context` is present, pass `triggering_sub_queries` to Step 3 and `competitor_sources` to Step 4. Run Step 6 (coverage validation) before post-pipeline processing.
`visibility-*`	Enrich existing content via pipeline

Build Output

Artifacts with

type

: "content", "schema", "metadata", "policy_file". Each includes placement instructions.

Reference Files

File	Purpose
`references/content-lab-pipeline.md`	Full 6-step pipeline with I/O formats
`references/gap-taxonomy.md`	Gap types, severity, search query rules
`references/enrichment-sources.md`	Citation format, source preferences
`references/schema-templates.md`	JSON-LD templates, skip conditions
`references/metadata-patterns.md`	SEO metadata rules
`references/llms-txt-spec.md`	llms.txt spec and generation
`references/faq-guidelines.md`	FAQ generation rules