ads-audit
Full multi-platform paid advertising audit with parallel subagent delegation. Analyzes Google Ads, Meta Ads, LinkedIn Ads, TikTok Ads, and Microsoft Ads…
Asks a few plain questions about the work, then names the one desktop app to run local AI models in, from LM Studio, Goose, Open WebUI, AnythingLLM, OpenWork or Odysseus. Use when the user asks "which app should I use for local AI", "LM Studio or Ollama", "what is the best local
$ npx -y skills add naveedharri/benai-skills --skill pick-my-harness --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/pick-my-harnessContext preview
The summary Claude sees to decide when to auto-load this skill.
Asks a few plain questions about the work, then names the one desktop app to run local AI models in, from LM Studio, Goose, Open WebUI, AnythingLLM, OpenWork or Odysseus. Use when the user asks "which app should I use for local AI", "LM Studio or Ollama", "what is the best local
name: pick-my-harness description: Asks a few plain questions about the work, then names the one desktop app to run local AI models in, from LM Studio, Goose, Open WebUI, AnythingLLM, OpenWork or Odysseus. Use when the user asks "which app should I use for local AI", "LM Studio or Ollama", "what is the best local AI interface", "do I need Open WebUI", "Open WebUI or Odysseus", "which one supports MCP", "which supports skills", "pick a harness for me", or is comparing local AI desktop apps. Names one tool and states what it cannot do. Never installs anything. Requires Claude Code with shell access on the user's own machine; refuses to run in a sandbox. disable-model-invocation: true
Turns "which app should I use" into one named recommendation. A model is only half a local AI setup; this picks the app that gives it skills, MCP servers and your files.
Run the check in `references/environment-check.md` first. These skills need a shell on the user's own machine. If the environment is a sandbox or container, stop and tell the user to run this in Claude Code on the computer they want to set up. Do not report detected specs from a sandbox: wrong specs are worse than none.
Track progress:
Task Progress: - [ ] 1. Ask the four questions - [ ] 2. Match against the matrix - [ ] 3. Name one tool and its limits - [ ] 4. Render the HTML report
Ask all four at once, as a short numbered list, in plain language. Never ask about VRAM here.
1. What is the main job: chatting with documents, writing code, general chat, or running automated tasks? 2. Is this just for you, or for a team who all need access? 3. How comfortable are you editing a config file if it unlocks more control? (happy / rather not) 4. Does everything need to stay on your machine with no cloud fallback?
If the user already answered some in their request, do not re-ask. Use what they gave.
Apply the decision rules in `references/harness-matrix.md`. That file holds the six tools, their real capability flags, and the tie-breakers.
Output exactly one recommendation, never a shortlist. Give: the tool, one sentence on why it won for their answers, its three capability flags, and one line on what it cannot do. Then give the single command or download link to get it.
If their answers make a second tool genuinely necessary alongside the first (most commonly LM Studio underneath something else), say so explicitly as a pair rather than presenting a choice.
Deliver the recommendation as a rendered HTML page, not as chat text. Build it from `references/report-template.md` using the `pick-my-harness` layout in section 4, save it to the Desktop, and open it. Keep the chat reply to two lines plus the file path.
Stop after step 3 and ask whether to proceed with installing it. Do not install anything from this skill. If they say yes, route to `/local-ai-setup`, or to `/install-openwebui` when Open WebUI is the pick. Open WebUI and Odysseus are the two browser harnesses and `/local-ai-setup` asks which one before installing, so route there rather than deciding for them.
This skill is never finished. Improve it as you use it.
| Step | Reference | |------|-----------| | before all steps | `references/environment-check.md` | | 2 | `references/harness-matrix.md` | | 4 | `references/report-template.md` |
Expert automation skills for Claude Code, organized by department.
Repo: naveedharri/benai-skills
Full multi-platform paid advertising audit with parallel subagent delegation. Analyzes Google Ads, Meta Ads, LinkedIn Ads, TikTok Ads, and Microsoft Ads…
Budget allocation and bidding strategy review across all ad platforms. Evaluates spend distribution, bidding strategy appropriateness, scaling readiness, and…
Competitor ad intelligence analysis across Google, Meta, LinkedIn, TikTok, and Microsoft. Analyzes competitor ad copy, creative strategy, keyword targeting,…
Cross-platform creative quality audit covering ad copy, video, image, and format diversity across all platforms. Detects creative fatigue, evaluates…
Google Ads deep analysis covering Search, Performance Max, Display, YouTube, and Demand Gen campaigns. Evaluates 74 checks across conversion tracking, wasted…
Landing page quality assessment for paid advertising campaigns. Evaluates message match, page speed, mobile experience, trust signals, form optimization, and…