pain-hunter

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• SKILL.md

SKILL.md (first lines)

# Pain-point Hunter

Hunt for real user pain points in public communities. Turn competitor weaknesses into your product opportunities.

This Claude Code skill is the Claude-host wrapper for the shared workflow in `core/pain-hunter.md`. Keep host-specific command and runtime guidance here; keep host-agnostic research logic aligned with the shared core.

## How to Use

```bash
# Quick mode — pass the product/market name directly
/pain-hunter "Notion"
/pain-hunter "AI writing tools"

# Guided mode — Claude walks you through the inputs
/pain-hunter
```

---

## Step 0 — Check Search Capability

Before starting, use Tavily as the search tool by default.

- **Use Tavily by default for search.**
- **Only use built-in WebSearch if the user explicitly says not to use Tavily.**
- **If Tavily is not configured or not callable:** stop and show the following instead of silently falling back:

```
💡 Tavily MCP is recommended for higher-quality community content (Reddit, HN, V2EX).

   Register (free tier available): https://tavily.com
   After getting your API Key, follow the setup guide: https://docs.tavily.com/docs/mcp

   Tavily is required by default for this workflow. If you want, tell me explicitly to continue with built-in WebSearch instead.
```

---

## Step 1 — Clarify Inputs

If no product name was passed as an argument, ask the user:

1. **Product or market name** — the user must provide the product or market name as free-text input. Do not show a product picker, recommendation list, or numbered menu.
   Examples: "Notion", "AI writing tools for developers", "video analysis for fencing coaches"
2. **Primary audience** — who uses this product?
3. **Specific competitor** — ask: "Any competitor to compare against? (e.g. Notion, Figma — or type 'none')"

---

## Step 2 — Build Search Queries

Combine the product name with this negative keyword matrix:

```
sucks / broken / slow / expensive / laggy / "switch to" / alternative /
"worst part" / "I hate" / "after X years" / "switched away from"
```

If a specific competitor was provided, also search:
- `[product] vs [competitor]`
- `switch from [product] to [competitor]`
- `alternative to [product]`

**Noise filters (always apply):**
- Exclude domains with affiliate link patterns
- Exclude titles matching: `"best X tools"` / `"top 10"` / `"review 202X"`

**Platform-specific tactics (best-effort, not guaranteed):**

**Reddit:**
- Use `include_domains: ["reddit.com"]` if using Tavily
- First find the product's home subreddit (e.g. r/Notion), then search within it
- Prioritize threads by **comment count** (controversy = real pain)
- Target titles containing: `"I switched from"` / `"alternatives to"` / `"after X years"`

**Hacker News:**
- Query: `site:news.ycombinator.com [product name]`
- Also try: `hn.algolia.com/?q=[product]&type=comment` (if accessible)
- Look for `Ask HN:` threads about alternatives
- HN pain skews technical: performance, API design, pricing model

**V2EX (Chinese products):**
- Query: `site:v2ex.com` + Chinese negative terms: `太慢 / 卡 / 垃圾 / 坑 / 不好用 / 替代 / 卸载 / 收费了`
- Find the product's home node first
- Key insight: V2EX titles are bland — **real pain is in the reply threads**

**Twitter/X:**
- Use recent search, query: `[product name] -filter:links`
- Role: supplementary validation only, not primary source
- Value: catching sudden complaint spikes from bad updates

**Product Hunt:**
- Query: `site:producthunt.com "[product name]"`
- Target neutral-to-negative comments (filter extreme 1-star and promotional 5-star)
- Availability depends on search engine; skip if not accessible

---

## Step 3 — Batch Processing (Token Overflow Prevention)

1. Read snippets only for all results → select Top 10 by relevance
2. From Top 10, pick the 3 posts with highest "pain density" → read full content
3. Remaining 7 → extract key sentences only

**Sample counting rules:**
- One independent URL = 1 source sample; multiple comments in the same URL still count as 1 source
- One source can match multiple pain points; each pain point counts at most once per source
- Cross-platform reposts of the same event: keep the original discussion, don't double-count
- Sample count reflects this session's retrieved set only — not total web volume

---

## Step 4 — Generate Report (in-chat)

Output the following structure in the conversation:

```markdown
## Pain-point Research Report: [Product Name]
**Date:** [timestamp] | **Search:** [Tavily / WebSearch]

From lyfeng/indie-research-toolkit, MIT.

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