Geo Fundamentals
wasp-lang/wasp
Generative Engine Optimization for AI search engines (ChatGPT, Claude, Perplexity).
When the user has AI visibility, GEO, AI answer gap, or AI citation evidence and wants a prioritized plan to improve where the brand is cited or mentioned in AI answers.
Install AI Visibility Fix Plan
$ npx skills add unifapi-agent/agents --skill ai-visibility-fix-plan -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install unifapi-agent/agents ai-visibility-fix-plan --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/unifapi-agent/agents.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/ai-visibility-agent/ai-visibility-fix-plan .claude/skills/ai-visibility-fix-plan && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "ai-visibility-fix-plan" agent skill from https://github.com/unifapi-agent/agents/tree/main/skills/ai-visibility-agent/ai-visibility-fix-plan into .claude/skills/ai-visibility-fix-plan/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-visibility-fix-plan", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/unifapi-agent/agents/tree/main/skills/ai-visibility-agent/ai-visibility-fix-planType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add unifapi-agent/agents --skill ai-visibility-fix-plan -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install unifapi-agent/agents ai-visibility-fix-plan --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/unifapi-agent/agents.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/ai-visibility-agent/ai-visibility-fix-plan .agents/skills/ai-visibility-fix-plan && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "ai-visibility-fix-plan" agent skill from https://github.com/unifapi-agent/agents/tree/main/skills/ai-visibility-agent/ai-visibility-fix-plan into .agents/skills/ai-visibility-fix-plan/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-visibility-fix-plan", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add unifapi-agent/agents --skill ai-visibility-fix-plan -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install unifapi-agent/agents ai-visibility-fix-plan --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/unifapi-agent/agents.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/ai-visibility-agent/ai-visibility-fix-plan .cursor/skills/ai-visibility-fix-plan && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "ai-visibility-fix-plan" agent skill from https://github.com/unifapi-agent/agents/tree/main/skills/ai-visibility-agent/ai-visibility-fix-plan into .cursor/skills/ai-visibility-fix-plan/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-visibility-fix-plan", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/unifapi-agent/agents.git --path skills/ai-visibility-agent/ai-visibility-fix-plan--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add unifapi-agent/agents --skill ai-visibility-fix-plan -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install unifapi-agent/agents ai-visibility-fix-plan --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/unifapi-agent/agents.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/ai-visibility-agent/ai-visibility-fix-plan .gemini/skills/ai-visibility-fix-plan && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "ai-visibility-fix-plan" agent skill from https://github.com/unifapi-agent/agents/tree/main/skills/ai-visibility-agent/ai-visibility-fix-plan into .gemini/skills/ai-visibility-fix-plan/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-visibility-fix-plan", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install unifapi-agent/agents ai-visibility-fix-planInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add unifapi-agent/agents --skill ai-visibility-fix-plan -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/unifapi-agent/agents.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/ai-visibility-agent/ai-visibility-fix-plan .github/skills/ai-visibility-fix-plan && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "ai-visibility-fix-plan" agent skill from https://github.com/unifapi-agent/agents/tree/main/skills/ai-visibility-agent/ai-visibility-fix-plan into .github/skills/ai-visibility-fix-plan/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-visibility-fix-plan", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add unifapi-agent/agents --skill ai-visibility-fix-plan -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install unifapi-agent/agents ai-visibility-fix-plan --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/unifapi-agent/agents.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/ai-visibility-agent/ai-visibility-fix-plan .opencode/skills/ai-visibility-fix-plan && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "ai-visibility-fix-plan" agent skill from https://github.com/unifapi-agent/agents/tree/main/skills/ai-visibility-agent/ai-visibility-fix-plan into .opencode/skills/ai-visibility-fix-plan/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-visibility-fix-plan", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
ai-visibility-fix-plan- GitHub stars
- 589
- Token cost
- ~1.7k tokens
- SKILL.md length
- 638 words
- Files
- 2
- Skills in repo
- 47
- Repo updated
- First seen
- Licence
- MIT
At a glance
When the user has AI visibility, GEO, AI answer gap, or AI citation evidence and wants a prioritized plan to improve where the brand is cited or mentioned in AI answers.
- Works in 6 steps: Load the gap set. Prefer an existing… → Group misses by root cause. → Choose the build path. → …
- Has AI visibility
- SKILL.md covers Use UnifAPI for live evidence, Workflow, Fix patterns and Output, plus 2 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
AI Visibility Fix Plan is an agent skill from unifapi-agent/agents. When the user has AI visibility, GEO, AI answer gap, or AI citation evidence and wants a prioritized plan to improve where the brand is cited or mentioned in AI answers. Also use on "AI visibility fixes," "AI visibility fix plan," "generative engine optimization fixes," "AI citation fixes," "answer engine optimization fixes," "fix AI answer gaps," or "how do we get cited in ChatGPT/AI Overviews." For finding gaps from scratch, run ai-visibility-audit or ai-answer-gap first.
Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `README.md`).
It sits in Marketing & SEO, covering AI search optimization. It works with OpenAI. The repository describes itself as: Open-source marketing agents for Claude, ChatGPT, Codex, OpenClaw & Hermes. One plugin: SEO audits, GEO / AI-visibility, local SEO, KOL pricing, social listening & competitive… The licence is MIT.
When your agent uses it
- Has AI visibility
- AI citation evidence and wants a prioritized plan to improve where the brand is cited
- Mentioned in AI answers
Example prompts
- “AI visibility fixes,”
- “AI visibility fix plan,”
- “generative engine optimization fixes,”
- “/ai-visibility-fix-plan”
Workflow steps
6 steps, taken from the first numbered list in SKILL.md.
- Load the gap set. Prefer an existing audit or answer-gap table. If absent, run a small prompt set first; do not create a fix plan from…
- Group misses by root cause.
- Choose the build path.
- Score the fix. Use AI search volume as the spine, then adjust for winnability, right-to-win, effort, and risk.
- Write acceptance checks. Each fix must say how to verify it after shipping: re-run geo/serp, read the page with browser/markdown, validate…
- Separate content from distribution. On-site structure fixes, authority edits, and third-party presence work should not be lumped into one…
What it can do on your machine
Read from SKILL.md and the folder at commit fb53247. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
AI Visibility Fix Plan loads about 1.7k tokens when it runs. Until then it costs about 125 tokens; SKILL.md has 638 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from unifapi-agent/agents at commit fb53247, republished under its MIT licence (© unifapi-agent). 638 words, ~1,734 tokens.
Download SKILL.mdSave it as
.claude/skills/ai-visibility-fix-plan/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.- name
- ai-visibility-fix-plan
- description
- When the user has AI visibility, GEO, AI answer gap, or AI citation evidence and wants a prioritized plan to improve where the brand is cited or mentioned in AI answers. Also use on "AI visibility fixes," "AI visibility fix plan," "generative engine optimization fixes," "AI citation fixes," "answer engine optimization fixes," "fix AI answer gaps," or "how do we get cited in ChatGPT/AI Overviews." For finding gaps from scratch, run ai-visibility-audit or ai-answer-gap first.
- license
- MIT
- metadata.author
- UnifAPI
- metadata.version
- 1.0.0
AI Visibility Fix Plan
Turn AI visibility evidence into an execution-ready fix plan for generative engine optimization. The plan should say which prompt gaps to attack, why the current cited source wins, and whether the fix is Structure, Authority, or Presence.
This is an enhanced skill: it reads live public data through UnifAPI when needed, but it remains eyes, not hands. It does not edit pages, post on third-party sites, buy reviews, or manipulate mentions.
Use UnifAPI for live evidence
Start from ai-visibility-audit or ai-answer-gap output. Re-pull only what is stale or missing:
- Per-prompt answer and citations -
geo/serpwithtargetset to the brand domain. Confirm whether the brand is cited, merely named, or absent. - Demand weighting -
geo/keywords/search-volumeso the fix plan attacks prompts people actually ask. - Answer owners -
geo/mentions/top-domains,geo/mentions/top-pages, andgeo/mentions/cross-aggregated-metricsto identify the source or competitor winning the answer. - Organic cross-read -
seo/serpto identify quick wins where the brand ranks organically but is not cited in the AI answer. - Page structure read -
browser/markdownon the brand page and winning source to compare extractability: definition blocks, comparison tables, FAQ sections, cited stats, and clear headings.
Keep the run date, platform, market, prompt set, and billing metadata in the output.
Workflow
- Load the gap set. Prefer an existing audit or answer-gap table. If absent, run a small prompt set first; do not create a fix plan from vibes.
- Group misses by root cause.
- Structure - the brand has the answer, but it is not extractable.
- Authority - the winning source has stronger stats, quotes, citations, freshness, or topical depth.
- Presence - the answer is owned by third-party surfaces where the brand is missing: directories, review sites, listicles, Wikipedia-style pages, communities, or partner pages.
- Choose the build path.
- Update existing page when the brand ranks organically, is name-dropped, or has a near-equivalent page.
- Create net-new page when no credible page exists for a high-demand prompt.
- Earn third-party presence when the cited source is a list, review surface, community thread, or external authority page.
- Score the fix. Use AI search volume as the spine, then adjust for winnability, right-to-win, effort, and risk.
- Write acceptance checks. Each fix must say how to verify it after shipping: re-run
geo/serp, read the page withbrowser/markdown, validate schema, or check the third-party listing. - Separate content from distribution. On-site structure fixes, authority edits, and third-party presence work should not be lumped into one content task.
Show full SKILL.md (236 more words)Show less
Fix patterns
Use these as the default remediation menu:
| Cause | Fix pattern | Acceptance check |
|---|---|---|
| Structure | Add concise definition, comparison table, FAQ, summary bullets, internal anchors, and clean headings. | browser/markdown shows extractable answer blocks. |
| Authority | Add original stats, dated claims, expert/customer quotes, primary-source citations, and author/review signals. | Page visibly cites sources and contains current, attributable evidence. |
| Presence | Get listed or genuinely mentioned on the surface AI already cites. | The third-party surface contains the brand with accurate positioning. |
| Organic-but-uncited | Reformat the ranking page instead of creating a new one. | seo/serp still ranks; geo/serp re-check shows citation movement or better source fit. |
Output
Return a prioritized plan:
# AI Visibility Fix Plan - {brand/domain} ({YYYY-MM-DD})
## Summary
- Highest-value prompt gap: ...
- Fastest win: ...
- Dominant miss cause: Structure | Authority | Presence
## Fix Plan
| Priority | Prompt | AI vol | Current owner | Cause | Build path | Action | Acceptance check |
| -------- | ---------------------------- | ------ | ------------- | -------- | ----------- | -------------------------------------------------------- | -------------------------------------- |
| Now | best {category} for startups | 1.9k | g2.com | Presence | Third-party | Earn accurate listing/reviews on the cited category page | Re-check top-pages and listing content |
## Page-Level Tasks
- Existing page updates: ...
- Net-new pages: ...
- Third-party presence targets: ...
## Re-check Plan
- Re-run the same prompt set and market after shipping.
- Compare cited-source slots, name-drops, and absences separately.
- Record UnifAPI cost from billing metadata.Guardrails
- Do not equate name-drops with citations. A name-drop is a quick-win signal, not a solved gap.
- Do not recommend fake reviews, spammy listicles, astroturfed communities, or manipulative third-party mentions.
- Do not create "AI-only" doorway pages. Fix the human page so it is clear, cited, and extractable.
- Do not overfit to one stochastic answer. Treat each result as a dated snapshot and re-check before large work.
Related Skills
- ai-visibility-audit: diagnose citation coverage and classify misses.
- ai-answer-gap: find and rank the prompt gaps this skill turns into fixes.
- llm-mention-tracking: monitor whether shipped fixes improve AI share of voice over time.
- seo-audit: cross-check organic ranking and page quality when a GEO miss is also an SEO issue.
- unifapi: the shared data skill (connect MCP, discover the GEO, SEO, and browser operations this skill reads).
© unifapi-agent, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 1 other file in skills/ai-visibility-agent/ai-visibility-fix-plan of unifapi-agent/agents.
- SKILL.md
- README.md
Open the folder on GitHubat commit fb53247
Compare with similar skills
AI Visibility Fix Plan next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| AI Visibility Fix Plan this skillunifapi-agent/agents | 589 | — | ~1.7k | Automated safety check: Pass | MIT | |
| Geo Fundamentalswasp-lang/wasp | 19k | 9 repos | ~861 | Automated safety check: Pass | MIT | |
| SEO GeoReScienceLab/opc-skills | 1.8k | 4 repos | ~2.1k | Automated safety check: Pass | Apache-2.0 | |
| GEO-First SEO Audit Toolzubair-trabzada/geo-seo-claude | 11k | — | ~2.8k | Automated safety check: Notes | MIT | |
| SEO DataforseoAgriciDaniel/codex-seo | 799 | 2 repos | ~4.6k | Automated safety check: Pass | MIT | |
| Fire Your SEO Agencyleopard627/fire-your-seo-agency | 711 | — | ~1.1k | Automated safety check: Pass | MIT |
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Related
Works with
Categories
Questions about AI Visibility Fix Plan
What does AI Visibility Fix Plan do?
When the user has AI visibility, GEO, AI answer gap, or AI citation evidence and wants a prioritized plan to improve where the brand is cited or mentioned in AI answers. AI Visibility Fix Plan is an agent skill from unifapi-agent/agents. When the user has AI visibility, GEO, AI answer gap, or AI citation evidence and wants a prioritized plan to improve where the brand is cited or mentioned in AI answers.
When should I use AI Visibility Fix Plan?
AI Visibility Fix Plan fits situations like: has AI visibility; AI citation evidence and wants a prioritized plan to improve where the brand is cited; mentioned in AI answers.
How do I install AI Visibility Fix Plan in Claude Code?
Run `npx skills add unifapi-agent/agents --skill ai-visibility-fix-plan -a claude-code`. Or copy the skill folder (skills/ai-visibility-agent/ai-visibility-fix-plan in unifapi-agent/agents) into .claude/skills/ai-visibility-fix-plan in your project. Claude Code loads it when a task matches its description.
How do I install AI Visibility Fix Plan in Codex?
Run `npx skills add unifapi-agent/agents --skill ai-visibility-fix-plan -a codex`. Or copy the skill folder (skills/ai-visibility-agent/ai-visibility-fix-plan in unifapi-agent/agents) into .agents/skills/ai-visibility-fix-plan in your project. Codex loads it when a task matches its description.
Can I use AI Visibility Fix Plan in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add unifapi-agent/agents --skill ai-visibility-fix-plan -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ai-visibility-fix-plan, .gemini/skills/ai-visibility-fix-plan, .github/skills/ai-visibility-fix-plan and .opencode/skills/ai-visibility-fix-plan in your project.
What does AI Visibility Fix Plan need to run?
SKILL.md names no scripts, command-line tools or credentials: AI Visibility Fix Plan is instructions for the agent only.
Does AI Visibility Fix Plan access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is AI Visibility Fix Plan safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does AI Visibility Fix Plan use?
AI Visibility Fix Plan is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does AI Visibility Fix Plan use?
About 1.7k tokens (SKILL.md is roughly 6.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to AI Visibility Fix Plan?
Skills that share tags, products or a category with AI Visibility Fix Plan: Geo Fundamentals (wasp-lang/wasp, 19k stars), SEO Geo (ReScienceLab/opc-skills, 1.8k stars), GEO-First SEO Audit Tool (zubair-trabzada/geo-seo-claude, 11k stars) and SEO Dataforseo (AgriciDaniel/codex-seo, 799 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains AI Visibility Fix Plan?
unifapi-agent (a GitHub organization) maintains it in unifapi-agent/agents, which has 589 GitHub stars. The repository holds 47 skills in this directory. The repository was last updated on September 5, 2026.
Source: unifapi-agent/agents on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.