--- name: talent-crm description: "Manage talent pipeline with candidate tracking, outreach automation, and interview scheduling. Use for recruiting CRM." domain: sales --- # talent-crm --- name: talent-crm description: > BerkahKarya Talent Agency CRM — manage talent scouting, contracts, commissions, client deals, and performance analytics for Indonesian creator-affiliate campaigns. Covers TikTok, Instagram, YouTube. Niches: digital products, lifestyle, business, education. AI GM: Vilona. Owner: Coder $String$ (Paijo). --- ## Overview BerkahKarya's Talent Agency division previously peaked at **IDR 5B/month** Shopee affiliate revenue. This skill rebuilds that division with a structured CRM pipeline: scout → onboard → contract → activate → track → pay → scale. **Team:** | Role | Person | Contact | |------|--------|---------| | AI GM / Orchestrator | Vilona | (AI) | | Owner | Coder $String$ (Paijo) | @codergaboets | | Ads Master | Veris | @alwayscuanbos | | Ops Manager | Sony | (internal) | | Trading Master | Nuno | @oens77 | --- ## 1. Talent Database - Configure crm, domain, relevant, talent, this settings before first use - Review output quality and adjust parameters - Monitor performance metrics during execution - Document custom configurations for team reference - Schedule regular runs for consistent results ### Scouting Criteria (Minimum Bar) | Platform | Min Followers | Min ER | Niche Fit | |----------|--------------|--------|-----------| | TikTok | 10,000 | ≥ 3% | ✅ Required | | Instagram | 5,000 | ≥ 2% | ✅ Required | | YouTube | 2,000 subs | ≥ 4% (views/subs) | ✅ Required | **Target Niches:** - `digital_products` — online courses, ebooks, templates, SaaS - `lifestyle` — daily vlog, OOTD, beauty, food - `business` — entrepreneur, side hustle, finance tips - `education` — tutorial, how-to, skill building, career **Disqualifiers:** - Fake/bought followers (check ratio spikes) - Inactive >30 days - Niche mismatch (pure gaming, pure politics) - Prior brand conflicts with BerkahKarya clients ### Talent Profile Fields ```yaml talent: id: "TLT-YYYYMMDD-###" name: "" username: "" # @handle platform: [tiktok, instagram, youtube] niche: [digital_products, lifestyle, business, education] followers: 0 engagement_rate: 0.0 # percentage, e.g. 4.5 avg_views_per_post: 0 contact_wa: "" # WhatsApp number contact_ig_dm: "" contact_email: "" region: "Indonesia" city: "" # e.g. Jakarta, Surabaya, Bandung contract_status: draft # draft | negotiating | signed | active | expired | churned commission_rate: 0.0 # percentage of deal value, e.g. 15.0 monthly_revenue_idr: 0 # last 30 days revenue generated total_revenue_idr: 0 # all-time onboarded_at: "" # ISO date contract_expiry: "" # ISO date notes: "" manager: "" # Vilona / Sony / Veris ``` ### Talent Status Flow ``` PROSPECT → CONTACTED → NEGOTIATING → SIGNED → ACTIVE → RENEWAL / EXPIRED / CHURNED ``` --- ## 2. Client Database - Configure crm, domain, relevant, talent, this settings before first use - Review output quality and adjust parameters - Monitor performance metrics during execution - Document custom configurations for team reference - Schedule regular runs for consistent results ### Client Profile Fields ```yaml client: id: "CLT-YYYYMMDD-###" company_name: "" pic_name: "" # Person In Charge pic_contact_wa: "" pic_contact_email: "" industry: "" # e.g. digital product, fashion, F&B deal_stage: prospecting # prospecting | proposal | negotiating | closed_won | closed_lost | active | completed contract_value_idr: 0 # total deal value commission_to_talents_idr: 0 # portion allocated to talents agency_margin_idr: 0 # contract_value - commission_to_talents deliverables: - type: "" # e.g. TikTok video, IG reel, YouTube review quantity: 0 platform: "" deadline: "" status: pending # pending | in_progress | submitted | approved | rejected campaign_start: "" campaign_end: "" assigned_talents: [] # list of talent IDs assigned_manager: "" kpi_target_views: 0 kpi_target_conversions: 0 actual_views: 0 actual_conversions: 0 client_satisfaction_score: 0 # 1-10 notes: "" ``` ### Deal Stage Flow ``` PROSPECTING → PROPOSAL SENT → NEGOTIATING → CLOSED (WON/LOST) → ACTIVE → COMPLETED ``` --- ## 3. Talent Scouting - Configure crm, domain, relevant, talent, this settings before first use - Review output quality and adjust parameters - Monitor performance metrics during execution - Document custom configurations for team reference - Schedule regular runs for consistent results ### Search Process **Step 1 — Define Campaign Requirements** ``` Campaign niche: [digital_products | lifestyle | business | education] Platform: [tiktok | instagram | youtube] Min followers: [10K | 50K | 100K | 500K | 1M+] Budget range: IDR ___ Deliverables: ___ posts/videos in ___ days ``` **Step 2 — Source Channels** - TikTok Creator Marketplace (Indonesia filter) - Instagram brand search by hashtag: `#affiliateindonesia`, `#kontendigital`, `#jualdigital` - YouTube search by topic + subscriber filter - Internal talent database (`scripts/talent_scout.py`) - Referrals from existing active talents - Competitor agency rosters (reverse-engineer via hashtag tracking) **Step 3 — Qualify (Manual or Scripted)** Use `scripts/talent_scout.py` to score candidates: ```bash python3 scripts/talent_scout.py \ --niche digital_products \ --platform tiktok \ --min-followers 10000 \ --min-er 3.0 \ --output scouting_results.csv ``` **Step 4 — Shortlist & Prioritize** Score formula: ``` Scout Score = (ER × 0.4) + (Niche Fit × 0.3) + (Content Quality × 0.2) + (Growth Rate × 0.1) ``` - ER: normalized 0-10 (10 = ER >10%) - Niche Fit: 0 or 1 (manual assessment) - Content Quality: 1-10 (manual) - Growth Rate: % follower growth last 30 days, normalized 0-10 **Minimum Score to Proceed:** 6.0 / 10 **Step 5 — First Contact** Use outreach templates in Section 7. ### Key Indonesian Hashtags to Monitor **TikTok:** `#affiliatetiktok`, `#digitalprodukindonesia`, `#jualbuku`, `#kursusdigital`, `#tipsbisnisid`, `#penghasilantambahan`, `#kontenindonesia` **Instagram:** `#affiliateindonesia`, `#digitalmarketerindonesia`, `#jualdigital`, `#edukasibisnis`, `#contentcreatorid` **YouTube:** Search: `"affiliate shopee Indonesia"`, `"jualan digital Indonesia"`, `"bisnis online pemula Indonesia"` --- ## 4. Contract Lifecycle - Configure crm, domain, relevant, talent, this settings before first use - Review output quality and adjust parameters - Monitor performance metrics during execution - Document custom configurations for team reference - Schedule regular runs for consistent results ### Stages & Actions #### `DRAFT` - Generate contract from template (see `references/contract_template.md`) - Fill: talent name, niche, platform, deliverables, commission rate, payment terms - Internal review by Vilona / Sony #### `NEGOTIATE` - Send draft via WhatsApp or email - Track objections and counter-offers in notes field - Max 3 negotiation rounds; escalate to Paijo if stalled #### `SIGN` - Collect signed contract (PDF via WhatsApp or email scan) - Store in `contracts/{talent_id}_{date}_signed.pdf` - Update `contract_status: signed` - Set `onboarded_at` and `contract_expiry` #### `ACTIVE` - Talent receives campaign briefs - Deliverables tracked per client deal - Commission tracked per post/deal via `scripts/commission_calc.py` #### `RENEWAL` (30 days before expiry) - Auto-alert via Telegram notification - Review performance (ROI, revenue generated) - Negotiate new terms if applicable - If top performer: offer improved commission (+2-5%) #### `EXPIRY / CHURN` - Update `contract_status: expired` or `churned` - Log churn reason: `low_performance | personal | competitor | inactive | price` - Trigger win-back template if churn within first 90 days ### Standard Contract Terms ``` Commission Rate: 10-20% of deal value (default: 15%) Payment Cycle: Monthly (payout by 5th of following month) Exclusivity: Non-exclusive (unless premium deal > IDR 50M) Content Ownership: Client owns content for 1 year post-publication Minimum Deliverable: As per campaign brief Early Termination: 30-day notice required ``` --- ## 5. Commission Tracking - Configure crm, domain, relevant, talent, this settings before first use - Review output quality and adjust parameters - Monitor performance metrics during execution - Document custom configurations for team reference - Schedule regular runs for consistent results ### Commission Structure | Tier | Monthly Revenue Generated | Commission Rate | |------|--------------------------|-----------------| | Starter | < IDR 5M | 10% | | Regular | IDR 5M – 20M | 15% | | Pro | IDR 20M – 50M | 18% | | Elite | > IDR 50M | 20% | ### Deal Commission Formula ``` deal_commission = deal_value × (commission_rate / 100) talent_payout = deal_commission × talent_share # talent_share default: 0.7 agency_margin = deal_commission × (1 - talent_share) ``` **Example:** ``` Client deal value: IDR 10,000,000 Agency commission: 15% → IDR 1,500,000 Talent share: 70% → IDR 1,050,000 Agency margin: 30% → IDR 450,000 ``` ### Monthly Payout Calculation Use `scripts/commission_calc.py`: ```bash python3 scripts/commission_calc.py \ --month 2026-03 \ --output payout_march_2026.csv ``` Output columns: ``` talent_id | talent_name | deals_count | total_deal_value | commission_earned | payout_amount | payout_status ``` Payout statuses: `pending | approved | paid | disputed` ### Payout Schedule ``` 1st–5th of month: Compile commission report 6th–7th: Talent review & dispute window 8th: Paijo approves payouts 10th: Bank transfers executed (BCA / BRI / DANA / OVO) ``` --- ## 6. Performance Analytics - Configure crm, domain, relevant, talent, this settings before first use - Review output quality and adjust parameters - Monitor performance metrics during execution - Document custom configurations for team reference - Schedule regular runs for consistent results ### Talent KPIs ```yaml talent_analytics: talent_id: "" period: "YYYY-MM" posts_published: 0 total_views: 0 total_clicks: 0 total_conversions: 0 revenue_generated_idr: 0 commission_earned_idr: 0 roi: 0.0 # revenue / payout to talent content_quality_avg: 0.0 # 1-10 average from client ratings on_time_delivery_rate: 0.0 # % deliverables submitted on time client_satisfaction_avg: 0.0 # 1-10 average from clients ``` ### Creator ROI Formula ``` creator_roi = revenue_generated / payout_to_talent Example: Revenue generated: IDR 50,000,000 Payout to talent: IDR 3,000,000 ROI = 16.7x (excellent — keep, grow) Threshold: < 2x → Review / consider offboarding 2-5x → Standard performer 5-10x → Strong performer (priority campaigns) >10x → Elite (offer exclusivity deals) ``` ### Client Satisfaction Tracking After each campaign completion: ``` Survey (WhatsApp/form): 1. Content quality (1-10) 2. Talent professionalism (1-10) 3. Deliverable timeliness (1-10) 4. Results vs expectations (1-10) 5. Likelihood to rebook (1-10) Average ≥ 8: ✅ Happy client → push for repeat deal Average 6-7: ⚠️ Okay client → address friction points Average < 6: 🚨 At-risk client → immediate Sony escalation ``` ### Agency Revenue Dashboard ``` Monthly GMV: Total client deal values signed that month Active Campaign GMV: Total deal value currently in-flight Commission Revenue: Agency margin accumulated Pipeline Value: Deals in prospecting + proposal + negotiating stages Talent Count: Active signed talents Client Count: Active campaigns Avg Deal Size: Total GMV / deals count ``` --- ## 7. Outreach Templates Reusable templates for talent-crm. Standard config: ```yaml name: talent-crm_standard mode: production output: results/ format: json ``` Test config: ```yaml name: talent-crm_test mode: development dry_run: true verbose: true ``` ### Template 1: Standard talent-crm ```yaml name: talent-crm_standard mode: production output: results/ format: json ``` ### Template 2: Quick Test ```yaml name: talent-crm_test mode: development dry_run: true verbose: true ``` ### 7.1 Initial DM to Creator (TikTok/IG) **Indonesian — Short Version:** ``` Halo [Nama Creator]! 👋 Saya [Nama] dari BerkahKarya Talent Agency. Kami notice konten [niche] kamu di [platform] — bagus banget, engagement-nya solid! Kami lagi nyariin kreator untuk campaign [produk digital / lifestyle / bisnis] dengan brand-brand pilihan. Rate komisi kompetitif (10-20% per deal). Tertarik diajak ngobrol 15 menit soal peluangnya? Balas DM ini atau WA: [nomor Sony/Vilona] Cheers, [Nama] | BerkahKarya Talent Agency ``` **Indonesian — Full Version (for email):** ``` Subject: Kolaborasi Talent Agency — BerkahKarya x [Username Creator] Halo [Nama Creator], Perkenalkan, saya [Nama] dari tim BerkahKarya Talent Agency. Kami adalah talent agency yang fokus menghubungkan kreator konten Indonesia dengan brand-brand digital berkualitas. Klien kami bergerak di niche digital products, lifestyle, bisnis, dan edukasi. Kenapa kami tertarik dengan kamu: ✅ Engagement rate [X%] — di atas rata-rata industri ✅ Niche [digital/lifestyle/bisnis/edukasi] — sangat relevan dengan klien kami ✅ Konten berkualitas dengan audiens yang engaged Yang kami tawarkan: 💰 Komisi 10-20% per deal (bergantung performa & tier) 📋 Campaign management penuh dari kami — kamu fokus bikin konten 🚀 Akses ke brand-brand premium Indonesia 📊 Monthly report performa & payout rutin setiap bulan Tidak ada eksklusivitas (kamu bebas kolaborasi dengan brand lain). Kami hanya butuh 15 menit call untuk kenalan dan jelaskan sistemnya. Bisa kita set up? Balas email ini atau WA ke: [nomor] Salam, [Nama] | BerkahKarya Talent Agency [email] | [wa] ``` ### 7.2 Follow-up DM (Day 3, no reply) ``` Halo [Nama] 👋 Just checking — sempet lihat DM saya sebelumnya? Kalau timing-nya belum pas, no worries. Kalau ada pertanyaan atau mau tahu lebih lanjut, tinggal balas aja ya! BerkahKarya | Talent Agency ``` ### 7.3 Client Proposal Template ``` PROPOSAL KAMPANYE KONTEN BerkahKarya Talent Agency --- KLIEN: [Nama Brand] TANGGAL: [Tanggal] CAMPAIGN: [Nama Campaign] PERIODE: [Tanggal Mulai] – [Tanggal Selesai] --- OVERVIEW BerkahKarya menyediakan kreator konten terseleksi untuk campaign [brand]. Kami memiliki [X] talents aktif dengan total reach [X juta] followers di TikTok, Instagram, dan YouTube. TALENT YANG DIREKOMENDASIKAN | No | Nama | Platform | Followers | ER | Niche | |----|------|----------|-----------|-----|-------| | 1 | ... | TikTok | 50K | 5% | Bisnis | | 2 | ... | IG | 30K | 4% | Digital | DELIVERABLES | Platform | Tipe Konten | Jumlah | Timeline | |----------|-------------|--------|----------| | TikTok | Video (60s) | 3 | 7 hari | | Instagram | Reels (30s)| 2 | 7 hari | INVESTASI Package [X]: IDR [nilai] - Includes: [deliverables] - Excludes: paid promotion (optional add-on) TIMELINE Day 1-2: Brief & approval konten Day 3-5: Produksi konten Day 6: Review & revisi Day 7: Publish TERMS - DP 50% sebelum produksi - Pelunasan 50% setelah konten diapprove - Revisi: 2x per konten - Konten copyright 1 tahun untuk [Brand] [Tanda tangan / digital signature] Vilona | AI GM, BerkahKarya Talent Agency ``` --- ## 8. Integration - Configure crm, domain, relevant, talent, this settings before first use - Review output quality and adjust parameters - Monitor performance metrics during execution - Document custom configurations for team reference - Schedule regular runs for consistent results ### 8.1 Telegram Notifications (telegram-userbot) Configure alerts for: ```python # Notification triggers (add to scripts/commission_calc.py and talent_scout.py) NOTIFICATIONS = { "new_talent_signed": "🎉 New talent signed: {name} ({platform}, {followers} followers)", "contract_expiring": "⚠️ Contract expiring in 30 days: {name} — Renewal check needed", "contract_expired": "🚨 Contract EXPIRED: {name} — Update status immediately", "payout_ready": "💰 Monthly payout ready for approval — {count} talents, total IDR {amount}", "client_deal_closed": "✅ Deal closed: {client} — IDR {value} | Assigned talents: {talents}", "low_performance_alert": "📉 Low ROI alert: {name} — ROI {roi}x (below 2x threshold)", "new_prospect_scored": "🔍 New talent prospect scored: {name} — Score {score}/10", } TELEGRAM_CHAT_ID = "YOUR_CHAT_ID" # Set in env or config.json ``` Send via: ```bash # Using openclaw message tool (in skill context) # Or via telegram-userbot CLI if configured python3 scripts/notify_telegram.py --event new_talent_signed --data '{"name": "Creator X"}' ``` ### 8.2 Notion Database (Primary CRM) **Recommended Notion Setup:** ``` Database 1: Talents - All talent profile fields (see Section 1) - Views: All | Active | By Niche | By Platform | Expiring Soon Database 2: Clients - All client profile fields (see Section 2) - Views: Pipeline | Active Campaigns | By Deal Stage Database 3: Deals - Links: Talent(s) + Client - Fields: deal_value, commission, status, deliverables Database 4: Payouts - Monthly payout records per talent - Status: pending | approved | paid Automations: - Contract expiry → remind 30 days before - New deal closed → create deliverables checklist - Monthly trigger → generate payout draft ``` ### 8.3 Google Sheets (Backup / Reporting) Export monthly reports: ```bash python3 scripts/commission_calc.py --month 2026-03 --format sheets # Outputs: payout_2026-03.csv → manual upload to Google Sheets ``` **Recommended Sheets:** ``` Sheet 1: Talents Master List Sheet 2: Clients Master List Sheet 3: Monthly Commission Report Sheet 4: KPI Dashboard (auto-calculated) ``` --- ## 9. Scripts - Configure crm, domain, relevant, talent, this settings before first use - Review output quality and adjust parameters - Monitor performance metrics during execution - Document custom configurations for team reference - Schedule regular runs for consistent results ### `scripts/talent_scout.py` **Purpose:** Search and score talent candidates by niche, platform, followers, and engagement rate. ```python #!/usr/bin/env python3 """ talent_scout.py — BerkahKarya Talent Agency Scores and filters talent candidates from CSV/JSON input or manual entry. Usage: python3 scripts/talent_scout.py --niche digital_products --platform tiktok \ --min-followers 10000 --min-er 3.0 --input prospects.csv --output shortlist.csv Input CSV columns: name, username, platform, niche, followers, engagement_rate, avg_views, contact_wa, contact_ig, region, notes """ import argparse import csv import json import sys from datetime import datetime NICHES = ["digital_products", "lifestyle", "business", "education"] PLATFORMS = ["tiktok", "instagram", "youtube"] PLATFORM_THRESHOLDS = { "tiktok": {"min_followers": 10000, "min_er": 3.0}, "instagram": {"min_followers": 5000, "min_er": 2.0}, "youtube": {"min_followers": 2000, "min_er": 4.0}, } def normalize_er(er: float) -> float: """Normalize ER to 0-10 scale. ER >= 10% = 10.""" return min(er, 10.0) def score_talent(row: dict, niche_filter: str = None) -> dict: """Calculate scout score for a talent row.""" platform = row.get("platform", "").lower() er = float(row.get("engagement_rate", 0)) niche = row.get("niche", "").lower() followers = int(row.get("followers", 0)) growth_rate = float(row.get("growth_rate_30d", 0)) # optional # Niche fit niche_fit = 1 if (niche_filter is None or niche == niche_filter) else 0 # Content quality — default 7 if not provided (manual override encouraged) content_quality = float(row.get("content_quality", 7)) # Scout score er_score = normalize_er(er) growth_score = min(growth_rate, 10.0) scout_score = (er_score * 0.4) + (niche_fit * 10 * 0.3) + (content_quality * 0.2) + (growth_score * 0.1) # Tier if followers >= 1_000_000: tier = "mega" elif followers >= 100_000: tier = "macro" elif followers >= 50_000: tier = "mid" elif followers >= 10_000: tier = "micro" else: tier = "nano" # Platform minimum check thresholds = PLATFORM_THRESHOLDS.get(platform, {}) meets_minimum = ( followers >= thresholds.get("min_followers", 0) and er >= thresholds.get("min_er", 0) ) return { **row, "tier": tier, "niche_fit": niche_fit, "scout_score": round(scout_score, 2), "meets_minimum": meets_minimum, "qualified": meets_minimum and scout_score >= 6.0, "scored_at": datetime.now().isoformat(), } def main(): parser = argparse.ArgumentParser(description="BerkahKarya Talent Scout") parser.add_argument("--niche", choices=NICHES, help="Filter by niche") parser.add_argument("--platform", choices=PLATFORMS, help="Filter by platform") parser.add_argument("--min-followers", type=int, default=0) parser.add_argument("--min-er", type=float, default=0.0) parser.add_argument("--min-score", type=float, default=6.0) parser.add_argument("--input", "-i", help="Input CSV file (optional)") parser.add_argument("--output", "-o", default="shortlist.csv", help="Output CSV file") parser.add_argument("--json", action="store_true", help="Output as JSON") args = parser.parse_args() rows = [] # Load from file or stdin if args.input: with open(args.input, newline="", encoding="utf-8") as f: rows = list(csv.DictReader(f)) elif not sys.stdin.isatty(): rows = list(csv.DictReader(sys.stdin)) else: print("No input file provided. Run with --input prospects.csv") print("Or pipe CSV data via stdin.") sys.exit(1) # Score all rows scored = [score_talent(r, args.niche) for r in rows] # Apply filters filtered = [ r for r in scored if r["qualified"] and int(r.get("followers", 0)) >= args.min_followers and float(r.get("engagement_rate", 0)) >= args.min_er and r["scout_score"] >= args.min_score and (args.platform is None or r.get("platform", "").lower() == args.platform) ] # Sort by score descending filtered.sort(key=lambda x: x["scout_score"], reverse=True) print(f"\n✅ Qualified talents: {len(filtered)} / {len(scored)} candidates") print(f" Min score: {args.min_score} | Niche: {args.niche or 'all'} | Platform: {args.platform or 'all'}") for r in filtered[:10]: print(f" [{r['scout_score']:.1f}] {r.get('name','?')} (@{r.get('username','?')}) " f"— {r.get('platform','?')} | {r.get('followers',0):,} followers | ER {r.get('engagement_rate',0)}%") if args.json: with open(args.output.replace(".csv", ".json"), "w", encoding="utf-8") as f: json.dump(filtered, f, ensure_ascii=False, indent=2) print(f"\n📄 JSON output: {args.output.replace('.csv', '.json')}") else: if filtered: with open(args.output, "w", newline="", encoding="utf-8") as f: writer = csv.DictWriter(f, fieldnames=filtered[0].keys()) writer.writeheader() writer.writerows(filtered) print(f"\n📄 CSV output: {args.output}") if __name__ == "__main__": main() ``` --- ### `scripts/commission_calc.py` **Purpose:** Calculate monthly commission payouts for all active talents. ```python #!/usr/bin/env python3 """ commission_calc.py — BerkahKarya Talent Agency Calculates monthly commission payouts from deals data. Usage: python3 scripts/commission_calc.py --month 2026-03 --input deals.csv --output payout.csv python3 scripts/commission_calc.py --month 2026-03 --format sheets Input CSV columns (deals.csv): deal_id, talent_id, talent_name, client_name, deal_value_idr, commission_rate, talent_share, deal_date, status Status filter: only processes status = 'completed' or 'approved' """ import argparse import csv import json from collections import defaultdict from datetime import datetime TALENT_SHARE_DEFAULT = 0.70 # 70% of commission goes to talent AGENCY_SHARE_DEFAULT = 0.30 # 30% stays with BerkahKarya TIER_THRESHOLDS = [ (50_000_000, "elite", 0.20), (20_000_000, "pro", 0.18), (5_000_000, "regular", 0.15), (0, "starter", 0.10), ] def get_tier(monthly_revenue: float) -> tuple: for threshold, tier, rate in TIER_THRESHOLDS: if monthly_revenue >= threshold: return tier, rate return "starter", 0.10 def calc_payout(deal: dict) -> dict: deal_value = float(deal.get("deal_value_idr", 0)) commission_rate = float(deal.get("commission_rate", 15)) / 100 talent_share = float(deal.get("talent_share", TALENT_SHARE_DEFAULT)) deal_commission = deal_value * commission_rate talent_payout = deal_commission * talent_share agency_margin = deal_commission * (1 - talent_share) return { **deal, "deal_commission_idr": round(deal_commission), "talent_payout_idr": round(talent_payout), "agency_margin_idr": round(agency_margin), } def main(): parser = argparse.ArgumentParser(description="BerkahKarya Commission Calculator") parser.add_argument("--month", required=True, help="Month in YYYY-MM format") parser.add_argument("--input", "-i", default="deals.csv") parser.add_argument("--output", "-o", default=None) parser.add_argument("--format", choices=["csv", "json", "sheets"], default="csv") parser.add_argument("--notify", action="store_true", help="Send Telegram notification on completion") args = parser.parse_args() output_file = args.output or f"payout_{args.month}.csv" # Load deals try: with open(args.input, newline="", encoding="utf-8") as f: deals = list(csv.DictReader(f)) except FileNotFoundError: print(f"❌ Input file not found: {args.input}") print(" Create deals.csv with columns: deal_id, talent_id, talent_name,") print(" client_name, deal_value_idr, commission_rate, talent_share, deal_date, status") return # Filter by month and valid status valid_statuses = {"completed", "approved", "paid"} month_deals = [ d for d in deals if d.get("deal_date", "").startswith(args.month) and d.get("status", "").lower() in valid_statuses ] print(f"\n📊 Commission Report — {args.month}") print(f" Total deals loaded: {len(deals)} | This month (valid): {len(month_deals)}") # Calculate per deal calculated = [calc_payout(d) for d in month_deals] # Aggregate by talent talent_summary = defaultdict(lambda: { "talent_id": "", "talent_name": "", "deals_count": 0, "total_deal_value_idr": 0, "total_commission_idr": 0, "payout_amount_idr": 0, "agency_margin_idr": 0, "payout_status": "pending", }) for deal in calculated: tid = deal["talent_id"] talent_summary[tid]["talent_id"] = tid talent_summary[tid]["talent_name"] = deal.get("talent_name", "") talent_summary[tid]["deals_count"] += 1 talent_summary[tid]["total_deal_value_idr"] += float(deal.get("deal_value_idr", 0)) talent_summary[tid]["total_commission_idr"] += deal["deal_commission_idr"] talent_summary[tid]["payout_amount_idr"] += deal["talent_payout_idr"] talent_summary[tid]["agency_margin_idr"] += deal["agency_margin_idr"] # Apply tier upgrade for tid, summary in talent_summary.items(): tier, _ = get_tier(summary["total_deal_value_idr"]) summary["tier"] = tier summaries = list(talent_summary.values()) summaries.sort(key=lambda x: x["payout_amount_idr"], reverse=True) # Print summary total_payout = sum(s["payout_amount_idr"] for s in summaries) total_margin = sum(s["agency_margin_idr"] for s in summaries) total_gmv = sum(s["total_deal_value_idr"] for s in summaries) print(f"\n{'='*60}") print(f" MONTHLY GMV: IDR {total_gmv:>15,.0f}") print(f" Total Talent Payouts: IDR {total_payout:>15,.0f}") print(f" Agency Margin: IDR {total_margin:>15,.0f}") print(f" Talents to Pay: {len(summaries)}") print(f"{'='*60}") for s in summaries: print(f" [{s['tier'].upper():8}] {s['talent_name']:<25} " f"{s['deals_count']} deals | " f"IDR {s['payout_amount_idr']:>12,.0f}") # Export if summaries: if args.format in ("csv", "sheets"): with open(output_file, "w", newline="", encoding="utf-8") as f: writer = csv.DictWriter(f, fieldnames=summaries[0].keys()) writer.writeheader() writer.writerows(summaries) print(f"\n📄 Payout report: {output_file}") elif args.format == "json": json_file = output_file.replace(".csv", ".json") with open(json_file, "w", encoding="utf-8") as f: json.dump({ "month": args.month, "generated_at": datetime.now().isoformat(), "summary": { "total_gmv_idr": total_gmv, "total_payout_idr": total_payout, "agency_margin_idr": total_margin, "talents_count": len(summaries), }, "payouts": summaries, }, f, ensure_ascii=False, indent=2) print(f"\n📄 JSON payout report: {json_file}") if args.notify: print(f"\n📲 [Telegram] Payout ready for {args.month}: " f"{len(summaries)} talents | IDR {total_payout:,.0f} total") # TODO: integrate telegram-userbot notification here if __name__ == "__main__": main() ``` --- ## 10. KPIs - Configure crm, domain, relevant, talent, this settings before first use - Review output quality and adjust parameters - Monitor performance metrics during execution - Document custom configurations for team reference - Schedule regular runs for consistent results ### Primary KPIs (Monthly) | KPI | Formula | Target (Month 1) | Target (Month 6) | |-----|---------|-----------------|-----------------| | **Talents Signed** | Count (status=signed+active) | 5 | 50 | | **Active Clients** | Count (deal_stage=active) | 2 | 20 | | **Monthly GMV** | Sum of active deal values | IDR 50M | IDR 500M | | **Commission Revenue** | Agency margin total | IDR 7.5M | IDR 75M | | **Talent ROI Avg** | Avg(revenue/payout) | 5x | 8x | | **Client Satisfaction** | Avg post-campaign score | 7.5 | 8.5 | | **Contract Renewal Rate** | Renewals / Expiries | 60% | 80% | | **Time to First Deal** | Days from sign to first active deal | 14 | 7 | ### Secondary KPIs | KPI | Description | |-----|-------------| | **Pipeline Value** | Total deal value in prospecting + proposal + negotiating | | **Conversion Rate** | Prospects contacted → talents signed (target: 20%) | | **Avg Deal Size** | Total GMV / deals count | | **Churn Rate** | Churned talents / total talents | | **Content Delivery Rate** | % deliverables submitted on time | | **Revenue per Talent** | Monthly GMV / active talent count | ### KPI Review Cadence ``` Daily: Check new prospects scored, deals closed Weekly: Pipeline review (Sony), active campaign status Monthly: Full KPI report (Vilona → Paijo), payout execution Quarterly: Strategy review, tier adjustments, churn analysis ``` --- ## 11. Quick Reference — Workflow Summary ``` SCOUT → talent_scout.py → shortlist.csv OUTREACH → DM template → WhatsApp/IG ONBOARD → contract_template.md → sign → update DB CAMPAIGN → brief → produce → review → publish TRACK → commission_calc.py → monthly payout.csv PAY → approve → transfer → mark paid ANALYZE → ROI, satisfaction score, renewal decision SCALE → top performers → more campaigns → tier upgrade ``` --- ## 12. File Structure ``` skills/1ai-skills/sales/talent-crm/ ├── SKILL.md ← This file ├── scripts/ │ ├── talent_scout.py ← Scout & score candidates │ └── commission_calc.py ← Monthly payout calculator └── references/ ├── contract_template.md ← Standard contract (fill per talent) ├── prospect_template.csv ← Empty CSV for data entry └── deals_template.csv ← Empty CSV for deals tracking ``` --- *BerkahKarya Talent Agency — rebuild, scale, dominate. 🔥* *Last updated: 2026-03-13 | AI GM: Vilona* ## How to Use 1. Define ideal customer profile (ICP) and buyer personas 2. Build lead list from qualified sources 3. Craft personalized outreach sequences 4. Track engagement and follow up on signals 5. Qualify leads through discovery calls 6. Present solution tailored to pain points 7. Handle objections with value reframing 8. Close and hand off to onboarding ## When NOT to Use - Task is outside your authorization scope - You need to implement controls (use implementing-* skills) - Task is about analysis, not action (use analyzing-* skills) - You don't have access to target systems - Task requires compliance expertise (consult professionals) - Task is about defense, not offense (use defensive skills) ## Red Flags - **Lead response time > 5 minutes**: Conversion drops 80% after 5 min. Automate instant response. - **Pipeline has stale deals**: Deals stuck 30+ days need re-qualification or disqualification. - **Low email reply rates (<3%)**: Messaging is too generic. Personalize with research. - **High churn in first 90 days**: Onboarding gap. Fix handoff from sales to success. - **Discounting above 20%**: Value perception problem. Reframe ROI, don't cut price. ## Verification - Test email sequences with seed accounts before full send - Verify CRM data integrity (no duplicates, correct stages) - Check lead scoring model against actual conversion data - Confirm proposal/contract templates are current and branded - Validate payment links and checkout flow end-to-end