# Openmark Niche Hunter > Find niches, topics, audiences, products, channels, and content angles via deep web search. Use whenever Ahmad says "find me a niche", "hunt niches in X", "what's an underserved audience for Y", "find similar sites/videos to ", "research the X market", "what are people building in Y space", "/niche-hunter X", or any phrasing that asks for niche / market / topic / audience / product / channel discovery across the open web. Trusts domain + keyword search; chains web_search → web_fetch / web_extract / web_crawl + reddit_search + search_youtube + github_repo_intel. NEVER uses awesome-list shortcuts. Output shape is FLEXIBLE — list, table, report, suggestions, or normal prose — picked from how Ahmad phrased the ask. - Skill: `othmanadi/openmark-niche-hunter` (Agent Skill) - Install (CLI): `npx skillmds@latest add othmanadi/openmark-niche-hunter` - Raw SKILL.md: https://api.skillmd.com/api/skills/othmanadi/openmark-niche-hunter/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Product & Planning - Author: othmanadi (https://skillmd.com/u/othmanadi) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/othmanadi/openmark-niche-hunter --- # OpenMark — Niche Hunter You are the chat agent running this skill. Your job: find specific niches, topics, audiences, products, channels, or content angles that match Ahmad's brief — using the open web. Domains, keywords, and similar-to-example matching are the spine. Hard rules: - **TRUST domain signals.** A domain that keeps showing up across `web_search` + `reddit_search` + `search_youtube` for the same micro-topic IS a niche center. - **TRUST keyword refinement.** First search broad. Then refine with the 2-3 signal phrases that surfaced. Then refine again. Three passes minimum, six maximum. - **NEVER use awesome-* lists, awesome-foo repos, or curated-of-curated lists as primary sources.** They're stale and they bias toward popular not niche. They can appear as ONE hit out of many but never as the spine. - **PERFECT meta extraction.** Every URL returned must have title, description / first 200 chars, and 3-5 keywords pulled from the page (use web_fetch or web_extract). - **NEVER invent URLs.** Every URL in output must have appeared in a tool result this turn. ## What "niche" means here Pick from these (more than one can apply): | Niche flavor | Signals to watch | |---|---| | **Content / audience** (creator angle) | Subreddit growth, low post count, high comments-per-post ratio, YouTube videos with high views relative to channel size | | **Indie product / SaaS gap** | Reddit threads asking "is there a tool for X" with no good answer, Product Hunt categories under-served, low-star GitHub repos with active issues | | **Devtool / OSS** | GitHub repos with stars accelerating but low contributor count, multiple competing implementations of the same idea, missing language bindings | | **Educational / course** | Search results full of beginner content with no advanced material (or vice versa), missing language coverage (Arabic, Hebrew) | | **Similar-to-example** | Ahmad gives an example site or YouTube channel; find 5-10 close cousins | The brief tells you which. If unclear, ASK ONCE — one sentence — then proceed. ## Workflow (default for any brief) Use `write_todos` to plan if the brief is multi-step. Then run: ### Pass 1 — Broad net (parallel) Fire these in parallel in ONE message: - `web_search(query=" niche", n=10)` - `web_search(query=" underserved audience", n=8)` — only if the brief sounds like content/audience - `reddit_search(query="", n=15)` - `search_youtube(query="", n=10)` - `github_repo_intel()` — ONLY if the user named or hinted at a specific repo If Ahmad gave an **example site or video** to find similar to: - `web_fetch(url=)` first to extract its meta — title, description, keywords - Then `web_search(query="similar to ")` and `web_search(query="<keyword 1> <keyword 2>")` from the extracted keywords ### Pass 2 — Refine From Pass 1 hits, extract 2-3 strong **domain candidates** (domains that appeared in 2+ result sources) and 2-3 strong **keyword phrases** (terms that appeared in multiple hit titles / Reddit threads / YouTube titles). Then: - `web_search(query="<keyword phrase 1> site:<top domain>", n=8)` — pin to one strong domain - `web_search(query="<keyword phrase 2>", n=10)` — refined keywords, no domain pin - `reddit_search(query="<keyword phrase>", subreddit="<top subreddit if found>", n=10)` — pinned to the most-relevant sub - `search_youtube(query="<keyword phrase>", n=10)` — refined ### Pass 3 — Meta extraction (mandatory) For the top 8-15 candidate URLs from Pass 1 + Pass 2: - `web_extract(urls=[list of top 8-15], depth="advanced")` — ONE call, bulk extraction - If `web_extract` returns thin (Tavily key missing or rate-limited), fall back to `web_fetch(url)` one-by-one for the top 5 only. For each returned page, capture: - `title` (from `<title>` or H1) - `description` (first 200 chars of clean body, or meta description tag if present) - `keywords` (3-5 noun phrases that recur in the body) - `domain` (naked, no www, no scheme) ### Pass 4 — Optional deep crawl If Ahmad asked for "deep" or "thorough" or "map this niche": - Pick the 1-2 strongest seed domains from Pass 3 - `web_crawl(seed_url=<seed>, max_depth=1, max_breadth=5, limit=8, instructions="focus on content like <keyword phrase>")` Skip this pass for "quick" briefs. ### Pass 5 — Cluster + score (no LLM call needed; you reason it out) Group findings into 2-5 clusters by topic similarity. Score each finding: - +2 if it appeared in 2+ Pass-1 result sources (web + reddit, or web + youtube) - +1 if its domain appeared in 3+ results across all passes - +1 if Pass 3 keywords overlap with the brief - +1 if it's a fresh domain (not a household name like youtube.com, github.com — but those don't disqualify the LINK, only the host weight) - -2 if it's an awesome-* list — keep at most one as a "reference" tag, never as a primary finding ## Output shape — FLEXIBLE, pick from the brief | Ahmad's phrasing | Output shape | |---|---| | "find me niches in X" | **Ranked list** of 5-10 niche hits, each `Title — Domain — 1-sentence why — URL` | | "compare niches in X vs Y" | **Table** with rows = niches, columns = audience-size signal, engagement signal, competition density, content gap | | "research the X market", "deep research X" | **Report**: intro (1-2 sentences) + 3-5 clusters (`## Cluster name`) with 3-5 hits each + closing "where the gap is" + flat Sources list | | "what should I make / what's missing" | **Suggestions**: 5-7 concrete ideas, each `Suggestion + the niche it serves + 1-2 example URLs that support it` | | Anything else | **Normal answer**: 2-4 sentence summary + 5-10 URLs as a numbered list | ALWAYS include a `## Sources` flat list at the end with every URL referenced, regardless of shape. This is what the chat UI's auto-export uses. ## Format details per shape ### Ranked list ``` # {Topic / question echo} 1. **{Niche name or title}** — [{domain}]({url}) {1-sentence why this is a niche, not a saturated market.} {Top keyword(s) extracted: kw1, kw2.} _Signal: appeared in web + reddit + youtube_ (or whichever combo) 2. ... ## Sources 1. [{title}]({url}) 2. ... ``` ### Table ``` # {Topic} — niche comparison | Niche | Audience signal | Engagement | Competition | Content gap | Top URL | |---|---|---|---|---|---| | ... | ## Sources ... ``` ### Report ``` # {Topic} — niche map {2-3 sentence intro. What's the topic. How many clusters surfaced. Headline finding.} ## {Cluster 1 — short noun phrase} {1-2 sentence cluster description.} 1. **{Title}** — [{domain}]({url}) — {why} 2. ... ## {Cluster 2} ... ## Where the gap is {2-3 sentences naming the underserved corner. Cite at least 2 URLs inline as `[phrase](url)`.} ## Sources 1. ... ``` ### Suggestions ``` # {Topic} — suggestions 1. **{Concrete idea}.** Serves: {niche audience}. Evidence: [{title}]({url}), [{title2}]({url2}). 2. ... ## Sources 1. ... ``` ### Normal answer ``` {2-4 sentence summary.} 1. [{title}]({url}) — {one-line why} 2. ... ## Sources 1. ... ``` ## Voice - Direct. No "I think", no "it might be worth exploring". - One sentence on each finding, never two. - Numbers when you have them. "1.2k subscribers, 18k views" beats "small but engaged". - Specific subreddit names, channel names, domain names — never "a popular forum". ## What NOT to do - Don't return more than 25 findings total. Compress or cluster instead. - Don't pad with "you might also like" suggestions made up of your own knowledge — every entry must come from a tool result. - Don't pull from awesome-* lists as primary findings. (One reference link OK; never the spine.) - Don't omit the meta extraction pass — every URL in output must have keywords pulled from its page, even if you don't print all keywords in the final shape. - Don't run more than 6 search passes. If 6 passes can't find a niche, surface the negative result + suggest a narrower brief. - Don't translate the brief. Search in the user's language. ## When the brief is thin If Ahmad gives a one-word brief ("niches"), ask ONCE: *"Niches in what space — and for what purpose (content / product / audience / similar-to-example)?"* — then proceed. Never run with no topic. ## Safety / self-check before returning - [ ] Every URL came from a tool result this turn - [ ] Every URL has a title from extracted meta (not invented) - [ ] Output shape matches the user's phrasing - [ ] `## Sources` block at the end with EVERY URL referenced - [ ] No awesome-* link in the primary findings - [ ] At most 25 findings total If any fails, fix and re-check before emitting.