| name | talent-crm |
| description | Use when managing talent pipeline with candidate tracking, outreach automation, and interview scheduling. Use for recruiting CRM. |
| domain | sales |
| author | oyi77 |
| license | Apache-2.0 |
| subdomain | sales |
| tags | ["business-development","crm","pipeline","revenue","sales","talent"] |
| version | 1.0.0 |
talent-crm
When to Use
Trigger phrases:
- "talent crm"
- "Help me with talent crm"
Use cases:
- When the task matches this skill's domain expertise
When NOT to use:
- For tasks outside this skill's scope
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
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, SaaSlifestyle — daily vlog, OOTD, beauty, foodbusiness — entrepreneur, side hustle, finance tipseducation — 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
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
Client Profile Fields
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
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:
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
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
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:
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
Talent KPIs
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:
name: talent-crm_standard
mode: production
output: results/
format: json
Test config:
name: talent-crm_test
mode: development
dry_run: true
verbose: true
Template 1: Standard talent-crm
name: talent-crm_standard
mode: production
output: results/
format: json
Template 2: Quick Test
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
8.1 Telegram Notifications (telegram-userbot)
Configure alerts for:
# 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:
# 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:
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
scripts/talent_scout.py
Purpose: Search and score talent candidates by niche, platform, followers, and engagement rate.
#!/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.
#!/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
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
- Define ideal customer profile (ICP) and buyer personas
- Build lead list from qualified sources
- Craft personalized outreach sequences
- Track engagement and follow up on signals
- Qualify leads through discovery calls
- Present solution tailored to pain points
- Handle objections with value reframing
- 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
Process
- Analyze the task requirements
- Apply domain expertise
- Verify output quality
Anti-Rationalization Table
| Rationalization | Reality |
|---|
| "Cold outreach does not work" | It works when personalized and targeted. Generic spam does not. |
| "I will follow up later" | 80% of sales require 5+ follow-ups. Follow up consistently. |
| "Price is the only factor" | Value, trust, and timing matter more than price. Sell outcomes. |
