Competitor Discovery

Competitor Discovery

The pitch deck usually names 2–3 lookalikes. The real comparison set is whatever the customer almost picked instead — including substitutes, free generic tools, "doing nothing," and adjacent-market players one feature-pivot away. This skill produces that map.

Framing (so the categories don't drift)

Three industry frameworks converge on what to capture:

• Christensen JTBD: The customer is "hiring" your product for a job. The true competitor is anything else they could hire for the same job — including bananas (vs. milkshakes), spreadsheets (vs. SaaS), boredom (vs. Facebook).
• April Dunford (Obviously Awesome): Competitive alternatives are the starting point of positioning. Ask "what would the customer do if our product didn't exist?" — this surfaces "do nothing" as the #1 alternative in ~25% of lost enterprise deals. Beware "phantom competitors" — theoretical companies that never show up in real deals; they dilute positioning.
• Porter's Five Forces — Substitutes + New Entrants: Substitutes solve the same outcome with a different category. New entrants are adjacent-market players one feature pivot away.

These map to our four output categories (Direct / Substitute / Workaround / Convergence). Detectors, app stores, compliance tools, payment processors, distribution platforms — these are "rule-setters" that shape the market, not competitors. Note them in context.

When this skill applies

• A founder names 1–3 obvious competitors; you validate or expand
• Pre-flight before customer-pain-mining or positioning-map
• Mapping a product into a category when the category is fuzzy
• Sanity-checking a pitch deck competitor slide

What you need

1. Product name (e.g. "Anysite")
2. One-liner — what it does, for whom (e.g. "AI writing for college students that drafts, cites, runs integrity checks")
3. Pitch-deck competitors if named (optional)
4. Segment indicator — consumer / SMB / B2B / DevTool / DeepTech. This determines which source order to use.
5. Mode — top5 (default; produces 5 anchor names + 1 workaround) or landscape (produces 5 anchors + 25-70 name long-tail + 2 suggested strategic-group axes for positioning-map).

Ask for missing info before running. Don't guess from the product name.

Tools

Anysite MCP (call mcp__claude_ai_Anysite__discover(source, category) once before first execute):

• mcp__claude_ai_Anysite__execute(source="linkedin", category="search", endpoint="search_companies", params={...}) — broad competitor search by keywords. Best primitive for consumer / mid-market.
• mcp__claude_ai_Anysite__execute(source="linkedin", category="search", endpoint="search_posts", params={...}) — listicle posts naming competitors with structured mentioned[] field. Landscape mode aggregates this across N posts.
• mcp__claude_ai_Anysite__execute(source="reddit", category="search", endpoint="search_posts", params={...}) — real comparison threads.
• mcp__claude_ai_Anysite__execute(source="twitter", category="search", endpoint="search_posts", params={...}) — viral category quotes. Set min_likes ≥ 5 to filter noise; for breakthrough quotes set ≥ 50.
• mcp__claude_ai_Anysite__execute(source="yc", category="search", endpoint="search_companies", params={...}) — useful for B2B / DeepTech, also for niches where the latest YC batches surge into a consumer category (check w/o assumption).
• mcp__claude_ai_Anysite__execute(source="youtube", category="search", endpoint="search_videos", params={...}) — listicle videos for B2C / prosumer niches. Filter by view_count ≥ 10K.
• mcp__claude_ai_Anysite__execute(source="youtube", category="video", endpoint="video_subtitles", params={...}) — fetch the transcript of 1 head-to-head comparison video for deep-dive context. Auto-captions can garble product names ("Elicit" → "illicit", "SciSpace" → "size space") — use for cross-validation, not primary extraction.
• mcp__claude_ai_Anysite__execute(source="youtube", category="video", endpoint="video_comments", params={...}) — comments under listicle videos cross-validate which competitors customers compare against. Filter like_count >= 5. Watch for affiliate astroturf (same product praised in 5+ short identical comments).
• mcp__claude_ai_Anysite__execute(source="duckduckgo", category="search", endpoint="search", params={...}) — Exa fallback for budget-constrained runs.
• mcp__claude_ai_Anysite__execute(source="sec", category="search", endpoint="search_companies", params={...}) — for niches with a public incumbent (e.g. Chegg in EdTech, Salesforce in B2B sales). Pull the latest 10-K, fetch the Competition section via Exa.
• mcp__claude_ai_Anysite__query_cache(cache_key, conditions, sort_by, aggregate, group_by, ...) — slice cached results. Landscape mode aggregates LinkedIn mentioned[] here.

Exa MCP:

• mcp__claude_ai_Exa__web_search_exa(query, numResults) — semantic search. Describe the ideal page, not keywords.
• mcp__claude_ai_Exa__web_fetch_exa(urls) — read JS-heavy pages (modern SaaS marketing sites the webparser can't render).

Budget:

• top5 mode: ~6 Anysite executes + ~3 Exa calls.
• landscape mode: ~10 Anysite + ~4 Exa. Includes 1 YouTube subtitle deep-dive, 1 SEC fetch if public incumbent, 1 landscape-mode aggregation.

After the budget, marginal signal drops fast.

How to run

Source order depends on segment:

• Consumer / Prosumer / EdTech / SMB SaaS: Exa → LinkedIn search_companies → LinkedIn search_posts (with mentioned[]) → YouTube search_videos → Reddit. Skip YC unless recent batch surge. Skip Instagram for software (validated).
• B2B / Enterprise / DeepTech / DevTool: YC → LinkedIn search_companies → LinkedIn search_posts → Exa → Reddit. Skip YouTube unless niche has analyst-channel ecosystem. SEC 10-K if public incumbent.
• Public-incumbent niche (EdTech, B2B sales, HR, fintech with public anchors): SEC 10-K Competition section is canonical — add as Step 8.

If Exa is rate-limited or returns thin results, fall back to duckduckgo/search/search — covers different long-tail URLs.

If mode = landscape, also run Step 3.5 (LinkedIn mentioned[] aggregation) and optionally Step 4a (YouTube subtitle deep-dive on the highest-view-count head-to-head video).

Step 1 — Exa: category listicles (consumer-first niches)

Find the "best X" comparison pages. These already did the clustering for you.

mcp__claude_ai_Exa__web_search_exa(query="listicle ranking best tools for 2025 — detailed comparison with pricing pros and cons", numResults=8)

Read the highlights — most listicles return 3–5 named competitors right in the snippet, often with prices. If a listicle looks dense, fetch it:

mcp__claude_ai_Exa__web_fetch_exa(urls=[""])

For Anysite (API-first web data platform with MCP for AI agents), the search "listicle ranking best web scraping APIs and AI agent data extraction platforms 2025 — detailed comparison with pricing and pros and cons" surfaces Bright Data, Apify, Firecrawl, ScrapingBee, ScraperAPI, Oxylabs, Zyte, Diffbot, Octoparse in the first page. That is the canonical set.

Step 2 — LinkedIn company search (broad category discovery)

mcp__claude_ai_Anysite__execute(source="linkedin", category="search", endpoint="search_companies", params={"keywords": "", "count": 20})

Pick keywords that name what the product does, not what it is. "AI writing assistant students academic" beats "EdTech AI" by 10x.

Returns 10+ companies with URN, name, industry. Cross-reference with Step 1 — companies appearing in both are strong direct competitors.

If keyword combo returns 0 results, broaden it (drop one term) — narrow queries fail silently on LinkedIn.

Step 3 — LinkedIn search_posts: listicle-shaped posts

Founders + creators in your niche routinely cross-post "10 tools for X" listicles. These contain structured competitor lists:

mcp__claude_ai_Anysite__execute(source="linkedin", category="search", endpoint="search_posts", params={"keywords": " ", "sort": "recent", "date_posted": "past-month", "count": 15})

Use seed-competitor + niche keywords (e.g. "Firecrawl Bright Data web scraping" or "Paperpal academic writing"). Broad market queries like "AI writing tools students best 2025" tend to return discourse posts (ethics, policy, hiring) rather than competitor listicles.

Each post has a mentioned[] array — extract company names and URLs directly. A single high-engagement listicle post typically names 6–12 competitors. Empirically, one well-targeted query on the web-scraping niche surfaced a 50-like "AI consensus" post naming 10 competitors (Bright Data, Zyte, ScrapingBee, Firecrawl, Scrape.do, ScraperAPI, Apify, Scrapingdog, Oxylabs, Scrapfly) with LinkedIn URLs attached.

Filter the spam: much of LinkedIn is SEO accounts cross-posting identical tool lists. Keep only posts where comment_count + sum(reactions[].count) >= 10. Use query_cache:

mcp__claude_ai_Anysite__query_cache(cache_key="", conditions=[{"field": "comment_count", "op": ">=", "value": 5}])

Step 3.5 — Landscape-mode aggregation (only if mode = "landscape")

For market-mapping or pitch-deck-validation use cases, you don't want a top-5 — you want the full long-tail. Aggregate the mentioned[] field across the 15+ posts from Step 3 into one ranked list. Anysite's query_cache group_by only handles single-level fields, so do the aggregation in your head / inline:

1. Use query_cache(cache_key=, sort_by="comment_count", sort_order="desc", limit=15) to surface the top engagement-weighted posts.
2. For each post in the cache, walk mentioned[] and pull name where @type == "@linkedin_search_post_mentioned_company".
3. Tally name → count across all posts. Sort descending. Names with ≥2 cross-post citations are your anchor candidates (high-confidence landscape members); names with 1 citation are the long-tail watch list.

Empirically: 1 broad LinkedIn search (Step 3, count=25) tends to surface 10–28 unique competitor names, with 5+ having ≥2 cross-post citations. The cross-post citation count is your phantom-competitor filter: keep names with ≥2 citations as candidates, set the rest aside in a "long-tail / watch list" section. You can optionally run Step 3 twice with different keyword combinations (e.g. one query with niche keywords + one with seed-trio competitors) and merge tallies.

Bonus: use the same cached posts to surface the top creator-analyst in the niche (highest-engagement repeat author) — useful for linkedin/user/posts follow-up if you later want creator-commentary signal.

Step 4 — YouTube listicle videos (B2C / prosumer / EdTech / consumer-AI / DevTool)

mcp__claude_ai_Anysite__execute(source="youtube", category="search", endpoint="search_videos", params={"query": "best tools comparison review", "count": 15})

Filter for view_count ≥ 10K — that's the affiliate-spam vs. real-creator line. Look for title patterns: "X vs Y", "best N tools for ", "I tried N tools". Each high-view-count video names 5–15 competitors with feature comparison. Validated on the web-scraping niche: "Apify Bright Data Firecrawl comparison review" returned a Greg Isenberg Firecrawl explainer at 169K views and an Apify-vs-Octoparse-vs-BrowseAI head-to-head.

For the single highest-view-count head-to-head comparison video, optionally fetch the transcript:

mcp__claude_ai_Anysite__execute(source="youtube", category="video", endpoint="video_subtitles", params={"video": "", "lang": "en"})

Auto-caption caveat: names can garble ("Elicit" → "illicit", "SciSpace" → "size space"). Use the transcript to cross-validate names already in your candidate list, not to extract new names via regex.

Optionally fetch top comments:

mcp__claude_ai_Anysite__execute(source="youtube", category="video", endpoint="video_comments", params={"video": "", "count": 30})

Filter like_count >= 5. Watch for affiliate astroturf — the same product praised in 5+ short identical-tone comments from different accounts is paid promotion, not signal. Skip the cluster.

YouTube has stronger affordance for B2C / prosumer / DevTool niches than for enterprise B2B. For DeepTech / strict-B2B niches, prefer YC + SEC over YouTube.

Step 5 — Reddit alternative-hunting (real comparison set)

For 2–3 of your strongest candidates from Steps 1–3:

mcp__claude_ai_Anysite__execute(source="reddit", category="search", endpoint="search_posts", params={"query": " alternative", "sort": "relevance", "time_filter": "year", "count": 15})

Useful query patterns (pick 2 of these per run, not all):

• " alternative" — direct substitution
• " tool recommendations" — discovery threads
• "better than " — explicit comparisons

Use sort=relevance, not sort=top, because top sorts by virality and floods you with off-topic viral content. Relevance is per-query.

Single-phrase queries beat OR-chains. "Bright Data alternative expensive" works; OR-chained queries like "Bright Data alternative sucks expensive overpriced" confuse Reddit's relevance ranking and return off-topic threads.

In each thread, capture: competitor names mentioned in the title and top comments, subreddit, post title, vote_count. Vote count is your relevance signal.

Step 6 — Workaround / "doing nothing"

The real competitor is often free + manual. Search Reddit for the manual workaround:

mcp__claude_ai_Anysite__execute(source="reddit", category="search", endpoint="search_posts", params={"query": " manually OR spreadsheet OR diy OR self-host", "count": 10, "sort": "relevance", "time_filter": "year"})

If users describe a free-and-manual recipe in detail, that recipe is a competitor. Founders systematically underrate this. For Anysite, "self-host Crawl4AI + bring your own LLM key + manage proxies yourself" is the dominant developer workaround across r/webscraping, r/LocalLLaMA — explicitly costed out in multiple comparison blogs.

Step 7 — (B2B / DevTool / DeepTech) YC search

Worth running for B2B / DeepTech / Enterprise / Dev Tool. YC tends to be thin for most consumer / EdTech niches; spot-check first. Exception: if the niche has had a recent YC batch surge (e.g. agentic-AI-consumer in W25/F25), check before skipping.

mcp__claude_ai_Anysite__execute(source="yc", category="search", endpoint="search_companies", params={"query": "", "count": 20})

Read one_liner, industries, subindustry. Keep names whose tagline overlaps the JTBD. Validated on the web-scraping niche: "web scraping browser automation agent" returned Browser Use (W25, 50K stars in 3 months) and StableBrowse (S26) — two convergence threats from the agentic-browser side that the named-competitor set missed.

Step 8 — (Public-incumbent niche only) SEC Competition section

For niches where a public-company incumbent exists (EdTech: Chegg, Duolingo; B2B sales: Salesforce; HR: Workday; etc.):

mcp__claude_ai_Anysite__execute(source="sec", category="search", endpoint="search_companies", params={"entity_name": "", "forms": ["10-K"], "count": 3})

Pick the latest 10-K, fetch its document_url via Exa: mcp__claude_ai_Exa__web_fetch_exa(urls=[""]). Look for the "Competition" and "Risk Factors" sections — these contain a legal-grade enumerated competitor list (Chegg's 10-K names Course Hero, ChatGPT, Quizlet directly). Skip when no public incumbent exists.

Step 9 — Synthesize

Cluster everything into four buckets:

1. Direct competitors — same job, same audience, same category (e.g. Firecrawl for Anysite)
2. Substitutes — different category, same outcome / JTBD (e.g. ChatGPT for an academic AI writer — Christensen lens)
3. Workarounds / "doing nothing" — no product, but a habit the customer already has (Dunford lens — often the #1 alternative the pitch deck misses)
4. Convergence threats (optional) — adjacent-market players one feature-pivot away (e.g. Browser Use's agentic browser is a convergence threat

…

Source & license

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

• Author: anysiteio
• Source: anysiteio/agent-skills
• License: MIT

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