claude

ai-search-blog-optimiser

Multi-agent blog optimiser for AI search. Crawls a blog, extracts brand voice, reads Peec AI gaps, benchmarks against cited competitors, and rewrites articles with evidence-grounded recommendations.

0 on GitHubby AI Heroesv0.7.1Apache-2.0 WITH Commons-Clauseupdated 6 months agomarketing
/

Plugin ai-search-blog-optimiser from marketplace ai-heroes-blog-optimiser in mlobo2012/AI-search-blog-optimiser. Commands read from the repo's manifest — 1 month ago.

This plugin runs code on your machine — it starts local MCP servers. The exact commands are listed below; read them before installing.

What it installs

Skills (2)

  • blog-optimiser-pipeline

    Canonical orchestration playbook for the AI Search Blog Optimiser pipeline. Runs in the main session, uses disk-first state, and only opens the dashboard after a fresh run has been registered.

  • peec-gap-read

    Canonical recipe for reading a brand's gap data from the Peec AI MCP for a specific article. Use when you need to produce the evidence trail that grounds a GEO recommendation in live AI citation behaviour. Consumed by the peec-gap-reader sub-agent and directly by the recommender when running single-

Slash commands (1)

  • /ai-search-blog-optimiser:blog-optimiser

    GEO-optimise a blog. Runs in the main session — never via an orchestrator sub-agent.

Subagents (7)

  • blog-crawler

    Crawls a blog index and its articles via Firecrawl or Crawl4AI, captures a structural fingerprint for each article, and persists artefacts through the local dashboard MCP.

  • competitor-crawler

    Crawls competitor article URLs via Firecrawl or Crawl4AI and writes comparison records through the local dashboard MCP.

  • evidence-builder

    Builds a reviewer-aware evidence pack for one article using public first-party and external sources, then writes it as a real run artifact.

  • generator

    Generates the optimised draft for one article using the rewrite blueprint and the site-scoped voice baseline.

  • peec-gap-reader

    Reads Peec AI data for a single article and writes the gap evidence artefact through the local dashboard MCP.

  • recommender

    Produces evidence-grounded GEO recommendations for one article using the article capture, deterministic rubric lint, Peec gaps, competitor evidence, and the site-scoped voice baseline.

  • voice-extractor

    Reads the captured article corpus for one site and writes a site-scoped voice baseline through the local dashboard MCP.

MCP servers (1)

  • blog-optimiser-dashboard

    runs: python3 ${CLAUDE_PLUGIN_ROOT}/dashboard/server.py --plugin-root ${CLAUDE_PLUGIN_ROOT}

View source on GitHub

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