An AI skill is a structured system prompt or set of custom instructions that gives AI assistants like ChatGPT, Claude, and Gemini specialized capabilities. It includes a clear role definition, specific knowledge areas, step-by-step processes, and quality criteria that help AI perform specific professional tasks.

How does Skill This work?

Simply describe your professional expertise in plain text. Our free AI skill generator analyzes your input and transforms it into a structured system prompt. The skill is then graded on clarity, specificity, and usefulness. You can share your skill via a unique URL or copy it to use as custom instructions with any AI assistant like ChatGPT, Claude, or Gemini.

Is Skill This free to use?

Yes, Skill This is completely free. No account or payment is required. You can create unlimited skills and share them with anyone.

How is my skill graded?

Skills are graded on a scale from A+ to F (0-100 points). The grade reflects how clear, specific, and actionable your skill is. Higher grades indicate skills that AI assistants can use more effectively.

How do I use my skill with an AI assistant?

Copy your generated skill and paste it into the system prompt or custom instructions of any AI assistant. For ChatGPT, use Personalization > Custom Instructions. For Claude, add it to a Project's instructions. For Cursor or other IDEs, save it as a .cursorrules or CLAUDE.md file. The AI will then adopt that skill and perform tasks according to your expertise.

AI Skill Report Card

Building Local Market Intelligence Briefs

A-88·Sep 29, 2026·Source: Extension-selection

Quick Start14 / 15

Core loop, no code required: Raw business list → Spreadsheet math → AI visibility audit → AI synthesis → PDF.

Pull a business list for a niche+city from a free source (library database, Google Maps, public directory).
Compute per-zip-code competitor density, workforce capacity, and a review gap in a spreadsheet.
Ask a web-enabled AI ("Gemini"/similar) to check whether the target business appears in conversational search results for that niche+city.
Feed both outputs back into the AI with a single synthesis prompt to generate a 4-page report.
Paste into a design tool (Canva) template and export as PDF.

Sell the PDF at $99–$349 depending on depth. Marginal cost per unit: ~$0.

Recommendation▾

Add a concrete before/after snippet of the actual synthesis prompt text used in Step 5 to make the workflow copy-pasteable rather than descriptive.

Workflow15 / 15

Progress:

Step 1: Source raw data (business list with name, address, zip, employee count, review count)
Step 2: Compute the Arbitrage Matrix (density, capacity, review gap, revenue-at-stake)
Step 3: Run the AI visibility audit (is the target business recommended by conversational AI?)
Step 4: Bridge/fuse Step 2 + Step 3 data explicitly before final synthesis
Step 5: Generate the 4-page report via a single synthesis prompt
Step 6: Human fact-check pass (verify every number traces back to source data)
Step 7: Package into a template and export

Step 1: Source Raw Data

Pull a list of businesses in one niche + one city. Minimum viable fields: company name, zip code, employee count (or size proxy), review count, star rating. Sources: library-provided business databases, manual Google Maps scrolling, any CSV export. If no employee count exists, default missing values to 1 to avoid divide-by-zero errors downstream.

Step 2: The Arbitrage Matrix

In a spreadsheet or via a decomposition prompt, group by zip code and compute:

Competitor Density: count of businesses per zip
Labor Capacity: sum of employee counts per zip
Arbitrage Index (0–100): normalized inverse ratio of density to capacity — high score = many small, under-resourced competitors = market vulnerability
Review Gap: MAX(reviews in market) - AVG(reviews in this zone)
Revenue at Stake: review_gap × assumed_customer_LTV (default: $85, adjust per industry)

Decompose this as its own isolated prompt/pass — do not let the model draft strategy prose yet. Force it to output only a clean table. This prevents hallucinated narrative from contaminating the numbers.

Step 3: AI Visibility Audit

Run a live web-search-enabled query asking an AI model: "who are the best [niche] in [city]?" Capture:

Top 3 companies actually recommended
Which directories/citations the model is pulling from (Yelp, Google Maps, Angi, etc.)
Whether the target business appears — return an explicit binary status: RECOMMENDED or NOT MENTIONED

This is the emotional hook of the entire product: proving invisibility to modern AI-driven discovery, not just weak SEO.

Step 4: The Fusion Bridge (critical, often skipped)

Do not paste Step 2 and Step 3 outputs directly into a final generation prompt and hope the model reconciles them. Use an explicit intermediate prompt that:

Labels each data block clearly (e.g., "DATA LAYER alpha" / "DATA LAYER beta")
Forces the model to manually compute any cross-referenced math (e.g., review gap using the live competitor benchmark, not the stale table figure)
Requires a short confirmation response before generating final copy — this catches misalignment early and cheaply

Step 5: Synthesis Prompt (single master pass)

Feed the fused data into one final prompt producing a 4-page structure:

Title/Framing — why the old way of measuring visibility (SEO rankings) is insufficient now
Visibility Dashboard — the NOT MENTIONED status, named rivals stealing the traffic, citation sources
Data Table — the arbitrage matrix with revenue-at-stake column
90-Day Roadmap — one paragraph per top opportunity zone + one timeline summary (30/60/90-day milestones)

Include a self-reflection instruction inside the same prompt: have the model check its own output for internal consistency (do the dollar figures on page 3 match the claims on page 2 and page 4?) before presenting final text.

Step 6: Human Fact-Check

Read every number. Cross-reference every dollar figure, competitor name, and zip code against your source data. This is the one step that cannot be automated — it's what makes the product defensible and non-hallucinated.

Step 7: Package

Paste validated markdown into a 4-page template (title / dashboard / data table / roadmap). Export as PDF. Name file [City]-[Niche]-[ProductName].pdf.

Recommendation▾

Example 2 is thinner than Example 1 — flesh out with a concrete output snippet rather than a meta-description of applying the same steps.

Examples16 / 20

Example 1: Input: Raw list of 12 commercial roofing companies in San Antonio with zip, employee count, review count. Target business: "Alamo Roofing & Construction" (78209, 38 reviews). Output: Arbitrage matrix ranking 78207 as highest opportunity zone (Index 100.0, only 7 employees, 11 avg reviews). Visibility audit returns NOT MENTIONED for the target, with Presidio/Dolan/Premier Roofing dominating AI recommendations. Fusion step computes review gap (462) and revenue-at-stake ($39,270/mo) for 78207. Final 4-page brief names specific 90-day tactics (infrared moisture audits, review-velocity SMS triggers) tied to the exact numbers.

Example 2: Input: A vague request to "build a lead-gen product for HVAC companies." Output: Clarify niche + city first (the workflow only works with a bounded geography and category), then apply the same 7-step sequence substituting HVAC-specific review benchmarks and a facility-manager-oriented pitch angle.

Recommendation▾

Consider adding a short troubleshooting example showing a bad AI output (e.g., hallucinated numbers) versus the corrected fused version to reinforce the fusion-step rationale.

Best Practices

Bound the scope before starting: always lock a specific city + specific niche + specific target business name before running any prompts. Generic requests produce generic (unsellable) output.
Isolate math from narrative: run the arbitrage calculation as its own step, separate from strategy writing, so the model doesn't blend fabricated numbers into confident-sounding prose.
Use an explicit LTV/assumption constant (e.g., $85 per customer) and state it visibly in the report — it makes the revenue-at-stake number feel calculated rather than invented, and lets you swap it per vertical.
Always run a confirmation/fusion step between data-gathering prompts and the final generation prompt in any multi-turn workflow — context and numbers silently drift or get conflated otherwise.
Price by depth, not effort: a 30-minute version and a 120-minute version of the same workflow can be priced very differently ($99 vs $349+) based on how many verification/historical-context passes are added, not how long it literally takes.
Keep the human in the fact-check seat — this is the trust layer that separates a sellable diagnostic from AI slop.

Common Pitfalls

Skipping the fusion/bridge step: pasting two disconnected AI outputs into one final prompt often causes the model to average, invent, or misattribute numbers between them.
Forgetting to lock a $LTV or benchmark constant before generating financial claims — results in inconsistent dollar figures across report sections.
Not defaulting missing employee/review counts to a safe non-zero value, causing broken ratio math.
Treating the AI visibility check as a one-time fact — competitive rankings and citations shift; if reselling this as a subscription or recurring service, re-run the audit step regularly rather than reusing stale results.
Over-trusting AI-generated financial figures without cross-checking them against the original spreadsheet — always do the human audit pass before packaging.
One-shotting complex reports: trying to get the full 4-page brief in a single prompt without the decomposition/audit/fusion steps first produces generic, unverifiable filler text.