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Agent skill

Homeowner Question Content

by unifapi-agent in unifapi-agent/agents

When a home-services business (HVAC, plumbing, roofing, electrical, or general contractor) wants to know the real problem and cost questions homeowners ask about its services, and turn them into a…

MITAuto-check passedMarketing & SEO

Install Homeowner Question Content

skills CLI
$ npx skills add unifapi-agent/agents --skill homeowner-question-content -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install unifapi-agent/agents homeowner-question-content --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/unifapi-agent/agents.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/home-services-marketing/homeowner-question-content .claude/skills/homeowner-question-content && rm -rf skills-src

Use ~/.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/

Facts

Skill name
homeowner-question-content
GitHub stars
589
Token cost
~2.5k tokens
SKILL.md length
1,097 words
Files
2
Skills in repo
47
Repo updated
First seen
Licence
MIT

At a glance

When a home-services business (HVAC, plumbing, roofing, electrical, or general contractor) wants to know the real problem and cost questions homeowners ask about its services, and turn them into a…

  • Works in 5 steps: Start from the service menu. Take the… → Mine search demand. For each service,… → Mine community + AI + seasonal… → …
  • Tasks that involve Content strategy
  • SKILL.md covers Use UnifAPI for live evidence, Workflow, Scoring rubric and Output: Homeowner Question…, plus 2 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Homeowner Question Content is an agent skill from unifapi-agent/agents. When a home-services business (HVAC, plumbing, roofing, electrical, or general contractor) wants to know the real problem and cost questions homeowners ask about its services, and turn them into a prioritized content plan. Also use on "what do homeowners search," "home services content ideas," "cost-to-replace content," "why is my ac freezing content," "trade SEO content plan," "questions homeowners ask," or "are we showing up in AI answers for repair questions." Reads public data only — read-only marketing…

Its SKILL.md is about 2.5k 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 Content strategy and On-page SEO. It works with Reddit. 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

  • Tasks that involve Content strategy
  • Tasks that involve On-page SEO

Example prompts

  • “what do homeowners search,”
  • “home services content ideas,”
  • “cost-to-replace content,”
  • “/homeowner-question-content”

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Start from the service menu. Take the trade's services (ac repair, furnace install, water heater, drain, roof repair, panel upgrade, …)…
  2. Mine search demand. For each service, expand problem + "cost to" queries with seo/keywords/ideas + seo/keywords/related +…
  3. Mine community + AI + seasonal questions. Find Reddit threads via seo/serp site:reddit.com → reddit/posts/{id}/comments; run problem/cost…
  4. Cluster into topics. Group raw questions into problem topics (diagnose / DIY-curious) and cost/decision topics (replace vs repair, what it…
  5. Score each cluster with the rubric below (with the seasonal multiplier) and sort, then build the plan: the exact homeowner phrasing to…

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

Homeowner Question Content loads about 2.5k tokens when it runs. Until then it costs about 137 tokens; SKILL.md has 1,097 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~137
When it runs · the whole SKILL.md, loaded when a task matches
~2.5k

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). 1,097 words, ~2,487 tokens.

Download SKILL.mdSave it as .claude/skills/homeowner-question-content/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
homeowner-question-content
description
When a home-services business (HVAC, plumbing, roofing, electrical, or general contractor) wants to know the real problem and cost questions homeowners ask about its services, and turn them into a prioritized content plan. Also use on "what do homeowners search," "home services content ideas," "cost-to-replace content," "why is my ac freezing content," "trade SEO content plan," "questions homeowners ask," or "are we showing up in AI answers for repair questions." Reads public data only — read-only marketing research.
license
MIT
metadata.author
UnifAPI
metadata.version
1.0.0

Homeowner Question Content

You are a home-services marketing researcher. Homeowners don't search in marketing language — they search the problem ("why is my ac freezing up", "water heater leaking from bottom") and the money question ("cost to replace a furnace", "how much does a new roof cost"). Those questions are the highest-intent, lowest-cost content a contractor can own, and they feed both Google and the AI assistants homeowners now ask. This skill mines the real questions across search demand, communities, AI prompts, and seasonal news, scores them with a seasonal-timing multiplier, and ranks them into a content plan tied to the services the business offers.

This is an enhanced skill: it reads live public data through UnifAPI.

Use UnifAPI for live evidence

Every topic is anchored to a public source that proves homeowners are actually asking — and the same question echoed across search and a community and a seasonal-news spike is a far stronger bet than a single loud one. Use the unifapi skill to connect (OAuth MCP), then call:

  • Search demand + intent — seo/keywords/ideas, seo/keywords/related, seo/keywords/suggestions (expand each service into the real problem questions and "cost to [verb]" queries homeowners type, with autocomplete/related variants), seo/keywords/intent (classify each query so "cost to replace" / "near me" buyer intent is a read signal, not a guess).
  • AI-answer prompts — geo/serp (run "best [trade] near me" and problem/cost questions as AI-Mode prompts; capture the answer, cited sources, and the is_target flag for whether the business is named), geo/keywords/search-volume (AI search volume per prompt, so unclaimed prompts rank by demand).
  • Community questions — Reddit has no keyword search, so find threads via seo/serp for site:reddit.com <problem> (e.g. site:reddit.com cost to replace furnace, communities like r/hvacadvice, r/homeowners), then open each with reddit/posts/{id}/comments to read how homeowners phrase the problem in their own words and the upvote/comment volume as a demand signal.
  • News + seasonal hooks — news/search (events that spike a question — heat waves, cold snaps, storms, freeze warnings — so seasonal content publishes before demand, not during it; capture dates).

UnifAPI reads public data only. Keep any billing metadata so the report can state record cost.

Workflow

  1. Start from the service menu. Take the trade's services (ac repair, furnace install, water heater, drain, roof repair, panel upgrade, …) and the service areas. Read .agents/product-marketing.md / .claude/product-marketing.md first if it exists.
  2. Mine search demand. For each service, expand problem + "cost to" queries with seo/keywords/ideas + seo/keywords/related + seo/keywords/suggestions, then tag each with seo/keywords/intent. Log source, source URL, verbatim phrasing, raw volume, the date, and the season it peaks (if any).
  3. Mine community + AI + seasonal questions. Find Reddit threads via seo/serp site:reddit.com <problem> → reddit/posts/{id}/comments; run problem/cost prompts through geo/serp for citation gaps; pull news/search for the spikes that set publish windows.
  4. Cluster into topics. Group raw questions into problem topics (diagnose / DIY-curious) and cost/decision topics (replace vs repair, what it costs) — the two patterns that convert for home services. Merge near-duplicate phrasings and keep every source URL on the cluster.
  5. Score each cluster with the rubric below (with the seasonal multiplier) and sort, then build the plan: the exact homeowner phrasing to answer, the service it routes to, localize-or-evergreen guidance, the publish window if seasonal, and the AI prompts worth optimizing for.

Scoring rubric

Score each cluster 1–5 on three axes, then apply a seasonal-timing multiplier. Intent is weighted because cost and emergency questions convert into calls; pure trivia does not.

AxisWhat it measures135
DemandVolume (overview) / repetition across sourcesthin, single sourcemoderate, steady, 2 sourceshigh volume or echoed across 3+ sources
WinnabilityHow beatable the current results are (seo/geo/serp)strong fresh comprehensive pagesmixed; some thin/datedthin, generic, off-topic, or no local owner
IntentHow close to hiring (seo/keywords/intent)trivia / pure curiosityproblem diagnosiscost / emergency / "near me" (calls)

Base Score = Demand × (Winnability + Intent). Range 2–50. Then a seasonal multiplier read off news/search spikes: a topic whose demand spikes seasonally (furnace in fall, AC in early summer, storm/roof ahead of storm season) gets ×1.25 if the publish window is still ahead (so it ranks before the spike), and ×0.8 if the window has just passed (publish next cycle). Evergreen topics stay ×1.0. Final Score = Base × seasonal_multiplier. This pushes "cost to replace a furnace" up in late summer (build before fall demand) and down in spring.

Drop clusters scoring Demand = 1 AND Intent ≤ 2 (a one-off low-intent question) and note them as discarded.

Show full SKILL.md (370 more words)Show less

Output: Homeowner Question Content Plan

A content-plan table sorted by Final Score, split into problem topics and cost/decision topics. State the service areas, audit date, and sources checked so the run is reproducible.

markdown
# Homeowner Question Content Plan — [Business], [Areas] — [date]

| #   | Topic (homeowner phrasing)  | Type          | Service         | Demand | Win | Intent | Season (window)     | Final Score       | Local/evergreen   | Proving source(s)                  |
| --- | --------------------------- | ------------- | --------------- | ------ | --- | ------ | ------------------- | ----------------- | ----------------- | ---------------------------------- |
| 1   | "cost to replace a furnace" | cost/decision | furnace install | 5      | 4   | 5      | fall (build by Aug) | 56 (5×(4+5)×1.25) | localize per area | SEO 8k/mo; r/hvacadvice 12 threads |

## Per top topic

The real homeowner phrasing to answer, the service it maps to, localize-or-evergreen guidance, the publish window if seasonal, and the proving source(s) incl. Reddit thread URLs.

## AI-answer prompts

Prompts (from geo/serp) the business should be cited for but isn't.

## Discarded

One line per cluster checked and rejected, with why.
Worked example (abbreviated)

An HVAC contractor, audited in August. "Cost to replace a furnace" — seo/keywords/overview ~8k/mo, echoed via site:reddit.com → reddit/posts/{id}/comments in r/hvacadvice and r/homeowners (Demand 5), top seo/serp results are dated national calculators with no local owner (Win 4), seo/keywords/intent commercial — pure cost/hire intent (Intent 5). Base = 5 × (4 + 5) = 45; news/search shows demand spikes in fall and the publish window is still ahead → ×1.25 → Final 56, rank #1, localize per service area. "Why is my AC freezing up" scored Demand 4 / Win 3 / Intent 3 = 28, but it's early-summer-seasonal and the window just passed → ×0.8 → 22.4, deferred to next spring. geo/serp for "furnace replacement cost in [city]" returns no local citation — target the new localized page at it. A "history of central heating" trivia cluster (Demand 1, Intent 1) was discarded.

Guardrails

  • Marketing research only, and read-only ("eyes, not hands"). It surfaces demand and content angles; it does not make repair, code, safety, or pricing claims — those belong to the licensed trade, and any cost figures published must be the business's own, not invented by the skill.
  • It plans content; it never publishes, runs ads, or manages listings. The operator's own team and assistant publish, within platform rules.
  • Confirmed vs inferred: label what's read off a source (volume, intent class, a verbatim Reddit question, a news date) versus what's deduced (winnability, the seasonal-window call).
  • Demand and seasonal signals are public-data estimates — present ranges and dates, weight recency, and treat AI/community signals as directional, not guaranteed traffic. Community sources skew toward power users; weight by cross-source overlap, not a single loud thread.
  • Every recommended topic must cite the public source that proves homeowners are asking. No source, no recommendation — and no fabricated volumes or quotes.
  • service-area-rank-audit (Home Services Marketing): the local-rank side — find which service areas are weak, then localize this content there.
  • content-opportunity-brief (Content Strategy Agent): the general-purpose demand-to-ranked-topics workflow this trade-specific plan is built on.
  • unifapi: the shared data skill — connect MCP and discover the SEO / GEO / Reddit / news 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/home-services-marketing/homeowner-question-content of unifapi-agent/agents.

  • SKILL.md
  • README.md

Open the folder on GitHubat commit fb53247

Compare with similar skills

Homeowner Question Content 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.

Homeowner Question Content compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Homeowner Question Content this skillunifapi-agent/agents589—~2.5kAutomated safety check: PassMIT
Content Brief Factorygooseworks-ai/goose-skills1.2k1 repos~3.1kAutomated safety check: PassMIT
SEO Keyword ClusteringAgriciDaniel/claude-seo19k2 repos~3.3kAutomated safety check: PassMIT
SEO Content Brief GeneratorAgriciDaniel/claude-seo19k2 repos~2.6kAutomated safety check: PassMIT
E2E SEO Assistantirinabuht12-oss/marketing-skills4.1k—~1.9kAutomated safety check: PassNone
SEO Content Briefseranking/seo-skills161—~2.5kAutomated safety check: PassMIT

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Works with

Categories

Questions about Homeowner Question Content

What does Homeowner Question Content do?

When a home-services business (HVAC, plumbing, roofing, electrical, or general contractor) wants to know the real problem and cost questions homeowners ask about its services, and turn them into a…. Homeowner Question Content is an agent skill from unifapi-agent/agents. When a home-services business (HVAC, plumbing, roofing, electrical, or general contractor) wants to know the real problem and cost questions homeowners ask about its services, and turn them into a prioritized content plan.

When should I use Homeowner Question Content?

Homeowner Question Content fits situations like: tasks that involve Content strategy; tasks that involve On-page SEO.

How do I install Homeowner Question Content in Claude Code?

Run `npx skills add unifapi-agent/agents --skill homeowner-question-content -a claude-code`. Or copy the skill folder (skills/home-services-marketing/homeowner-question-content in unifapi-agent/agents) into .claude/skills/homeowner-question-content in your project. Claude Code loads it when a task matches its description.

How do I install Homeowner Question Content in Codex?

Run `npx skills add unifapi-agent/agents --skill homeowner-question-content -a codex`. Or copy the skill folder (skills/home-services-marketing/homeowner-question-content in unifapi-agent/agents) into .agents/skills/homeowner-question-content in your project. Codex loads it when a task matches its description.

Can I use Homeowner Question Content 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 homeowner-question-content -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/homeowner-question-content, .gemini/skills/homeowner-question-content, .github/skills/homeowner-question-content and .opencode/skills/homeowner-question-content in your project.

What does Homeowner Question Content need to run?

SKILL.md names no scripts, command-line tools or credentials: Homeowner Question Content is instructions for the agent only.

Does Homeowner Question Content 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 Homeowner Question Content 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 Homeowner Question Content use?

Homeowner Question Content 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 Homeowner Question Content use?

About 2.5k tokens (SKILL.md is roughly 9.9k 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 Homeowner Question Content?

Skills that share tags, products or a category with Homeowner Question Content: Content Brief Factory (gooseworks-ai/goose-skills, 1.2k stars), SEO Keyword Clustering (AgriciDaniel/claude-seo, 19k stars), SEO Content Brief Generator (AgriciDaniel/claude-seo, 19k stars) and E2E SEO Assistant (irinabuht12-oss/marketing-skills, 4.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Homeowner Question Content?

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.