# Awesome-Claude-Skills I built 135 Claude Skills with real formulas. Here's what "production-grade" actually means. > Source: > Published: 2026-05-21 03:16:43+00:00 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:** ``` 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:** ``` python 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:**``` php 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 ``` php 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. ``` php 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:**``` python 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**— they're the most complete and immediately useful regardless of what you're building.**`startup-cto` 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.*