# Fiverr Gig Optimizer

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- **Type:** Skill
- **Install:** `agentstack add skill-ahad690-fiverr-gig-optimizer-fiverr-gig-optimizer`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [Ahad690](https://agentstack.voostack.com/s/ahad690)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [Ahad690](https://github.com/Ahad690)
- **Source:** https://github.com/Ahad690/fiverr-gig-optimizer/tree/main/skills/fiverr-gig-optimizer

## Install

```sh
agentstack add skill-ahad690-fiverr-gig-optimizer-fiverr-gig-optimizer
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

## 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.
2. **Always run the scoring scripts** to produce competition/demand/opportunity
   and pricing. Surface their JSON fields verbatim.
3. **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.
4. **Offer design is allowed.** Delivery days, revisions, and included items are
   the seller's choices; keep them realistic and consistent with stated turnaround.
5. **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](https://github.com/Ahad690)
- **Source:** [Ahad690/fiverr-gig-optimizer](https://github.com/Ahad690/fiverr-gig-optimizer)
- **License:** MIT

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

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-ahad690-fiverr-gig-optimizer-fiverr-gig-optimizer
- Seller: https://agentstack.voostack.com/s/ahad690
- Browse the marketplace: https://agentstack.voostack.com/browse

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