I've been frustrated for a long time.
Every "awesome Claude prompts" repo I found looked like this:
"Act as a senior software engineer. Be helpful, thorough,
and professional. Consider edge cases."
That's not a skill. That's a costume.
Real expertise has frameworks. Named responsibilities.
Actual formulas. Code that runs. Constraints that prevent
the model from giving you the easy wrong answer.
So I spent 6 months building what I actually wanted.
AgentOS 2.0 — 135 production-grade Claude Skills.
This article explains exactly what's inside and why it's
different from every other prompt collection on GitHub.
The Problem With Every Other Prompt Repo
Most prompt repositories fall into one of three traps:
Trap 1: The costume prompt
"You are an expert financial analyst.
Help the user with their finance questions."
Zero frameworks. Zero formulas. Zero depth.
Trap 2: The instruction dump
"When answering, always:
- Be professional
- Consider multiple angles
- Cite sources
- Format your response clearly"
This is just asking Claude to be Claude. It changes nothing.
Trap 3: The persona prompt
"You are Alex, a no-nonsense McKinsey consultant
with 20 years of experience..."
Roleplay, not expertise. The model doesn't suddenly
know DCF models because you named it Alex.
What actually works: Named sub-agents with
distinct responsibilities, actual domain formulas
in code, and explicit forbidden behaviors that
prevent hallucination in critical areas.
Here's what that looks like in practice.
What "Production-Grade" Actually Looks Like
FinanceOracle — The Apex Skill
This is the most complete skill in the repo.
Here's a fraction of what's inside:
12 Sub-Agents:
-
OptionsDesk— derivatives pricing and structuring -
MacroStrategist— macro regime analysis -
HedgeFundArchitect— strategy design -
FamilyOfficeCIO— multi-generational allocation -
TaxOptimizer— harvest and structure optimization -
DerivativesStructurer— exotic product design (+ 6 more)
Actual runnable Python:
def black_scholes(S, K, T, r, sigma, option_type='call'):
d1 = (np.log(S/K) + (r + 0.5*sigma**2)*T) / (sigma*np.sqrt(T))
d2 = d1 - sigma * np.sqrt(T)
if option_type == 'call':
price = S * norm.cdf(d1) - K * np.exp(-r*T) * norm.cdf(d2)
delta = norm.cdf(d1)
else:
price = K * np.exp(-r*T) * norm.cdf(-d2) - S * norm.cdf(-d1)
delta = -norm.cdf(-d1)
gamma = norm.pdf(d1) / (S * sigma * np.sqrt(T))
vega = S * norm.pdf(d1) * np.sqrt(T) / 100
theta = (-(S * norm.pdf(d1) * sigma) / (2 * np.sqrt(T))
- r * K * np.exp(-r*T) * norm.cdf(d2)) / 365
return {"price": price, "delta": delta,
"gamma": gamma, "vega": vega, "theta": theta}
Black-Litterman portfolio construction:
def black_litterman(Sigma, market_weights, views_P,
views_Q, views_omega, tau=0.05, delta=2.5):
pi = delta * Sigma @ market_weights
M_inv = np.linalg.inv(
np.linalg.inv(tau * Sigma) +
views_P.T @ np.linalg.inv(views_omega) @ views_P
)
mu_bl = M_inv @ (
np.linalg.inv(tau * Sigma) @ pi +
views_P.T @ np.linalg.inv(views_omega) @ views_Q
)
return {"expected_returns": mu_bl}
This isn't pseudocode. This runs.
OKREngine — Catches Failures Before They Kill Your Quarter
I've watched two startups waste entire quarters on
broken OKRs. This skill exists because of that.
The objective quality scorer:
def score_okr(objective: str, key_results: list[dict]) -> dict:
obj_score = 0
obj_score += 3 if len(objective) < 100 else 0
obj_score += 3 if not objective.lower().startswith("improve") else 0
obj_score += 4 if any(w in objective.lower() for w in
["best", "lead", "#1", "transform", "redefine"]) else 0
kr_scores = []
for kr in key_results:
kr_score = 0
kr_score += 3 if kr.get("metric") else 0
kr_score += 3 if kr.get("baseline") is not None else 0
kr_score += 4 if kr.get("target") is not None else 0
kr_scores.append({
"kr": kr["text"][:60],
"score": kr_score,
"grade": "Good" if kr_score >= 8 else "Needs work"
})
return {
"objective_score": f"{obj_score}/10",
"key_results": kr_scores,
"recommendation": "Strong OKR" if obj_score >= 8 else "Needs revision"
}
The skill also catches the 12 most common OKR failure modes — including sandbagging, health metrics disguised as OKRs, and the single most destructive mistake: tying OKR scores to bonuses.
VentureIntelligence — Term Sheet Red Flag Detector
def score_term_sheet(terms: dict) -> dict:
red_flags = []
if terms.get("liq_pref_multiple", 1) > 1:
red_flags.append(
f"Liquidation preference {terms['liq_pref_multiple']}x — above 1x is punishing"
)
if terms.get("participating_preferred", False):
red_flags.append(
"Participating preferred — VCs get paid twice in exits below threshold"
)
if terms.get("anti_dilution") == "full_ratchet":
red_flags.append(
"Full ratchet anti-dilution — catastrophic in a down round"
)
if terms.get("board_seats_investor", 0) > terms.get("board_seats_founder", 0):
red_flags.append(
"Investor has majority board control — you can be fired from your company"
)
score = 10 - (len(red_flags) * 3)
return {
"score": max(0, score),
"grade": "Sign it" if score >= 8 else "Negotiate" if score >= 5 else "Get a lawyer NOW",
"red_flags": red_flags
}
12 sub-agents including TermSheetDecoder, ValuationNegotiator, ChampionDeveloper, and BoardRelationshipManager.
CrisisIntelligence — War Room OS
Every company will face a crisis. Almost none prepare.
def classify_crisis(crisis: dict) -> dict:
severity_score = 0
if crisis["customer_impact_pct"] >= 0.5: severity_score += 30
if crisis["revenue_at_risk"] >= 1_000_000: severity_score += 20
coverage = {"none": 0, "local": 5, "national": 15, "viral": 30}
severity_score += coverage.get(crisis["media_coverage"], 0)
if crisis["regulatory_involvement"]: severity_score += 15
if crisis["legal_liability"]: severity_score += 15
if severity_score >= 70:
level = "CRITICAL (P0)"
action = "CEO leads. War room activated NOW."
elif severity_score >= 40:
level = "HIGH (P1)"
action = "VP-level lead. External comms needed."
else:
level = "MEDIUM (P2)"
action = "Director-level. Monitor externally."
return {
"level": level,
"immediate_action": action,
"time_to_first_response": "1 hour" if severity_score >= 70 else "4 hours"
}
The 5Rs framework (Recognize → Respond → Responsible → Remediate → Restore) is built into every communication template.
How It Works (60-Second Setup)
Claude.ai Projects:
# 1. Open Claude.ai → Projects → New Project
# 2. Paste SKILL.md into "Project Instructions"
# 3. Start chatting
Claude Code:
cat finance-oracle/SKILL.md >> .claude/CLAUDE.md
Claude API:
import anthropic
with open("startup-cto/SKILL.md", "r") as f:
skill = f.read()
client = anthropic.Anthropic()
response = client.messages.create(
model="claude-sonnet-4-6",
max_tokens=4096,
system=skill,
messages=[{"role": "user", "content": "Audit our tech stack decision"}]
)
That's it. Claude is now that specialist.
The Full 135-Skill Index
🚀 Startup & Team Management (11)
startup-cto team-performance-os startup-hiring-machine
culture-architect remote-team-commander okr-engine
startup-finance-controller venture-intelligence
startup-legal-shield talent-management-os talent-brand-builder
🏆 Apex Legendary (4)
finance-oracle claude-mythos ceo-war-room founder-to-ceo
🤖 AI & Engineering (14)
rag-architect mlops-engineer system-architect senior-dev
ai-red-teamer voice-agent-builder knowledge-graph-builder
incident-commander mcp-builder agentic-workflow-builder
api-integrator realtime-data-agent agent-smith prompt-engineer
📊 Data & Analytics (10)
data-scientist-pro sql-analyzer data-pipeline-pro
business-intelligence-pro timeseries-oracle quant-trader
synthetic-data-generator arxiv-researcher abtest-scientist
data-governance-agent
💹 Finance (9)
finance-oracle financial-model-builder cfo-intelligence
portfolio-optimizer quant-researcher saas-metrics-analyst
insurance-actuary ma-dealmaker risk-sentinel
🏢 Operations & Business (20)
ceo-war-room founder-to-ceo go-to-market-commander
enterprise-sales-os sales-enablement-os board-deck-builder
crisis-intelligence partnership-intelligence pricing-strategist
project-command marketing-os supply-chain-oracle (+ 8 more)
👤 Product & Customer (11)
product-roadmap-os sprint-master engineering-manager
ai-product-manager user-research-os customer-interview-analyst
product-analytics-os network-effects-analyst marketplace-strategist
performance-marketing-os churn-analyst
🛠 Developer Tools (19)
developer-experience-os api-design-architect data-warehouse-architect
cloud-cost-optimizer design-system-architect technical-pm
code-reviewer load-tester code-migrator webapp-tester (+ 9 more)
🌐 Specialized Domains (12)
healthcare-analytics web3-developer climate-tech-analyst
biotech-analyst cybersecurity-analyst real-estate-intelligence
legal-eagle patent-analyst esg-compass (+ 3 more)
What Makes This Different From Every Other Repo
| Feature | Generic repos | AgentOS 2.0 |
|---|---|---|
| Sub-agents | ❌ | ✅ 10-12 per skill |
| Actual formulas | ❌ | ✅ Black-Scholes, DCF, MEDDPICC |
| Runnable code | ❌ | ✅ Python, TypeScript, Go, Shell |
| Forbidden behaviors | ❌ | ✅ Every skill |
| Benchmark data | ❌ | ✅ Industry standards built in |
| Total skills | ~10-20 | 135+ |
Try It Right Now
The fastest way to understand the depth is to try one.
I recommend starting with okr-engine or startup-cto — they're the most complete and immediately useful regardless of what you're building.
Paste the SKILL.md into Claude Projects. Ask it to review your current OKRs or tech stack. You'll see the difference immediately.
GitHub link in the comments.
What skill would you build your work around? Drop it below — I read every comment and I'm actively building more.
MIT License. Free forever. Star it if it's useful — helps others find it.