Map Your Market

python3 -c "
import json
with open('/tmp/mym-raw.json') as f:
    d = json.load(f)
top60 = sorted(d['raw_pains'], key=lambda x: x['pain_score'], reverse=True)[:60]
print(json.dumps(top60, indent=2))
"

{
  "clusters": [
    {
      "theme": "exact language from the data",
      "total_score": 847,
      "signal_count": 34,
      "sources": {"reddit": 18, "hn": 12, "github_issue": 4},
      "top_subreddits": ["devops", "sysadmin"],
      "verbatim_quotes": [
        {"text": "exact quote", "source": "reddit", "score": 234, "url": "..."},
        {"text": "exact quote", "source": "hn", "score": 87, "url": "..."}
      ],
      "who_has_this_pain": "description of who is posting about this"
    }
  ]
}

python3 -c "
import json, os
# Confirm clusters file was written
with open('/tmp/mym-clusters.json') as f:
    d = json.load(f)
print(f'Clusters written: {len(d[\"clusters\"])}')
for c in d['clusters']:
    print(f'  {c[\"theme\"]} -- score: {c[\"total_score\"]}, signals: {c[\"signal_count\"]}')
"

python3 -c "
import json
with open('/tmp/mym-raw.json') as f:
    d = json.load(f)
print('ICP signals:')
print(json.dumps(d['icp_signals'], indent=2))
print()
print('Subreddit distribution:')
sub_counts = {}
for p in d['raw_pains']:
    s = p.get('subreddit', '')
    if s:
        sub_counts[s] = sub_counts.get(s, 0) + 1
for sub, count in sorted(sub_counts.items(), key=lambda x: -x[1])[:10]:
    print(f'  r/{sub}: {count} signals')
"

{
  "icp": {
    "who_they_are": "2-3 sentence profile using language from the data",
    "where_they_live": ["r/devops (89 posts)", "r/sysadmin (67 posts)", "HN ask-hn (34 threads)"],
    "what_they_say": ["verbatim quote 1", "verbatim quote 2", "verbatim quote 3"],
    "what_they_have_tried": ["alternative tools or approaches mentioned in the data"],
    "confidence": "high|medium|low -- based on signal volume and source diversity"
  }
}

python3 -c "
import json
with open('/tmp/mym-raw.json') as f:
    d = json.load(f)
ms = d['market_signals']
print('Market signals:')
print(f'  G2 vendors:         {ms[\"vendor_count_g2\"]}')
print(f'  Trends direction:   {ms[\"trends_direction\"]}')
print(f'  HN signals (12mo):  {ms[\"hn_signals_found\"]}')
print(f'  Reddit signals:     {ms[\"reddit_signals_found\"]}')
print(f'  G2 top vendors:     {json.dumps(ms.get(\"top_vendors\", []), indent=4)}')
"

{
  "positioning_angles": [
    {
      "pain": "theme name",
      "statement": "one-line positioning using market language",
      "headline": "landing page headline using verbatim pain language",
      "cold_email_subject": "subject line"
    }
  ],
  "icp_card": {
    "one_liner": "one sentence: who they are + what they care about",
    "where_to_find_them": [...],
    "how_to_talk_to_them": "tone + vocabulary notes from the data"
  },
  "market_map": [...top vendors from G2 with positioning notes...]
}

python3 -c "
import json

# Load all outputs
with open('/tmp/mym-raw.json') as f:
    raw = json.load(f)
with open('/tmp/mym-clusters.json') as f:
    clusters = json.load(f)
with open('/tmp/mym-output.json') as f:
    output = json.load(f)

raw_texts = set()
for p in raw['raw_pains']:
    raw_texts.add(p.get('title', ''))
    raw_texts.add(p.get('body_excerpt', ''))

# Check 1: No em dashes
import json as j
full_text = j.dumps(output)
if '—' in full_text:
    print('FAIL: em dash found in output')
else:
    print('PASS: no em dashes')

# 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: {found}')
else:
    print('PASS: no banned words')

# Check 3: Market size language check
if 'billion' in full_text.lower() or 'trillion' in full_text.lower() or 'worth \$' in full_text.lower():
    print('FAIL: hard market size estimate found -- use directional language only')
else:
    print('PASS: no hard market size estimates')

# Check 4: Signal counts match
total = raw['summary']['total_pain_signals']
print(f'PASS: {total} total pain signals in raw data')

print('Self-QA complete.')
"

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

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

slug = re.sub(r'[^a-z0-9]+', '-', inp['category'].lower()).strip('-')
date = datetime.now().strftime('%Y-%m-%d')
outpath_md = f"docs/market-maps/{slug}-{date}.md"
outpath_json = f"docs/market-maps/{slug}-{date}.json"

# Build markdown report
ms = raw['market_signals']
icp = clusters.get('icp', {})
market_assessment = clusters.get('market_size', {})
angles = output.get('positioning_angles', [])
icp_card = output.get('icp_card', {})
market_map = output.get('market_map', [])

lines = [
    f"# Market Map: {inp['category'].title()}",
    f"Date: {date} | Signals analyzed: {raw['summary']['total_pain_signals']} | Sources: Reddit ({ms['reddit_signals_found']}) + HN ({ms['hn_signals_found']}) + GitHub Issues ({ms['github_issue_signals']})",
    "",
    "---",
    "",
    "## Market Size Signals",
    f"Vendors on G2: {ms['vendor_count_g2']} | Google Trends: {ms['trends_direction'].upper()} | Market stage: {market_assessment.get('stage', 'see signals below')}",
    "",
    market_assessment.get('summary', ''),
    "",
    "---",
    "",
    "## Your ICP",
    "",
    f"**Who they are:** {icp.get('who_they_are', '')}",
    "",
    f"**Where they live:** {', '.join(icp.get('where_they_live', []))}",
    "",
    "**What they say:**",
]
for q in icp.get('what_they_say', []):
    lines.append(f'> "{q}"')
lines += ["", "---", "", "## Top Pains (ranked by signal strength)", ""]

for i, c in enumerate(clusters.get('clusters', []), 1):
    lines.append(f"### Pain {i}: {c['theme']} [score: {c['total_score']}]")
    sources = c.get('sources', {})
    source_str = " + ".join(f"{src} ({cnt})" for src, cnt in sources.items())
    lines.append(f"{c['signal_count']} signals | Sources: {source_str}")
    lines.append(f"Who has this pain: {c.get('who_has_this_pain', '')}")
    lines.append("")
    lines.append("Verbatim:")
    for q in c.get('verbatim_quotes', [])[:4]:
        lines.append(f'> "{q[\"text\"]}" ({q["source"]}, score: {q["score"]})')
    lines.append("")

lines += ["---", "", "## Market Map (Key Players)", ""]
if market_map:
    lines.append("| Vendor | Positioning |")
    lines.append("|---|---|")
    for v in market_map:
        lines.append(f"| {v.get('name','')} | {v.get('positioning','')} |")
else:
    top = ms.get('top_vendors', [])
    if top:
        lines.append("| Vendor | G2 Reviews | Rating |")
        lines.append("|---|---|---|")
        for v in top:
            lines.append(f"| {v.get('name','')} | {v.get('review_count','')} | {v.get('rating','')} |")

lines += ["", "---", "", "## Messaging Framework", ""]
for a in angles:
    lines.append(f"**{a['pain']}:** {a['statement']}")
    lines.append(f"Headline: \"{a['headline']}\"")
    lines.append(f"Cold email subject: \"{a['cold_email_subject']}\"")
    lines.append("")

lines += ["---", "", "## ICP Card", "",
    f"**One liner:** {icp_card.get('one_liner', '')}",
    "",
    f"**Find them at:** {', '.join(icp_card.get('where_to_find_them', []))}",
    "",
    f"**How to talk to them:** {icp_card.get('how_to_talk_to_them', '')}",
    "",
    "---",
    "",
    "## Data Quality Notes",
    f"- All pain quotes are verbatim from raw signals",
    f"- All vendor names from G2 scrape",
    f"- Market size is directional only (no dollar estimates)",
    f"- Sources: Reddit ({ms['reddit_signals_found']}), HN ({ms['hn_signals_found']}), GitHub Issues ({ms['github_issue_signals']}), G2 ({ms['vendor_count_g2']} vendors)",
    "",
    f"Saved to: {outpath_md}",
    f"JSON snapshot: {outpath_json}",
]

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

# Save JSON snapshot
snapshot = {"input": inp, "market_signals": ms, "clusters": clusters.get('clusters', []),
            "icp": icp, "market_size": market_assessment, "positioning": output, "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/mym-input.json /tmp/mym-raw.json /tmp/mym-clusters.json /tmp/mym-output.json
echo "Done. Market map saved to docs/market-maps/"