Where Your Customer Lives

python3 -c "
import json
with open('/tmp/wcl-raw.json') as f:
    d = json.load(f)
channels = d['channels_discovered']
print(f'Channels found: {len(channels)}')
print()
print(f'{'#':<4} {'Channel':<35} {'Type':<14} {'Members':<12} {'ICP signals':<13} {'Score':<8} Tier')
print('-' * 100)
for i, ch in enumerate(channels[:15], 1):
    members = ch.get('members', 0)
    m_str = f'{members//1000}K' if members >= 1000 else str(members) if members else '?'
    print(f'{i:<4} {ch[\"name\"]:<35} {ch[\"type\"]:<14} {m_str:<12} {ch.get(\"icp_signal_count\",0):<13} {ch.get(\"channel_score\",0):<8} {ch.get(\"tier\",\"\")}')
"

python3 -c "
import json
with open('/tmp/wcl-raw.json') as f:
    d = json.load(f)
channels = d['channels_discovered']
if channels:
    top = channels[0]
    print(f'Top channel: {top[\"name\"]}')
    print(f'Evidence posts:')
    for ep in top.get('evidence_posts', [])[:3]:
        print(f'  [{ep.get(\"score\",0):.0f}] {ep.get(\"title\",\"\")}')
        print(f'       {ep.get(\"url\",\"\")}')
"

python3 -c "
import json
with open('/tmp/wcl-raw.json') as f:
    d = json.load(f)
top_channels = [ch for ch in d['channels_discovered'] if ch.get('tier') in ('top-priority', 'high')]
print(json.dumps(top_channels, indent=2))
"

{
  "playbook": [
    {
      "channel": "r/devops",
      "evidence": "34 ICP signals traced here, avg pain score 180",
      "who_is_here": "...",
      "entry_tactic": "...",
      "content_angle": "...",
      "anti_patterns": ["...", "..."]
    }
  ]
}

python3 -c "
import json
with open('/tmp/wcl-channels.json') as f:
    d = json.load(f)
print(f'Playbook entries: {len(d[\"playbook\"])}')
for p in d['playbook']:
    print(f'  {p[\"channel\"]}')
"

python3 << 'PYEOF'
import json
from datetime import datetime

with open('/tmp/wcl-input.json') as f:
    inp = json.load(f)
with open('/tmp/wcl-raw.json') as f:
    raw = json.load(f)
with open('/tmp/wcl-channels.json') as f:
    enriched = json.load(f)

playbook_by_channel = {p['channel']: p for p in enriched['playbook']}
channels = raw['channels_discovered']

output = {
    "date": raw['date'],
    "product": inp['product'],
    "icp_role": inp['icp_role'],
    "icp_pain": inp['icp_pain'],
    "competitors": inp.get('competitors', []),
    "total_channels": raw['summary']['total_channels'],
    "channels": []
}

for ch in channels:
    name = ch['name']
    playbook = playbook_by_channel.get(name, {})
    output['channels'].append({
        "rank": channels.index(ch) + 1,
        "name": name,
        "type": ch['type'],
        "url": ch['url'],
        "members": ch.get('members', 0),
        "active_users": ch.get('active_users', 0),
        "icp_signal_count": ch.get('icp_signal_count', 0),
        "competitor_mentions": ch.get('competitor_mentions', 0),
        "channel_score": ch.get('channel_score', 0),
        "tier": ch.get('tier', ''),
        "entry_type": ch.get('entry_type', 'open'),
        "evidence_posts": ch.get('evidence_posts', []),
        "who_is_here": playbook.get('who_is_here', ''),
        "entry_tactic": playbook.get('entry_tactic', ''),
        "content_angle": playbook.get('content_angle', ''),
        "anti_patterns": playbook.get('anti_patterns', []),
    })

with open('/tmp/wcl-output.json', 'w') as f:
    json.dump(output, f, indent=2)

print(f"Output written: /tmp/wcl-output.json")
print(f"Total channels: {len(output['channels'])}")
PYEOF

python3 -c "
import json

with open('/tmp/wcl-raw.json') as f:
    raw = json.load(f)
with open('/tmp/wcl-output.json') as f:
    output = json.load(f)

full_text = json.dumps(output)
raw_channel_names = {ch['name'].lower() for ch in raw['channels_discovered']}
passes = 0
fails = 0

# Check 1: No em dashes
if chr(8212) in full_text:
    print('FAIL: em dash found in output -- replace with hyphen')
    fails += 1
else:
    print('PASS: no em dashes')
    passes += 1

# Check 2: No banned words
banned = ['powerful', 'robust', 'seamless', 'innovative', 'game-changing',
          'streamline', 'leverage', 'transform', 'revolutionize']
found = [w for w in banned if w.lower() in full_text.lower()]
if found:
    print(f'FAIL: banned words: {found}')
    fails += 1
else:
    print('PASS: no banned words')
    passes += 1

# Check 3: At least 3 channel types
types = {ch['type'] for ch in output['channels']}
if len(types) < 3:
    print(f'FAIL: only {len(types)} channel type(s) in output: {types}')
    fails += 1
else:
    print(f'PASS: {len(types)} channel types: {types}')
    passes += 1

# Check 4: All channel names exist in raw data
for ch in output['channels']:
    if ch['name'].lower() not in raw_channel_names:
        print(f'FAIL: channel not in raw data: {ch[\"name\"]}')
        fails += 1

if fails == 0:
    print('PASS: all channel names verified in raw data')
    passes += 1

# Check 5: No generic entry tactics
generic_phrases = ['engage with the community', 'post about your product', 'share your content']
for ch in output['channels']:
    tactic = ch.get('entry_tactic', '').lower()
    for phrase in generic_phrases:
        if phrase in tactic:
            print(f'FAIL: generic entry tactic in {ch[\"name\"]}: contains \"{phrase}\"')
            fails += 1

if fails == 0:
    print('PASS: entry tactics are channel-specific')

print()
print(f'Result: {passes} passed, {fails} failed')
if fails > 0:
    print('Fix failures before saving.')
else:
    print('All checks passed. Ready to save.')
"

python3 << 'PYEOF'
import json, os, re
from datetime import datetime

with open('/tmp/wcl-input.json') as f:
    inp = json.load(f)
with open('/tmp/wcl-raw.json') as f:
    raw = json.load(f)
with open('/tmp/wcl-output.json') as f:
    output = json.load(f)

slug = re.sub(r'[^a-z0-9]+', '-', (inp.get('icp_role') or inp['category']).lower()).strip('-')[:40]
date = datetime.now().strftime('%Y-%m-%d')
os.makedirs('docs/channel-map', exist_ok=True)

outpath_md = f"docs/channel-map/{slug}-{date}.md"
outpath_json = f"docs/channel-map/{slug}-{date}.json"

channels = output['channels']
by_type = {}
for ch in channels:
    by_type.setdefault(ch['type'], []).append(ch)

lines = [
    f"# Where Your Customer Lives: {inp['product'] or inp['category'].title()}",
    f"ICP: {inp['icp_role']} | Date: {date} | Channels found: {len(channels)}",
    "",
    "---",
    "",
    "## Channel Ranking",
    "",
]

tier_labels = {"top-priority": "TOP PRIORITY", "high": "HIGH", "medium": "MEDIUM", "low": "LOW"}

for ch in channels:
    members = ch.get('members', 0)
    m_str = f"{members//1000}K" if members >= 1000 else str(members) if members else "member count not found"
    tier_label = tier_labels.get(ch.get('tier', ''), ch.get('tier', '').upper())
    
    lines.append(f"### #{ch['rank']}: {ch['name']} [score: {ch['channel_score']}] -- {tier_label}")
    
    active = ch.get('active_users', 0)
    active_str = f" | Active: {active//1000}K/day" if active >= 1000 else f" | Active: {active}/day" if active else ""
    lines.append(f"Type: {ch['type'].title()} | Members: {m_str}{active_str} | {ch.get('entry_type', 'open').title()} to join")
    
    evidence_str = f"{ch['icp_signal_count']} ICP signals traced here" if ch['icp_signal_count'] > 0 else "Discovered via DuckDuckGo search"
    lines.append(f"Evidence: {evidence_str}")
    
    if ch.get('competitor_mentions', 0) > 0 and inp.get('competitors'):
        lines.append(f"Competitor mentions: {ch['competitor_mentions']} across {', '.join(inp['competitors'][:3])}")
    
    lines.append("")
    
    if ch.get('who_is_here'):
        lines.append(f"**Who is here:** {ch['who_is_here']}")
        lines.append("")
    
    if ch.get('entry_tactic'):
        lines.append(f"**Entry tactic:** {ch['entry_tactic']}")
        lines.append("")
    
    if ch.get('content_angle'):
        lines.append(f"**Content angle:** {ch['content_angle']}")
        lines.append("")
    
    if ch.get('anti_patterns'):
        lines.append("**Anti-patterns:**")
        for ap in ch['anti_patterns']:
            lines.append(f"- {ap}")
        lines.append("")
    
    lines.append("---")
    lines.append("")

lines += [
    "## Channel Summary by Type",
    "",
    "| Type | Count | Best channel | Score |",
    "|---|---|---|---|",
]
for ch_type, chs in sorted(by_type.items(), key=lambda x: -max(c['channel_score'] for c in x[1])):
    best = max(chs, key=lambda x: x['channel_score'])
    lines.append(f"| {ch_type.title()} | {len(chs)} | {best['name']} | {best['channel_score']} |")

lines += [
    "",
    "---",
    "",
    "## Data Quality Notes",
    f"- All channel names exist in Reddit API response or DuckDuckGo search results",
    f"- Member counts from Reddit about.json API or search snippets",
    f"- ICP signal counts match raw data ({raw['reddit_posts_found']} Reddit posts, {raw['hn_signals_found']} HN signals)",
    f"- Competitor layer ran: {raw['summary']['competitor_layer_ran']}",
    f"- Sources: Reddit signal-trace, HN signal-trace, DuckDuckGo channel discovery",
    "",
    f"Saved to: {outpath_md}",
    f"JSON snapshot: {outpath_json}",
]

with open(outpath_md, 'w') as f:
    f.write('\n'.join(lines))

# JSON snapshot
snapshot = {
    "input": inp,
    "channels": channels,
    "summary": raw['summary'],
    "date": date,
}
with open(outpath_json, 'w') as f:
    json.dump(snapshot, f, indent=2)

print(f"Report saved: {outpath_md}")
print(f"JSON snapshot: {outpath_json}")
PYEOF

rm -f /tmp/wcl-input.json /tmp/wcl-raw.json /tmp/wcl-channels.json /tmp/wcl-output.json
echo "Done. Channel map saved to docs/channel-map/"