customer-pain-mining

What this skill does

# Customer Pain Mining

Competitors at the early stage aren't a threat — they're a **proxy for the customer**. Their unhappy users have already done your custdev calls. This skill harvests them.

The goal is **verbatim wording**, not paraphrase. Founders summarize and lose the gold. Pull exact phrases — they become product copy.

## When this skill applies

- A founder asks what users dislike about competitors or the category
- Preparing custdev calls — you want talking points in the customer's own language
- Writing landing-page copy or positioning — you need real pain wording
- Hunting white-space features competitors aren't shipping
- After `competitor-discovery` — going deeper on the named players

## What you need

1. **Competitor list** — 1 to 5 named competitors (run `competitor-discovery` first if there isn't one)
2. **Niche / category** — one line for context (e.g. "AI writing for students")
3. **Use case for the output** — affects source weighting:
   - Custdev prep → Reddit (long-form verbatim wins)
   - Ad copy → Exa for short blog-post pull-quotes
   - Product strategy → Exa for structured "Pros / Cons" review pages

## Tools

**Anysite MCP**:
- `mcp__claude_ai_Anysite__execute(source="reddit", category="search", endpoint="search_posts", params={...})` — Reddit posts by query (anonymous, viral, long-form).
- `mcp__claude_ai_Anysite__execute(source="linkedin", category="search", endpoint="search_posts", params={"keywords": "<pain-phrase>", "sort": "relevance", "count": 15})` — LinkedIn posts BY professionals describing the pain. Critical: search by the PAIN, not the competitor name (see Step 5).
- `mcp__claude_ai_Anysite__query_cache(cache_key, conditions, ...)` — filter cached results (e.g. `vote_count > 50`).
- `mcp__claude_ai_Anysite__get_page(cache_key, offset)` — paginate.
- `mcp__claude_ai_Anysite__execute(source="youtube", category="video", endpoint="video_comments", params={"video": "<id>", "count": 50})` — comments under a competitor-review YouTube video; people who watched a 15-min review have committed to evaluating the product. Filter `like_count >= 5`. Watch for affiliate astroturf (5+ short identical-tone praises from different accounts).
- `mcp__claude_ai_Anysite__execute(source="twitter", category="search", endpoint="search_posts", params={"query": "<niche+pain>", "min_likes": 5, "count": 20, "language": "English"})` — viral short-form pain quotes. Set `min_likes` ≥ 50 for breakthrough quotes; otherwise filter aggressively for relevance because the query catches handle namesakes (e.g. "Bright" matches users named "Bright").

**Exa MCP** (covers review aggregators and blog content):
- `mcp__claude_ai_Exa__web_search_exa(query, numResults)` — find blog reviews, comparison articles, "why I left X" Medium posts.
- `mcp__claude_ai_Exa__web_fetch_exa(urls)` — pull full review content. Note: Exa fetch on Reddit URLs sometimes returns SOURCE_NOT_AVAILABLE; use the Anysite Reddit endpoint as primary for Reddit content.

Budget: ~8 Anysite executes (3-4 Reddit + 1-2 LinkedIn + 1 YouTube + optional Twitter) + ~5 Exa calls. Reddit, LinkedIn, Exa, and YouTube comments each carry distinct slices of signal — Reddit is anonymous-viral, LinkedIn is identified-professional, Exa is curated review-blog, YouTube comments are evaluation-stage.

## Source mix — pick by segment BEFORE you start

The order of sources depends on where the user lives professionally. Don't run blindly — pick the right primary first:

| Segment | Primary | Secondary | Skip / late |
|---|---|---|---|
| Consumer + EdTech + B2C SaaS | Reddit broad sweep (Step 1) | Exa review blogs (Step 6) | LinkedIn helpful but not primary |
| B2B SaaS sold to professionals (sales tools, marketing, HR) | LinkedIn issue-pain search (Step 5) | Exa fetch of structured review pages | Reddit thinner for these niches |
| Developer tools / DevTool / AI infrastructure | Exa review blogs + LinkedIn issue-pain (parallel) + r/programming / r/LocalLLaMA / r/webscraping / r/AI_Agents | YouTube comments under review videos; Twitter for viral pain | — |
| Mixed (pro-sumer, freelance tools) | Reddit + LinkedIn in parallel | Exa review blogs | — |

Test (3 second check): "Would the typical user of this competitor post their complaint on Reddit (anonymous), LinkedIn (professional reputation), or a developer blog post (technical depth)?" Start with whichever wins.

## How to run

### Step 1 — Reddit category-level pain sweep (start broad) — **consumer / viral-pain niches only**

Counterintuitively, **for niches where pain is broadly viral, a broad query often returns more pain than a negative query.** Reddit's algorithm surfaces high-virality posts; in pain-dominated niches (consumer detector/integrity scandals, broken health products, expensive subscriptions), pain dominates.

`mcp__claude_ai_Anysite__execute(source="reddit", category="search", endpoint="search_posts", params={"query": "<niche> <one descriptor>", "sort": "top", "time_filter": "year", "count": 20})`

For pain-dominant consumer niches (e.g. AI-writing-students), broad queries like `"best AI writing tool college essay"` (sort=top) surface viral pain posts at 20K–48K upvotes describing exactly the product friction you'd otherwise need to dig for. The query is framed as discovery but the algorithm bubbles up frustration.

Pre-check before running this step: Skim the top 5 results' titles. If most are about regulation/ethics/controversy and NOT about product friction, the niche isn't pain-dominant and broad-Reddit-first will mislead. Drop to Step 2 + Step 5 instead. For developer / B2B niches, broad-Reddit-first usually misleads — skip to Step 2 + Step 5 + Step 6.

### Step 2 — Reddit per-competitor sweep

For each named competitor, run ONE single-phrase query. Avoid OR-chains — Reddit's relevance ranking gets confused:

`mcp__claude_ai_Anysite__execute(source="reddit", category="search", endpoint="search_posts", params={"query": "<competitor> <one descriptor>", "sort": "relevance", "time_filter": "year", "count": 15})`

Working query patterns:
- `"<competitor> review"` — review threads
- `"<competitor> <quality complaint>"` — e.g. `"QuillBot paraphrase robotic"` or `"Bright Data expensive failed requests"`
- `"<competitor> <accuracy complaint>"` — e.g. `"Jenni citations hallucination"` or `"Firecrawl credit multiplier"`
- `"<competitor> alternative"` — implicit critique (also surfaces substitute names)

Anti-patterns to avoid:
- OR-chains like `"<competitor> sucks OR broken OR hate OR alternative"` confuse relevance and return off-topic threads.
- Single-phrase queries combining a smaller-brand name with multiple descriptors can return zero results because the smaller brand has a thin Reddit footprint. Start with the most-talked-about competitor first.

For each post, capture: title, subreddit, vote_count, any quotable phrase from the title. Titles are usually the most distilled version of the complaint.

Filter cached results to keep only posts with real engagement (10+ votes for small subs, 50+ for major ones):

```
mcp__claude_ai_Anysite__query_cache(cache_key="<from step 2>", conditions=[{"field": "vote_count", "op": ">=", "value": 10}], sort_by="vote_count", sort_order="desc")
```

### Step 3 — Find the dedicated subreddit

Niche pain often clusters in a single subreddit dedicated to the problem. For AI-writing-detection pain, validation has surfaced `r/BypassAiDetect`, `r/humanizing`, `r/PromptEngineering`. For web-scraping API pain, the recurring subs are `r/webscraping`, `r/WebScrapingInsider`, `r/WebDataDiggers`, `r/Agent_AI`, and `r/LocalLLaMA` (self-host workaround).

If you spot a recurring sub in Step 1–2 results, run one targeted query inside it via title-keyword filtering — though the Reddit endpoint doesn't filter by sub directly, results from that sub float to the top of relevance queries.

### Step 4 — YouTube comments under a top review video

For consumer or DevTool products with strong YouTube review presence, comments