--- name: competitor-pr-finder description: 'Give it your product URL or description. It finds your top 5 competitors, runs three-track PR research across all of them (editorial, podcasts, communities), identifies which channels appear most frequently, looks up the journalist or host for each, and returns a tiered outreach list with story angles and ready-to-send cold pitch drafts tailored to your product. Use when asked to find PR opportunities, discover where competitors got featured, build a media outreach list, find which journalists cover my space, or get pitch templates for press coverage.' compatibility: [claude-code, gemini-cli, github-copilot] ---

# Competitor PR Finder

Give it your product URL. It finds your competitors, researches every PR channel they used (news, podcasts, communities), surfaces the channels that appear across multiple competitors (your proven targets), finds the journalist or host for each, and drafts a personalized cold pitch for your product at every tier-1 channel.

---

**Zero-hallucination policy:** Every channel, journalist name, story angle, and pitch detail in the output must trace to a specific Tavily search result or the fetched product page. This applies to: - Competitor names: must appear in Tavily search results, not AI training knowledge - Channel names: must have a URL in the search results - Journalist/host names: must appear verbatim in a Tavily snippet - Story angles: extracted from article/episode titles in search results only - Pitch drafts: reference specific evidence from search data + product analysis

---

## Common Mistakes

| The agent will want to... | Why that's wrong | |---|---| | Name a journalist from training knowledge | Every journalist name must trace to a search result snippet. Writing "Sarah Perez covers startups at TechCrunch" from memory is hallucination. | | List channels without evidence URLs | Every channel in the output must have at least one URL from the PR search results proving a competitor was featured there. | | Skip the competitor confirmation step | Always show discovered competitors and wait for the user to confirm. Wrong competitors = wasted searches and a useless output. | | Generate generic pitches ("We'd love to be featured") | Every pitch must reference a specific angle from the evidence AND a specific differentiator from the product analysis. | | Mark a channel as Tier 1 with only 1 competitor occurrence | Tier 1 = 3+ competitors. Tier 2 = exactly 2. Tier 3 = 1. Do not promote channels that haven't proven themselves. | | Use em dashes in output | Replace all em dashes (--) with hyphens. |

---

## Read Reference Files Before Each Run

```bash cat references/pr-channel-types.md cat references/pitch-guide.md cat references/tier-scoring.md ```

---

## Step 1: Setup Check

```bash echo "TAVILY_API_KEY: ${TAVILY_API_KEY:+set}${TAVILY_API_KEY:-NOT SET -- required}" echo "FIRECRAWL_API_KEY: ${FIRECRAWL_API_KEY:+set}${FIRECRAWL_API_KEY:-not set, Tavily extract will be used as fallback}" ```

**If TAVILY_API_KEY is missing:** Stop immediately. Tell the user: "TAVILY_API_KEY is required to research competitors and find PR coverage. There is no fallback. Get it at app.tavily.com -- free tier: 1000 credits/month (about 43 full runs at ~23 searches/run). Add it to your .env file."

**If only FIRECRAWL_API_KEY is missing:** Continue. Tavily extract will be used for the URL fetch.

---

## Step 2: Parse Input

Collect from the conversation: - `product_url`: the URL to fetch (required, unless user pastes a description directly) - `product_name`: optional, derived from page if not provided - `geography`: optional -- US / Europe / global. Default: US

**If the user provides only a pasted description (no URL):** Skip Steps 3 and 4. Go directly to Step 4 (product analysis) using the pasted text as `product_content`. Set `page_source` to `user_description` and note in `data_quality_flags`.

**If neither URL nor description:** Ask: "What is the URL of your product or startup? Or paste a short description: what it does, who it is for, and what makes it different from competitors."

Derive product slug:

```bash PRODUCT_SLUG=$(python3 -c " from urllib.parse import urlparse import sys url = 'URL_HERE' if url.startswith('http'): host = urlparse(url).netloc.replace('www.', '') print(host.split('.')[0]) else: import re print(re.sub(r'[^a-z0-9]', '-', url[:30].lower()).strip('-')) ") echo "Product slug: $PRODUCT_SLUG" ```

---

## Step 3: Fetch Product Page

**Primary: Firecrawl (if FIRECRAWL_API_KEY is set)**

```bash curl -s -X POST https://api.firecrawl.dev/v1/scrape \ -H "Authorization: Bearer $FIRECRAWL_API_KEY" \ -H "Content-Type: application/json" \ -d '{"url": "URL_HERE", "formats": ["markdown"], "onlyMainContent": true}' \ | python3 -c " import sys, json d = json.load(sys.stdin) content = d.get('data', {}).get('markdown', '') or d.get('markdown', '') print(f'Fetched via Firecrawl: {len(content)} characters') open('/tmp/cprf-product-raw.md', 'w').write(content) " ```

**Fallback: Tavily extract (if FIRECRAWL_API_KEY is not set)**

```bash curl -s -X POST https://api.tavily.com/extract \ -H "Content-Type: application/json" \ -d "{\"api_key\": \"$TAVILY_API_KEY\", \"urls\": [\"URL_HERE\"]}" \ | python3 -c " import sys, json d = json.load(sys.stdin) content = d.get('results', [{}])[0].get('raw_content', '') print(f'Fetched via Tavily extract: {len(content)} characters') open('/tmp/cprf-product-raw.md', 'w').write(content) " ```

**Checkpoint:**

```bash python3 -c " content = open('/tmp/cprf-product-raw.md').read() if len(content) < 200: print('ERROR: fewer than 200 characters fetched') else: print(f'Content OK: {len(content)} characters') " ```

**If content < 200 characters:** Stop fetching. Tell the user: "The product page returned no readable content -- the site is likely JavaScript-rendered and blocked the fetch. Please paste a short description directly: what it does, who it is for, and what makes it different."

---

## Step 4: Product Analysis (AI)

Print page content:

```bash python3 -c " content = open('/tmp/cprf-product-raw.md').read()[:5000] print('=== PRODUCT PAGE (first 5000 chars) ===') print(content) " ```

**AI instructions:** Analyze the product page above and extract:

- `product_name`: the product or company name - `one_line_description`: what it does, for whom, core value prop. Under 20 words. No marketing language. Example: "CI/CD automation for developer teams that self-host their pipelines." - `industry_taxonomy`: `l1` (top-level: e.g. developer tools / fintech / healthtech / consumer), `l2` (sector: e.g. devops / payments / telemedicine), `l3` (specific niche: e.g. CI/CD automation / embedded payments / async video consultation). Vague labels like "technology" alone are not acceptable. - `differentiators`: exactly 2-3 specific things that distinguish this product from generic competitors. These feed directly into the pitch drafts -- be specific. Example: ["Self-hosted pipeline runner -- no data leaves your infra", "Native support for monorepos with dynamic step generation"] - `icp`: `buyer_persona` (job title), `company_type`, `company_size` - `geography_bias`: US / Europe / global / unclear - `page_source`: "live_page" or "user_description"

Write to `/tmp/cprf-product-analysis.json`:

```bash python3 << 'PYEOF' import json

analysis = { # FILL from your analysis above "product_name": "", "one_line_description": "", "industry_taxonomy": {"l1": "", "l2": "", "l3": ""}, "differentiators": [], "icp": {"buyer_persona": "", "company_type": "", "company_size": ""}, "geography_bias": "US", "page_source": "live_page" }

json.dump(analysis, open('/tmp/cprf-product-analysis.json', 'w'), indent=2) print('Product analysis written.') PYEOF ```

Verify:

```bash python3 -c " import json a = json.load(open('/tmp/cprf-product-analysis.json')) print('Product:', a['product_name']) print('Industry:', a['industry_taxonomy']['l1'], '>', a['industry_taxonomy']['l2'], '>', a['industry_taxonomy']['l3']) print('Differentiators:') for d in a['differentiators']: print(f' - {d}') " ```

---

## Step 4b: Phase 1 -- Competitor Discovery

```bash ls scripts/research.py 2>/dev/null && echo "script found" || echo "ERROR: scripts/research.py not found -- cannot continue" ```

```bash python3 scripts/research.py \ --phase discover \ --product-analysis /tmp/cprf-product-analysis.json \ --tavily-key "$TAVILY_API_KEY" \ --output /tmp/cprf-competitors-raw.json ```

Print results for AI review:

```bash python3 -c " import json data = json.load(open('/tmp/cprf-competitors-raw.json')) print(f'Searches run: {len(data[\"competitor_searches\"])}') for s in data['competitor_searches']: print(f'\nQuery: {s[\"query\"]}') print(f'Answer: {s.get(\"answer\",\"\")[:400]}') for r in s.get('results', [])[:5]: print(f' - {r[\"title\"]} | {r[\"url\"]}') print(f' {r.get(\"content\",\"\")[:200]}') " ```

**AI instructions:** Read the search results above. Pick exactly 5 competitor companies that: 1. Are named in the search result titles, answers, or snippets 2. Are in the same L3 niche as the product being analyzed 3. Are actual competing products (not agencies, consultancies, or list articles) 4. Are distinct from each other (not the same company under different names)

For each competitor write: `name`, `url` (from the search result where they appeared), `description` (one sentence from snippet), `source_url` (the search result URL where they were found).

---

## Step 5: Competitor Confirmation

**Show the discovered competitors to the user:**

```bash python3 << 'PYEOF' import json

analysis = json.load(open('/tmp/cprf-product-analysis.json'))

# FILL: 5 competitors from the search results above candidates = [ # {"name": str, "url": str, "description": str, "source_url": str} ]

print(f"\nFound 5 competitors for {analysis['product_name']} in {analysis['industry_taxonomy']['l3']}:\n") for i, c in enumerate(candidates, 1): print(f" {i}. {c['name']} -- {c['description']}") print(f" {c['url']}")

data = json.load(open('/tmp/cprf-competitors-raw.json')) data['competitor_candidates'] = candidates json.dump(data, open('/tmp/cprf-competitors-raw.json', 'w'), indent=2) PYEOF ```

Tell the user: "These are the 5 competitors I'll research for PR coverage. Add, remove, or swap any -- or say 'looks good' to continue."

**Wait for confirmation.** If the user edits the list (adds/removes/swaps), update the candidates accordingly. Then write the confirmed list:

```bash python3 << 'PYEOF' import json

# FILL: confirmed competitor list (after user review) confirmed = [ # {"name": str, "url": str} ]

json.dump({"confirmed_competitors": confirmed}, open('/tmp/cprf-competitors-confirmed.json', 'w'), indent=2) print(f"Confirmed {len(confirmed)} competitors for PR research.") for c in confirmed: print(f" - {c['name']} ({c['url']})") PYEOF ```

---

## Step 6: Three-Track PR Research (Phase 2)

```bash python3 scripts/research.py \ --phase pr-research \ --competitors /tmp/cprf-competitors-confirmed.json \ --product-analysis /tmp/cprf-product-analysis.json \ --tavily-key "$TAVILY_API_KEY" \ --output /tmp/cprf-pr-raw.json ```

This runs 3 searches per competitor (15 total): - **Track A (Editorial):** `"[competitor]" featured press coverage TechCrunch Forbes Wired article interview` - **Track B (Podcasts):** `"[competitor]" founder CEO podcast interview appeared on episode` - **Track C (Communities):** `"[competitor]" site:reddit.com OR site:news.ycombinator.com OR site:producthunt.com`

Print coverage summary:

```bash python3 -c " import json data = json.load(open('/tmp/cprf-pr-raw.json')) print(f'Competitors researched: {data[\"competitors_researched\"]}') print() for r in data['results']: print(f'{r[\"competitor\"]}:') for track, tdata in r['tracks'].items(): n = len(tdata.get('results'

--- name: competitor-pr-finder description: 'Give it your product URL or description. It finds your top 5 competitors, runs three-track PR research across all of them (editorial, podcasts, communities), identifies which channels appear most frequently, looks up the journalist or host for each, and returns a tiered outreach list with story angles and ready-to-send cold pitch drafts tailored to your product. Use when asked to find PR opportunities, discover where competitors got featured, build a media outreach list, find which journalists cover my space, or get pitch templates for press coverage.' compatibility: [claude-code, gemini-cli, github-copilot] ---

# Competitor PR Finder

Give it your product URL. It finds your competitors, researches every PR channel they used (news, podcasts, communities), surfaces the channels that appear across multiple competitors (your proven targets), finds the journalist or host for each, and drafts a personalized cold pitch for your product at every tier-1 channel.

---

**Zero-hallucination policy:** Every channel, journalist name, story angle, and pitch detail in the output must trace to a specific Tavily search result or the fetched product page. This applies to: - Competitor names: must appear in Tavily search results, not AI training knowledge - Channel names: must have a URL in the search results - Journalist/host names: must appear verbatim in a Tavily snippet - Story angles: extracted from article/episode titles in search results only - Pitch drafts: reference specific evidence from search data + product analysis

---

## Common Mistakes

| The agent will want to... | Why that's wrong | |---|---| | Name a journalist from training knowledge | Every journalist name must trace to a search result snippet. Writing "Sarah Perez covers startups at TechCrunch" from memory is hallucination. | | List channels without evidence URLs | Every channel in the output must have at least one URL from the PR search results proving a competitor was featured there. | | Skip the competitor confirmation step | Always show discovered competitors and wait for the user to confirm. Wrong competitors = wasted searches and a useless output. | | Generate generic pitches ("We'd love to be featured") | Every pitch must reference a specific angle from the evidence AND a specific differentiator from the product analysis. | | Mark a channel as Tier 1 with only 1 competitor occurrence | Tier 1 = 3+ competitors. Tier 2 = exactly 2. Tier 3 = 1. Do not promote channels that haven't proven themselves. | | Use em dashes in output | Replace all em dashes (--) with hyphens. |

---

## Read Reference Files Before Each Run

```bash cat references/pr-channel-types.md cat references/pitch-guide.md cat references/tier-scoring.md ```

---

## Step 1: Setup Check

```bash echo "TAVILY_API_KEY: ${TAVILY_API_KEY:+set}${TAVILY_API_KEY:-NOT SET -- required}" echo "FIRECRAWL_API_KEY: ${FIRECRAWL_API_KEY:+set}${FIRECRAWL_API_KEY:-not set, Tavily extract will be used as fallback}" ```

**If TAVILY_API_KEY is missing:** Stop immediately. Tell the user: "TAVILY_API_KEY is required to research competitors and find PR coverage. There is no fallback. Get it at app.tavily.com -- free tier: 1000 credits/month (about 43 full runs at ~23 searches/run). Add it to your .env file."

**If only FIRECRAWL_API_KEY is missing:** Continue. Tavily extract will be used for the URL fetch.

---

## Step 2: Parse Input

Collect from the conversation: - `product_url`: the URL to fetch (required, unless user pastes a description directly) - `product_name`: optional, derived from page if not provided - `geography`: optional -- US / Europe / global. Default: US

**If the user provides only a pasted description (no URL):** Skip Steps 3 and 4. Go directly to Step 4 (product analysis) using the pasted text as `product_content`. Set `page_source` to `user_description` and note in `data_quality_flags`.

**If neither URL nor description:** Ask: "What is the URL of your product or startup? Or paste a short description: what it does, who it is for, and what makes it different from competitors."

Derive product slug:

```bash PRODUCT_SLUG=$(python3 -c " from urllib.parse import urlparse import sys url = 'URL_HERE' if url.startswith('http'): host = urlparse(url).netloc.replace('www.', '') print(host.split('.')[0]) else: import re print(re.sub(r'[^a-z0-9]', '-', url[:30].lower()).strip('-')) ") echo "Product slug: $PRODUCT_SLUG" ```

---

## Step 3: Fetch Product Page

**Primary: Firecrawl (if FIRECRAWL_API_KEY is set)**

```bash curl -s -X POST https://api.firecrawl.dev/v1/scrape \ -H "Authorization: Bearer $FIRECRAWL_API_KEY" \ -H "Content-Type: application/json" \ -d '{"url": "URL_HERE", "formats": ["markdown"], "onlyMainContent": true}' \ | python3 -c " import sys, json d = json.load(sys.stdin) content = d.get('data', {}).get('markdown', '') or d.get('markdown', '') print(f'Fetched via Firecrawl: {len(content)} characters') open('/tmp/cprf-product-raw.md', 'w').write(content) " ```

**Fallback: Tavily extract (if FIRECRAWL_API_KEY is not set)**

```bash curl -s -X POST https://api.tavily.com/extract \ -H "Content-Type: application/json" \ -d "{\"api_key\": \"$TAVILY_API_KEY\", \"urls\": [\"URL_HERE\"]}" \ | python3 -c " import sys, json d = json.load(sys.stdin) content = d.get('results', [{}])[0].get('raw_content', '') print(f'Fetched via Tavily extract: {len(content)} characters') open('/tmp/cprf-product-raw.md', 'w').write(content) " ```

**Checkpoint:**

```bash python3 -c " content = open('/tmp/cprf-product-raw.md').read() if len(content) < 200: print('ERROR: fewer than 200 characters fetched') else: print(f'Content OK: {len(content)} characters') " ```

**If content < 200 characters:** Stop fetching. Tell the user: "The product page returned no readable content -- the site is likely JavaScript-rendered and blocked the fetch. Please paste a short description directly: what it does, who it is for, and what makes it different."

---

## Step 4: Product Analysis (AI)

Print page content:

```bash python3 -c " content = open('/tmp/cprf-product-raw.md').read()[:5000] print('=== PRODUCT PAGE (first 5000 chars) ===') print(content) " ```

**AI instructions:** Analyze the product page above and extract:

- `product_name`: the product or company name - `one_line_description`: what it does, for whom, core value prop. Under 20 words. No marketing language. Example: "CI/CD automation for developer teams that self-host their pipelines." - `industry_taxonomy`: `l1` (top-level: e.g. developer tools / fintech / healthtech / consumer), `l2` (sector: e.g. devops / payments / telemedicine), `l3` (specific niche: e.g. CI/CD automation / embedded payments / async video consultation). Vague labels like "technology" alone are not acceptable. - `differentiators`: exactly 2-3 specific things that distinguish this product from generic competitors. These feed directly into the pitch drafts -- be specific. Example: ["Self-hosted pipeline runner -- no data leaves your infra", "Native support for monorepos with dynamic step generation"] - `icp`: `buyer_persona` (job title), `company_type`, `company_size` - `geography_bias`: US / Europe / global / unclear - `page_source`: "live_page" or "user_description"

Write to `/tmp/cprf-product-analysis.json`:

```bash python3 << 'PYEOF' import json

analysis = { # FILL from your analysis above "product_name": "", "one_line_description": "", "industry_taxonomy": {"l1": "", "l2": "", "l3": ""}, "differentiators": [], "icp": {"buyer_persona": "", "company_type": "", "company_size": ""}, "geography_bias": "US", "page_source": "live_page" }

json.dump(analysis, open('/tmp/cprf-product-analysis.json', 'w'), indent=2) print('Product analysis written.') PYEOF ```

Verify:

```bash python3 -c " import json a = json.load(open('/tmp/cprf-product-analysis.json')) print('Product:', a['product_name']) print('Industry:', a['industry_taxonomy']['l1'], '>', a['industry_taxonomy']['l2'], '>', a['industry_taxonomy']['l3']) print('Differentiators:') for d in a['differentiators']: print(f' - {d}') " ```

---

## Step 4b: Phase 1 -- Competitor Discovery

```bash ls scripts/research.py 2>/dev/null && echo "script found" || echo "ERROR: scripts/research.py not found -- cannot continue" ```

```bash python3 scripts/research.py \ --phase discover \ --product-analysis /tmp/cprf-product-analysis.json \ --tavily-key "$TAVILY_API_KEY" \ --output /tmp/cprf-competitors-raw.json ```

Print results for AI review:

```bash python3 -c " import json data = json.load(open('/tmp/cprf-competitors-raw.json')) print(f'Searches run: {len(data[\"competitor_searches\"])}') for s in data['competitor_searches']: print(f'\nQuery: {s[\"query\"]}') print(f'Answer: {s.get(\"answer\",\"\")[:400]}') for r in s.get('results', [])[:5]: print(f' - {r[\"title\"]} | {r[\"url\"]}') print(f' {r.get(\"content\",\"\")[:200]}') " ```

**AI instructions:** Read the search results above. Pick exactly 5 competitor companies that: 1. Are named in the search result titles, answers, or snippets 2. Are in the same L3 niche as the product being analyzed 3. Are actual competing products (not agencies, consultancies, or list articles) 4. Are distinct from each other (not the same company under different names)

For each competitor write: `name`, `url` (from the search result where they appeared), `description` (one sentence from snippet), `source_url` (the search result URL where they were found).

---

## Step 5: Competitor Confirmation

**Show the discovered competitors to the user:**

```bash python3 << 'PYEOF' import json

analysis = json.load(open('/tmp/cprf-product-analysis.json'))

# FILL: 5 competitors from the search results above candidates = [ # {"name": str, "url": str, "description": str, "source_url": str} ]

print(f"\nFound 5 competitors for {analysis['product_name']} in {analysis['industry_taxonomy']['l3']}:\n") for i, c in enumerate(candidates, 1): print(f" {i}. {c['name']} -- {c['description']}") print(f" {c['url']}")

data = json.load(open('/tmp/cprf-competitors-raw.json')) data['competitor_candidates'] = candidates json.dump(data, open('/tmp/cprf-competitors-raw.json', 'w'), indent=2) PYEOF ```

Tell the user: "These are the 5 competitors I'll research for PR coverage. Add, remove, or swap any -- or say 'looks good' to continue."

**Wait for confirmation.** If the user edits the list (adds/removes/swaps), update the candidates accordingly. Then write the confirmed list:

```bash python3 << 'PYEOF' import json

# FILL: confirmed competitor list (after user review) confirmed = [ # {"name": str, "url": str} ]

json.dump({"confirmed_competitors": confirmed}, open('/tmp/cprf-competitors-confirmed.json', 'w'), indent=2) print(f"Confirmed {len(confirmed)} competitors for PR research.") for c in confirmed: print(f" - {c['name']} ({c['url']})") PYEOF ```

---

## Step 6: Three-Track PR Research (Phase 2)

```bash python3 scripts/research.py \ --phase pr-research \ --competitors /tmp/cprf-competitors-confirmed.json \ --product-analysis /tmp/cprf-product-analysis.json \ --tavily-key "$TAVILY_API_KEY" \ --output /tmp/cprf-pr-raw.json ```

This runs 3 searches per competitor (15 total): - **Track A (Editorial):** `"[competitor]" featured press coverage TechCrunch Forbes Wired article interview` - **Track B (Podcasts):** `"[competitor]" founder CEO podcast interview appeared on episode` - **Track C (Communities):** `"[competitor]" site:reddit.com OR site:news.ycombinator.com OR site:producthunt.com`

Print coverage summary:

```bash python3 -c " import json data = json.load(open('/tmp/cprf-pr-raw.json')) print(f'Competitors researched: {data[\"competitors_researched\"]}') print() for r in data['results']: print(f'{r[\"competitor\"]}:') for track, tdata in r['tracks'].items(): n = len(tdata.get('results'

--- name: competitor-pr-finder description: 'Give it your product URL or description. It finds your top 5 competitors, runs three-track PR research across all of them (editorial, podcasts, communities), identifies which channels appear most frequently, looks up the journalist or host for each, and returns a tiered outreach list with story angles and ready-to-send cold pitch drafts tailored to your product. Use when asked to find PR opportunities, discover where competitors got featured, build a media outreach list, find which journalists cover my space, or get pitch templates for press coverage.' compatibility: [claude-code, gemini-cli, github-copilot] ---

# Competitor PR Finder

Give it your product URL. It finds your competitors, researches every PR channel they used (news, podcasts, communities), surfaces the channels that appear across multiple competitors (your proven targets), finds the journalist or host for each, and drafts a personalized cold pitch for your product at every tier-1 channel.

---

**Zero-hallucination policy:** Every channel, journalist name, story angle, and pitch detail in the output must trace to a specific Tavily search result or the fetched product page. This applies to: - Competitor names: must appear in Tavily search results, not AI training knowledge - Channel names: must have a URL in the search results - Journalist/host names: must appear verbatim in a Tavily snippet - Story angles: extracted from article/episode titles in search results only - Pitch drafts: reference specific evidence from search data + product analysis

---

## Common Mistakes

| The agent will want to... | Why that's wrong | |---|---| | Name a journalist from training knowledge | Every journalist name must trace to a search result snippet. Writing "Sarah Perez covers startups at TechCrunch" from memory is hallucination. | | List channels without evidence URLs | Every channel in the output must have at least one URL from the PR search results proving a competitor was featured there. | | Skip the competitor confirmation step | Always show discovered competitors and wait for the user to confirm. Wrong competitors = wasted searches and a useless output. | | Generate generic pitches ("We'd love to be featured") | Every pitch must reference a specific angle from the evidence AND a specific differentiator from the product analysis. | | Mark a channel as Tier 1 with only 1 competitor occurrence | Tier 1 = 3+ competitors. Tier 2 = exactly 2. Tier 3 = 1. Do not promote channels that haven't proven themselves. | | Use em dashes in output | Replace all em dashes (--) with hyphens. |

---

## Read Reference Files Before Each Run

```bash cat references/pr-channel-types.md cat references/pitch-guide.md cat references/tier-scoring.md ```

---

## Step 1: Setup Check

```bash echo "TAVILY_API_KEY: ${TAVILY_API_KEY:+set}${TAVILY_API_KEY:-NOT SET -- required}" echo "FIRECRAWL_API_KEY: ${FIRECRAWL_API_KEY:+set}${FIRECRAWL_API_KEY:-not set, Tavily extract will be used as fallback}" ```

**If TAVILY_API_KEY is missing:** Stop immediately. Tell the user: "TAVILY_API_KEY is required to research competitors and find PR coverage. There is no fallback. Get it at app.tavily.com -- free tier: 1000 credits/month (about 43 full runs at ~23 searches/run). Add it to your .env file."

**If only FIRECRAWL_API_KEY is missing:** Continue. Tavily extract will be used for the URL fetch.

---

## Step 2: Parse Input

Collect from the conversation: - `product_url`: the URL to fetch (required, unless user pastes a description directly) - `product_name`: optional, derived from page if not provided - `geography`: optional -- US / Europe / global. Default: US

**If the user provides only a pasted description (no URL):** Skip Steps 3 and 4. Go directly to Step 4 (product analysis) using the pasted text as `product_content`. Set `page_source` to `user_description` and note in `data_quality_flags`.

**If neither URL nor description:** Ask: "What is the URL of your product or startup? Or paste a short description: what it does, who it is for, and what makes it different from competitors."

Derive product slug:

```bash PRODUCT_SLUG=$(python3 -c " from urllib.parse import urlparse import sys url = 'URL_HERE' if url.startswith('http'): host = urlparse(url).netloc.replace('www.', '') print(host.split('.')[0]) else: import re print(re.sub(r'[^a-z0-9]', '-', url[:30].lower()).strip('-')) ") echo "Product slug: $PRODUCT_SLUG" ```

---

## Step 3: Fetch Product Page

**Primary: Firecrawl (if FIRECRAWL_API_KEY is set)**

```bash curl -s -X POST https://api.firecrawl.dev/v1/scrape \ -H "Authorization: Bearer $FIRECRAWL_API_KEY" \ -H "Content-Type: application/json" \ -d '{"url": "URL_HERE", "formats": ["markdown"], "onlyMainContent": true}' \ | python3 -c " import sys, json d = json.load(sys.stdin) content = d.get('data', {}).get('markdown', '') or d.get('markdown', '') print(f'Fetched via Firecrawl: {len(content)} characters') open('/tmp/cprf-product-raw.md', 'w').write(content) " ```

**Fallback: Tavily extract (if FIRECRAWL_API_KEY is not set)**

```bash curl -s -X POST https://api.tavily.com/extract \ -H "Content-Type: application/json" \ -d "{\"api_key\": \"$TAVILY_API_KEY\", \"urls\": [\"URL_HERE\"]}" \ | python3 -c " import sys, json d = json.load(sys.stdin) content = d.get('results', [{}])[0].get('raw_content', '') print(f'Fetched via Tavily extract: {len(content)} characters') open('/tmp/cprf-product-raw.md', 'w').write(content) " ```

**Checkpoint:**

```bash python3 -c " content = open('/tmp/cprf-product-raw.md').read() if len(content) < 200: print('ERROR: fewer than 200 characters fetched') else: print(f'Content OK: {len(content)} characters') " ```

**If content < 200 characters:** Stop fetching. Tell the user: "The product page returned no readable content -- the site is likely JavaScript-rendered and blocked the fetch. Please paste a short description directly: what it does, who it is for, and what makes it different."

---

## Step 4: Product Analysis (AI)

Print page content:

```bash python3 -c " content = open('/tmp/cprf-product-raw.md').read()[:5000] print('=== PRODUCT PAGE (first 5000 chars) ===') print(content) " ```

**AI instructions:** Analyze the product page above and extract:

- `product_name`: the product or company name - `one_line_description`: what it does, for whom, core value prop. Under 20 words. No marketing language. Example: "CI/CD automation for developer teams that self-host their pipelines." - `industry_taxonomy`: `l1` (top-level: e.g. developer tools / fintech / healthtech / consumer), `l2` (sector: e.g. devops / payments / telemedicine), `l3` (specific niche: e.g. CI/CD automation / embedded payments / async video consultation). Vague labels like "technology" alone are not acceptable. - `differentiators`: exactly 2-3 specific things that distinguish this product from generic competitors. These feed directly into the pitch drafts -- be specific. Example: ["Self-hosted pipeline runner -- no data leaves your infra", "Native support for monorepos with dynamic step generation"] - `icp`: `buyer_persona` (job title), `company_type`, `company_size` - `geography_bias`: US / Europe / global / unclear - `page_source`: "live_page" or "user_description"

Write to `/tmp/cprf-product-analysis.json`:

```bash python3 << 'PYEOF' import json

analysis = { # FILL from your analysis above "product_name": "", "one_line_description": "", "industry_taxonomy": {"l1": "", "l2": "", "l3": ""}, "differentiators": [], "icp": {"buyer_persona": "", "company_type": "", "company_size": ""}, "geography_bias": "US", "page_source": "live_page" }

json.dump(analysis, open('/tmp/cprf-product-analysis.json', 'w'), indent=2) print('Product analysis written.') PYEOF ```

Verify:

```bash python3 -c " import json a = json.load(open('/tmp/cprf-product-analysis.json')) print('Product:', a['product_name']) print('Industry:', a['industry_taxonomy']['l1'], '>', a['industry_taxonomy']['l2'], '>', a['industry_taxonomy']['l3']) print('Differentiators:') for d in a['differentiators']: print(f' - {d}') " ```

---

## Step 4b: Phase 1 -- Competitor Discovery

```bash ls scripts/research.py 2>/dev/null && echo "script found" || echo "ERROR: scripts/research.py not found -- cannot continue" ```

```bash python3 scripts/research.py \ --phase discover \ --product-analysis /tmp/cprf-product-analysis.json \ --tavily-key "$TAVILY_API_KEY" \ --output /tmp/cprf-competitors-raw.json ```

Print results for AI review:

```bash python3 -c " import json data = json.load(open('/tmp/cprf-competitors-raw.json')) print(f'Searches run: {len(data[\"competitor_searches\"])}') for s in data['competitor_searches']: print(f'\nQuery: {s[\"query\"]}') print(f'Answer: {s.get(\"answer\",\"\")[:400]}') for r in s.get('results', [])[:5]: print(f' - {r[\"title\"]} | {r[\"url\"]}') print(f' {r.get(\"content\",\"\")[:200]}') " ```

**AI instructions:** Read the search results above. Pick exactly 5 competitor companies that: 1. Are named in the search result titles, answers, or snippets 2. Are in the same L3 niche as the product being analyzed 3. Are actual competing products (not agencies, consultancies, or list articles) 4. Are distinct from each other (not the same company under different names)

For each competitor write: `name`, `url` (from the search result where they appeared), `description` (one sentence from snippet), `source_url` (the search result URL where they were found).

---

## Step 5: Competitor Confirmation

**Show the discovered competitors to the user:**

```bash python3 << 'PYEOF' import json

analysis = json.load(open('/tmp/cprf-product-analysis.json'))

# FILL: 5 competitors from the search results above candidates = [ # {"name": str, "url": str, "description": str, "source_url": str} ]

print(f"\nFound 5 competitors for {analysis['product_name']} in {analysis['industry_taxonomy']['l3']}:\n") for i, c in enumerate(candidates, 1): print(f" {i}. {c['name']} -- {c['description']}") print(f" {c['url']}")

data = json.load(open('/tmp/cprf-competitors-raw.json')) data['competitor_candidates'] = candidates json.dump(data, open('/tmp/cprf-competitors-raw.json', 'w'), indent=2) PYEOF ```

Tell the user: "These are the 5 competitors I'll research for PR coverage. Add, remove, or swap any -- or say 'looks good' to continue."

**Wait for confirmation.** If the user edits the list (adds/removes/swaps), update the candidates accordingly. Then write the confirmed list:

```bash python3 << 'PYEOF' import json

# FILL: confirmed competitor list (after user review) confirmed = [ # {"name": str, "url": str} ]

json.dump({"confirmed_competitors": confirmed}, open('/tmp/cprf-competitors-confirmed.json', 'w'), indent=2) print(f"Confirmed {len(confirmed)} competitors for PR research.") for c in confirmed: print(f" - {c['name']} ({c['url']})") PYEOF ```

---

## Step 6: Three-Track PR Research (Phase 2)

```bash python3 scripts/research.py \ --phase pr-research \ --competitors /tmp/cprf-competitors-confirmed.json \ --product-analysis /tmp/cprf-product-analysis.json \ --tavily-key "$TAVILY_API_KEY" \ --output /tmp/cprf-pr-raw.json ```

This runs 3 searches per competitor (15 total): - **Track A (Editorial):** `"[competitor]" featured press coverage TechCrunch Forbes Wired article interview` - **Track B (Podcasts):** `"[competitor]" founder CEO podcast interview appeared on episode` - **Track C (Communities):** `"[competitor]" site:reddit.com OR site:news.ycombinator.com OR site:producthunt.com`

Print coverage summary:

```bash python3 -c " import json data = json.load(open('/tmp/cprf-pr-raw.json')) print(f'Competitors researched: {data[\"competitors_researched\"]}') print() for r in data['results']: print(f'{r[\"competitor\"]}:') for track, tdata in r['tracks'].items(): n = len(tdata.get('results'

--- name: competitor-pr-finder description: 'Give it your product URL or description. It finds your top 5 competitors, runs three-track PR research across all of them (editorial, podcasts, communities), identifies which channels appear most frequently, looks up the journalist or host for each, and returns a tiered outreach list with story angles and ready-to-send cold pitch drafts tailored to your product. Use when asked to find PR opportunities, discover where competitors got featured, build a media outreach list, find which journalists cover my space, or get pitch templates for press coverage.' compatibility: [claude-code, gemini-cli, github-copilot] ---

# Competitor PR Finder

Give it your product URL. It finds your competitors, researches every PR channel they used (news, podcasts, communities), surfaces the channels that appear across multiple competitors (your proven targets), finds the journalist or host for each, and drafts a personalized cold pitch for your product at every tier-1 channel.

---

**Zero-hallucination policy:** Every channel, journalist name, story angle, and pitch detail in the output must trace to a specific Tavily search result or the fetched product page. This applies to: - Competitor names: must appear in Tavily search results, not AI training knowledge - Channel names: must have a URL in the search results - Journalist/host names: must appear verbatim in a Tavily snippet - Story angles: extracted from article/episode titles in search results only - Pitch drafts: reference specific evidence from search data + product analysis

---

## Common Mistakes

| The agent will want to... | Why that's wrong | |---|---| | Name a journalist from training knowledge | Every journalist name must trace to a search result snippet. Writing "Sarah Perez covers startups at TechCrunch" from memory is hallucination. | | List channels without evidence URLs | Every channel in the output must have at least one URL from the PR search results proving a competitor was featured there. | | Skip the competitor confirmation step | Always show discovered competitors and wait for the user to confirm. Wrong competitors = wasted searches and a useless output. | | Generate generic pitches ("We'd love to be featured") | Every pitch must reference a specific angle from the evidence AND a specific differentiator from the product analysis. | | Mark a channel as Tier 1 with only 1 competitor occurrence | Tier 1 = 3+ competitors. Tier 2 = exactly 2. Tier 3 = 1. Do not promote channels that haven't proven themselves. | | Use em dashes in output | Replace all em dashes (--) with hyphens. |

---

## Read Reference Files Before Each Run

```bash cat references/pr-channel-types.md cat references/pitch-guide.md cat references/tier-scoring.md ```

---

## Step 1: Setup Check

```bash echo "TAVILY_API_KEY: ${TAVILY_API_KEY:+set}${TAVILY_API_KEY:-NOT SET -- required}" echo "FIRECRAWL_API_KEY: ${FIRECRAWL_API_KEY:+set}${FIRECRAWL_API_KEY:-not set, Tavily extract will be used as fallback}" ```

**If TAVILY_API_KEY is missing:** Stop immediately. Tell the user: "TAVILY_API_KEY is required to research competitors and find PR coverage. There is no fallback. Get it at app.tavily.com -- free tier: 1000 credits/month (about 43 full runs at ~23 searches/run). Add it to your .env file."

**If only FIRECRAWL_API_KEY is missing:** Continue. Tavily extract will be used for the URL fetch.

---

## Step 2: Parse Input

Collect from the conversation: - `product_url`: the URL to fetch (required, unless user pastes a description directly) - `product_name`: optional, derived from page if not provided - `geography`: optional -- US / Europe / global. Default: US

**If the user provides only a pasted description (no URL):** Skip Steps 3 and 4. Go directly to Step 4 (product analysis) using the pasted text as `product_content`. Set `page_source` to `user_description` and note in `data_quality_flags`.

**If neither URL nor description:** Ask: "What is the URL of your product or startup? Or paste a short description: what it does, who it is for, and what makes it different from competitors."

Derive product slug:

```bash PRODUCT_SLUG=$(python3 -c " from urllib.parse import urlparse import sys url = 'URL_HERE' if url.startswith('http'): host = urlparse(url).netloc.replace('www.', '') print(host.split('.')[0]) else: import re print(re.sub(r'[^a-z0-9]', '-', url[:30].lower()).strip('-')) ") echo "Product slug: $PRODUCT_SLUG" ```

---

## Step 3: Fetch Product Page

**Primary: Firecrawl (if FIRECRAWL_API_KEY is set)**

```bash curl -s -X POST https://api.firecrawl.dev/v1/scrape \ -H "Authorization: Bearer $FIRECRAWL_API_KEY" \ -H "Content-Type: application/json" \ -d '{"url": "URL_HERE", "formats": ["markdown"], "onlyMainContent": true}' \ | python3 -c " import sys, json d = json.load(sys.stdin) content = d.get('data', {}).get('markdown', '') or d.get('markdown', '') print(f'Fetched via Firecrawl: {len(content)} characters') open('/tmp/cprf-product-raw.md', 'w').write(content) " ```

**Fallback: Tavily extract (if FIRECRAWL_API_KEY is not set)**

```bash curl -s -X POST https://api.tavily.com/extract \ -H "Content-Type: application/json" \ -d "{\"api_key\": \"$TAVILY_API_KEY\", \"urls\": [\"URL_HERE\"]}" \ | python3 -c " import sys, json d = json.load(sys.stdin) content = d.get('results', [{}])[0].get('raw_content', '') print(f'Fetched via Tavily extract: {len(content)} characters') open('/tmp/cprf-product-raw.md', 'w').write(content) " ```

**Checkpoint:**

```bash python3 -c " content = open('/tmp/cprf-product-raw.md').read() if len(content) < 200: print('ERROR: fewer than 200 characters fetched') else: print(f'Content OK: {len(content)} characters') " ```

**If content < 200 characters:** Stop fetching. Tell the user: "The product page returned no readable content -- the site is likely JavaScript-rendered and blocked the fetch. Please paste a short description directly: what it does, who it is for, and what makes it different."

---

## Step 4: Product Analysis (AI)

Print page content:

```bash python3 -c " content = open('/tmp/cprf-product-raw.md').read()[:5000] print('=== PRODUCT PAGE (first 5000 chars) ===') print(content) " ```

**AI instructions:** Analyze the product page above and extract:

- `product_name`: the product or company name - `one_line_description`: what it does, for whom, core value prop. Under 20 words. No marketing language. Example: "CI/CD automation for developer teams that self-host their pipelines." - `industry_taxonomy`: `l1` (top-level: e.g. developer tools / fintech / healthtech / consumer), `l2` (sector: e.g. devops / payments / telemedicine), `l3` (specific niche: e.g. CI/CD automation / embedded payments / async video consultation). Vague labels like "technology" alone are not acceptable. - `differentiators`: exactly 2-3 specific things that distinguish this product from generic competitors. These feed directly into the pitch drafts -- be specific. Example: ["Self-hosted pipeline runner -- no data leaves your infra", "Native support for monorepos with dynamic step generation"] - `icp`: `buyer_persona` (job title), `company_type`, `company_size` - `geography_bias`: US / Europe / global / unclear - `page_source`: "live_page" or "user_description"

Write to `/tmp/cprf-product-analysis.json`:

```bash python3 << 'PYEOF' import json

analysis = { # FILL from your analysis above "product_name": "", "one_line_description": "", "industry_taxonomy": {"l1": "", "l2": "", "l3": ""}, "differentiators": [], "icp": {"buyer_persona": "", "company_type": "", "company_size": ""}, "geography_bias": "US", "page_source": "live_page" }

json.dump(analysis, open('/tmp/cprf-product-analysis.json', 'w'), indent=2) print('Product analysis written.') PYEOF ```

Verify:

```bash python3 -c " import json a = json.load(open('/tmp/cprf-product-analysis.json')) print('Product:', a['product_name']) print('Industry:', a['industry_taxonomy']['l1'], '>', a['industry_taxonomy']['l2'], '>', a['industry_taxonomy']['l3']) print('Differentiators:') for d in a['differentiators']: print(f' - {d}') " ```

---

## Step 4b: Phase 1 -- Competitor Discovery

```bash ls scripts/research.py 2>/dev/null && echo "script found" || echo "ERROR: scripts/research.py not found -- cannot continue" ```

```bash python3 scripts/research.py \ --phase discover \ --product-analysis /tmp/cprf-product-analysis.json \ --tavily-key "$TAVILY_API_KEY" \ --output /tmp/cprf-competitors-raw.json ```

Print results for AI review:

```bash python3 -c " import json data = json.load(open('/tmp/cprf-competitors-raw.json')) print(f'Searches run: {len(data[\"competitor_searches\"])}') for s in data['competitor_searches']: print(f'\nQuery: {s[\"query\"]}') print(f'Answer: {s.get(\"answer\",\"\")[:400]}') for r in s.get('results', [])[:5]: print(f' - {r[\"title\"]} | {r[\"url\"]}') print(f' {r.get(\"content\",\"\")[:200]}') " ```

**AI instructions:** Read the search results above. Pick exactly 5 competitor companies that: 1. Are named in the search result titles, answers, or snippets 2. Are in the same L3 niche as the product being analyzed 3. Are actual competing products (not agencies, consultancies, or list articles) 4. Are distinct from each other (not the same company under different names)

For each competitor write: `name`, `url` (from the search result where they appeared), `description` (one sentence from snippet), `source_url` (the search result URL where they were found).

---

## Step 5: Competitor Confirmation

**Show the discovered competitors to the user:**

```bash python3 << 'PYEOF' import json

analysis = json.load(open('/tmp/cprf-product-analysis.json'))

# FILL: 5 competitors from the search results above candidates = [ # {"name": str, "url": str, "description": str, "source_url": str} ]

print(f"\nFound 5 competitors for {analysis['product_name']} in {analysis['industry_taxonomy']['l3']}:\n") for i, c in enumerate(candidates, 1): print(f" {i}. {c['name']} -- {c['description']}") print(f" {c['url']}")

data = json.load(open('/tmp/cprf-competitors-raw.json')) data['competitor_candidates'] = candidates json.dump(data, open('/tmp/cprf-competitors-raw.json', 'w'), indent=2) PYEOF ```

Tell the user: "These are the 5 competitors I'll research for PR coverage. Add, remove, or swap any -- or say 'looks good' to continue."

**Wait for confirmation.** If the user edits the list (adds/removes/swaps), update the candidates accordingly. Then write the confirmed list:

```bash python3 << 'PYEOF' import json

# FILL: confirmed competitor list (after user review) confirmed = [ # {"name": str, "url": str} ]

json.dump({"confirmed_competitors": confirmed}, open('/tmp/cprf-competitors-confirmed.json', 'w'), indent=2) print(f"Confirmed {len(confirmed)} competitors for PR research.") for c in confirmed: print(f" - {c['name']} ({c['url']})") PYEOF ```

---

## Step 6: Three-Track PR Research (Phase 2)

```bash python3 scripts/research.py \ --phase pr-research \ --competitors /tmp/cprf-competitors-confirmed.json \ --product-analysis /tmp/cprf-product-analysis.json \ --tavily-key "$TAVILY_API_KEY" \ --output /tmp/cprf-pr-raw.json ```

This runs 3 searches per competitor (15 total): - **Track A (Editorial):** `"[competitor]" featured press coverage TechCrunch Forbes Wired article interview` - **Track B (Podcasts):** `"[competitor]" founder CEO podcast interview appeared on episode` - **Track C (Communities):** `"[competitor]" site:reddit.com OR site:news.ycombinator.com OR site:producthunt.com`

Print coverage summary:

```bash python3 -c " import json data = json.load(open('/tmp/cprf-pr-raw.json')) print(f'Competitors researched: {data[\"competitors_researched\"]}') print() for r in data['results']: print(f'{r[\"competitor\"]}:') for track, tdata in r['tracks'].items(): n = len(tdata.get('results'