Fiverr Gig Optimizer

Overview

This skill turns a freelancer's services into an optimized Fiverr gig catalog. Every market number — competition counts, demand, competitor prices — comes from a Python script operating on real input data, never from the model. Offer-design fields (delivery days, revisions, package contents) are the seller's own choices and may be authored, kept realistic. Scores and prices are computed by the scripts below; you surface their JSON output verbatim.

When to use

Optimize a Fiverr gig; research a Fiverr keyword; check gig competition; price a gig or its tiers; plan a Fiverr launch; build a gig catalog from a service list.

Hard rules

1. Never invent a market number. Competition counts, demand scores, and

competitor prices come only from score_keyword.py, query_dataset.py, and analyze_pricing.py. Do not invent, round, relabel, or recall from memory any of them.

1. Always run the scoring scripts to produce competition/demand/opportunity

and pricing. Surface their JSON fields verbatim.

1. If market data is missing, ask or say "I don't have that." When a dataset

lookup returns no_match, ask the user to paste the Fiverr count — never fabricate one.

1. Offer design is allowed. Delivery days, revisions, and included items are

the seller's choices; keep them realistic and consistent with stated turnaround.

1. Strip PII before any contribution (see contribute.py / DATA_POLICY.md).

Run scripts as: python3 ${CLAUDE_SKILL_DIR}/scripts/.py ...

Workflow

Step 1 — Gather (FR1/FR2). Ask in ONE numbered message: name; brand (optional); website (optional); services list; headshot path (optional); existing gig URLs or "none"; monthly revenue goal; experience level (New / L1 / L2 / Top Rated). If fewer than 3 services, ask for more.

Step 1b — Optional profile import. If the user gives their Fiverr profile URL, run import_profile.py --url "" to pre-fill name, seller level, and their existing gigs with current prices/tags (public data only — no private analytics). Use existing_gigs to optimize what they already have and suggested_services as keyword seeds; still ask for the revenue goal and any new services. Skip if no link is given — never require it.

Step 2 — Generate keyword candidates (FR6a). From the services, propose single-service keywords and 2-way (sometimes 3-way) combos, following references/fiverr-seo-playbook.md. These are search candidates, not measurements — generating them is allowed. They get scored next.

Step 3 — Acquire data (pick a path).

• Path A (default, free): for each candidate run

query_dataset.py --keyword "". Use the returned gig_count, top_gigs, and match_confidence. On flags:["no_match"] → go to Path B for that keyword. Write top_gigs to a temp file to feed Step 4.

• Path B (manual): ask the user to paste the "X services available" count

Fiverr shows for the keyword. Never fabricate it.

• Path C (live scrape, opt-in): run

scrape.py --query "" [--category ""] [--limit N]. The primary engine (vendored Perseus reader) needs no key and returns the real search total as gig_count_in_search; on a non-residential IP set PROXY_URL. With an Apify key it can fall back to the configured actor (which cannot supply the search total — count then comes from a manual paste). Then run build_benchmarks.py to build the pools + index. Live scraping is the user's responsibility under Fiverr's ToS.

Step 4 — Score (FR7/FR8). For each keyword: score_keyword.py --keyword "" --gig-count [--top-gigs top.json]. Surface competition_score, tier, demand_score, opportunity_score, flags verbatim.

Step 5 — Price (FR9/FR10). Build per-tier price lists for the category (from pricing-pools.local.json via build_benchmarks.py, or from top_gigs), then analyze_pricing.py --prices pools.json --category "" --experience . Use the recommended triple; respect low-confidence flags.

Step 6 — Assemble gig-config.json (FR11–FR13). Using the computed tiers, scores, and prices + the playbook + references/categories.json: write titles/tags/descriptions (lint rules in the playbook), pick phases and cross-sells, set per-gig thumbnail accent from the palette. The competition, scores, and pricing blocks are the script outputs — do not alter them.

Also author img.ai_prompt for each gig — a rich prompt for an AI image model (ChatGPT/DALL·E/Midjourney), offered in the catalog as an alternative to the canvas PNG. Thumbnail design is offer design, so be creative here: describe a distinctive scene, composition, or visual metaphor suited to the service (an isometric workflow, a stylized robot at a desk, glowing pipeline nodes…) — not just flat text on a gradient. Requirements: specify 1280×769 landscape; quote the exact headline/badge strings that must appear and say "spelled exactly as written"; keep it consistent with the gig's accent color; forbid watermarks, logos, and any unquoted text. Never put claims in the image that aren't true of the seller (no "Top Rated", review counts, or client numbers unless supplied). If you skip ai_prompt, the catalog falls back to a deterministic prompt mirroring the canvas design.

Step 7 — Render (FR14/FR15). build_catalog.py gig-config.json → fiverr-catalog.html. Optionally build_pdfs.py gig-config.json for per-gig PDFs (skips if no Chrome). After a live scrape, offer contribution (contribute.py, default no).

Output format

• Present each keyword's scores exactly as the script returned them, with a

provenance line, e.g.: Competition for "ai chatbot n8n": sample-data match, confidence HIGH, dataset generated 2025-12 · score 82 (LOW) · demand n/a · opportunity 82.

• State where files were written (fiverr-catalog.html, any PDFs).
• For low-confidence pricing or low match confidence, say so plainly.

Error handling

• No scraper key → offer Path A (sample) or Path B (manual).
• query_dataset.py no_match / LOW confidence → ask for a manual count.
• Pricing tier confidence:"low" → present the number but flag it; for n=0,

recommend nothing for that tier.

• No Chrome/Edge → build_pdfs.py warns and exits 0; the HTML still renders.

Examples

(a) Full run on sample data (MEDIUM/HIGH match). User lists "n8n automation, AI chatbots, OpenAI integrations". You generate combos, run query_dataset.py ("ai chatbot n8n" → gig_count 1243, confidence HIGH), score it (82, LOW), analyze pricing from top_gigs, assemble gig-config.json, render the catalog, and report each number with its provenance line.

(b) No match → ask, don't guess. A niche combo returns flags:["no_match"]. You reply: "I don't have data for that keyword in the sample set. Open Fiverr, search it, and paste the 'X services available' count so I can score it." You do not state any competition number.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: Ahad690
• Source: Ahad690/fiverr-gig-optimizer
• License: MIT

Install and usage instructions live in the source repository linked above.