并非所有 Reddit 主题都适合外展。询问“最适合我的初创公司的 SERP API 是什么”的帖子比“什么是 SERP API”的购买意愿要高得多。该评分器会在 Reddit 中搜索您的产品类别，根据意图信号对话题进行分类，并输出值得参与的高意图话题的排名列表。每次搜索费用为 0.005 美元。
前置条件
- Python 3.8+
- 请求库
- 来自 scavio.dev 的 Scavio API 密钥
- 目标产品类别或关键词
操作指南
步骤 1: 定义意图信号模式
为不同级别的购买意图创建模式匹配器。
Python
import os, requests, re
API_KEY = os.environ['SCAVIO_API_KEY']
SH = {'x-api-key': API_KEY, 'Content-Type': 'application/json'}
INTENT_SIGNALS = {
'high_purchase': {
'patterns': ['looking for', 'need a', 'want to buy', 'budget for', 'willing to pay',
'recommend me', 'what should i use', 'shopping for'],
'weight': 10
},
'comparison': {
'patterns': ['vs', 'versus', 'compared to', 'alternative to', 'better than',
'switch from', 'migrate from'],
'weight': 8
},
'evaluation': {
'patterns': ['review of', 'experience with', 'thoughts on', 'worth it',
'anyone tried', 'opinions on', 'how is'],
'weight': 6
},
'pain_point': {
'patterns': ['frustrated with', 'problem with', 'struggling with', 'hate',
'broken', 'too expensive', 'unreliable'],
'weight': 7
},
'informational': {
'patterns': ['what is', 'how does', 'explain', 'eli5', 'tutorial', 'guide'],
'weight': 2
}
}
print('Intent signal categories configured:')
for cat, data in INTENT_SIGNALS.items():
print(f' {cat}: {len(data["patterns"])} patterns, weight {data["weight"]}')步骤 2: 搜索 Reddit 并评分主题
拉出 Reddit 线程并根据发现的意图信号对每个线程进行评分。
Python
def score_thread(title, snippet):
text = f'{title} {snippet}'.lower()
total_score = 0
matched_intents = []
for category, data in INTENT_SIGNALS.items():
for pattern in data['patterns']:
if pattern in text:
total_score += data['weight']
matched_intents.append(category)
break # One match per category
return total_score, list(set(matched_intents))
def search_and_score(query):
data = requests.post('https://api.scavio.dev/api/v1/search',
headers=SH, json={'query': query, 'platform': 'reddit', 'country_code': 'us'}).json()
scored = []
for r in data.get('organic_results', []):
title = r.get('title', '')
snippet = r.get('snippet', '')
score, intents = score_thread(title, snippet)
scored.append({
'title': title[:80], 'link': r.get('link', ''),
'snippet': snippet[:120], 'score': score,
'intents': intents
})
scored.sort(key=lambda x: x['score'], reverse=True)
return scored
results = search_and_score('serp api recommendation')
for r in results[:5]:
print(f' [{r["score"]:2}] {r["title"][:60]} | {r["intents"]}')步骤 3: 搜索多个查询并聚合
运行多个以意图为中心的查询并合并结果。
Python
def multi_query_score(product, queries=None):
if not queries:
queries = [
f'{product} recommendation',
f'best {product} for startup',
f'{product} alternative',
f'{product} vs',
f'looking for {product}',
]
all_scored = []
seen_links = set()
for query in queries:
results = search_and_score(query)
for r in results:
if r['link'] not in seen_links:
seen_links.add(r['link'])
all_scored.append(r)
all_scored.sort(key=lambda x: x['score'], reverse=True)
cost = len(queries) * 0.005
print(f'Scored {len(all_scored)} unique threads from {len(queries)} queries. Cost: ${cost:.3f}')
return all_scored
scored = multi_query_score('serp api')
print(f'\nHigh intent threads (score >= 8):')
high_intent = [s for s in scored if s['score'] >= 8]
for s in high_intent[:10]:
print(f' [{s["score"]:2}] {s["title"][:60]}')
print(f' Intents: {", ".join(s["intents"])}')步骤 4: 生成外展优先级列表
按意图得分和新近度对线程进行排名，以进行外展优先级。
Python
def outreach_list(scored, min_score=6):
qualified = [s for s in scored if s['score'] >= min_score]
print(f'\n=== Outreach Priority List ===')
print(f'Threads above score {min_score}: {len(qualified)}/{len(scored)}')
print(f'\n{"#":3} {"Score":6} {"Intents":30} {"Thread":50}')
print('-' * 95)
for i, s in enumerate(qualified[:20], 1):
intent_str = ', '.join(s['intents'][:3])
print(f'{i:3} [{s["score"]:2}] {intent_str:28} {s["title"][:48]}')
# Summary stats
if qualified:
avg_score = sum(s['score'] for s in qualified) / len(qualified)
intent_dist = {}
for s in qualified:
for intent in s['intents']:
intent_dist[intent] = intent_dist.get(intent, 0) + 1
print(f'\nAvg intent score: {avg_score:.1f}')
print(f'Intent distribution:')
for intent, count in sorted(intent_dist.items(), key=lambda x: -x[1]):
print(f' {intent}: {count} threads')
outreach_list(scored)Python 示例
Python
import os, requests
SH = {'x-api-key': os.environ['SCAVIO_API_KEY'], 'Content-Type': 'application/json'}
HIGH_INTENT = ['looking for', 'recommend', 'budget for', 'need a', 'best']
def score_reddit(query):
data = requests.post('https://api.scavio.dev/api/v1/search',
headers=SH, json={'query': query, 'platform': 'reddit', 'country_code': 'us'}).json()
for r in data.get('organic_results', [])[:5]:
text = f"{r.get('title','')} {r.get('snippet','')}".lower()
score = sum(1 for p in HIGH_INTENT if p in text)
if score > 0:
print(f' [{score}] {r["title"][:60]}')
score_reddit('serp api recommendation')JavaScript 示例
JavaScript
const SH = { 'x-api-key': process.env.SCAVIO_API_KEY, 'Content-Type': 'application/json' };
const HIGH_INTENT = ['looking for', 'recommend', 'budget for', 'need a', 'best'];
async function scoreReddit(query) {
const data = await fetch('https://api.scavio.dev/api/v1/search', {
method: 'POST', headers: SH,
body: JSON.stringify({ query, platform: 'reddit', country_code: 'us' })
}).then(r => r.json());
for (const r of (data.organic_results || []).slice(0, 5)) {
const text = `${r.title || ''} ${r.snippet || ''}`.toLowerCase();
const score = HIGH_INTENT.filter(p => text.includes(p)).length;
if (score > 0) console.log(` [${score}] ${r.title.slice(0, 60)}`);
}
}
scoreReddit('serp api recommendation').catch(console.error);预期输出
JSON
Intent signal categories configured:
high_purchase: 8 patterns, weight 10
comparison: 7 patterns, weight 8
evaluation: 7 patterns, weight 6
pain_point: 7 patterns, weight 7
Scored 42 unique threads from 5 queries. Cost: $0.025
High intent threads (score >= 8):
[18] Looking for a SERP API alternative to SerpAPI, budget $50/mo
Intents: high_purchase, comparison
[16] Need a search API for my AI agent startup, what should I use?
Intents: high_purchase, evaluation
[14] Frustrated with SerpAPI pricing, looking for alternatives
Intents: pain_point, high_purchase
=== Outreach Priority List ===
Threads above score 6: 15/42