Install
openclaw skills install @ljseeking/geo-growth-orchestratorUnified GEO growth workflow for brand knowledge base building, LLM visibility audits, Doubao/DeepSeek readiness review, AI-GEO content asset generation, platform draft planning for Zhihu/Toutiao/CSDN/Juejin, client delivery reports, internal QA, and 7/14/30 day retests. Use when Codex needs to analyze or improve a brand's AI search visibility, generate GEO reports, diagnose model mentions/citations/rankings, create GEO-friendly content tasks, or consolidate GEO platform workflows into one human-reviewed delivery package.
openclaw skills install @ljseeking/geo-growth-orchestratorThis skill is now the unified GEO workflow. It can run as one self-contained skill instead of requiring separate Doubao audit, DeepSeek audit, AI-GEO content, and platform draft skills.
Use the old neighboring skills only as optional references when the user explicitly asks to inspect or preserve their original behavior. Do not require the user to understand or invoke multiple GEO skills.
Turn brand materials into a human-reviewed GEO delivery package:
audit_only: visibility diagnosis and report.content_only: content assets and platform drafts.full_workflow: audit, gap analysis, assets, drafts, delivery report, retest plan.debug_existing_report: investigate mismatch between model output and report.brand_name.company_name if present.brand_aliases, including abbreviations, city + brand, brand + service, legal name, common short name, old names, pinyin/English names, and likely spelling variants.verified_live_check: live model/API/platform check with query, answer, time, and raw evidence.manual_check: user-provided screenshot, copied answer, or manual summary with time/source.inferred_estimate: reasoning from brand materials only.unverified_assumption: weak assumption for planning only.Minimum natural-language input is acceptable. Prefer structured fields when available:
| Field | Use |
|---|---|
brand_materials | Company intro, website text, product/service details, FAQ, cases, channels, compliance notes |
brand_name | Primary brand/entity name |
brand_aliases | Known short names, alternate names, source titles, common model spellings |
target_keywords | Category and decision keywords; do not treat these as brand aliases |
target_models | doubao, deepseek, generic, chatgpt, perplexity, other |
target_platforms | zhihu, toutiao, csdn, juejin, xiaohongshu, douyin, website |
existing_geo_report | Prior report, copied model answers, screenshots, source lists, citation ranks, score JSON |
mock_model_outputs | User-provided or demo model answers for scoring; must not be presented as live evidence |
campaign_goal | Audit, visibility improvement, content generation, lead generation, retest |
compliance_constraints | Forbidden claims, industry limits, required disclaimers |
Load only the reference needed for the current task:
references/entity-evidence-rules.md when diagnosing mention/citation/ranking mismatches, using model answers, scoring audits, or handling aliases.references/geo-unified-modules.md when running the full workflow or generating stage outputs.references/platform-style-guide.md when creating platform-specific drafts or content task plans.Scripts and schemas:
scripts/generate_full_report.py for dual-model report generation from structured input.scripts/generate_client_report.py and scripts/generate_internal_report.py when assembling client/internal reports from artifacts.schemas/ and templates/ to keep outputs consistent.Capture brand, business category, target market, keywords, target models, target platforms, goal, constraints, and available evidence. If the brand has multiple names, create aliases before scoring.
Build a compact knowledge base with:
Mark unknown facts as 待确认. Never invent prices, locations, cases, certificates, rankings, or third-party endorsements.
Use natural, non-leading probes covering:
For each answer, separate:
answer_mention: target entity appears in the answer body.citation_mention: target entity appears in citation/source/title/link.source_rank: target entity's rank in source/citation list.brand_alias_hits: matched aliases.category_keyword_hits: category terms only.competitor_mentions: alternatives or competitors.evidence_level: verified/manual/inferred/unverified.Do not collapse citation rank into answer mention. Do not count category keywords as brand mentions.
Convert audit results into gaps. Each gap must include scenario, involved model, evidence source, business impact, priority, and remediation action.
Turn gaps into concrete tasks with platform, title, target keyword, target user, intended role, source gap, brand points to include, fact dependencies, compliance notes, and review status.
Generate reusable assets from the brand knowledge base:
llms.txtAll content must be factual, structured, low-hype, and explicit about boundaries.
Route by platform:
Never auto-publish. Human review is mandatory.
Default output mode is full_report. Unless the user explicitly asks for summary only, output the complete Markdown report body in the conversation and save artifacts when working locally.
The report must include:
Keep internal details out of the client report but preserve them in internal QA:
When a user reports "model shows us but the report says not mentioned":
brand_name against brand_aliases.For local service brands, always expect name variants such as city + brand, brand + service, brand + school/company, and short brand names.