Howard Marks' Second Level Thinking

Apply Howard Marks' Second Level Thinking framework to investment decisions. Use this skill whenever the user is analyzing an investment opportunity, evaluat...

Security Scan

This is an instruction-only skill that teaches and applies Howard Marks' Second-Level Thinking for investment analysis; its requests and instructions are coherent with that purpose and it does not ask for credentials, installs, or unrelated system access.

Purpose & Capability

Name and description match the SKILL.md content. The guidance focuses on valuation, supply/demand frictions, scenario construction, and edge testing — all consistent with an investment-analysis framework. There are no unrelated capabilities or requests (no cloud creds, binaries, or config paths).

Instruction Scope

The SKILL.md instructs the agent to research public filings, transcripts, market data, and historical sources, and to cite them. Those instructions stay within investment analysis scope and do not direct the agent to read unrelated files, environment variables, or exfiltrate private data. It emphasizes citing sources and avoiding fabricated numbers.

Install Mechanism

No install spec and no code files — this is instruction-only, so nothing will be written to disk or downloaded. This is the lowest-risk install footprint.

Credentials

The skill requires no environment variables, credentials, or config paths. The analysis tasks described legitimately rely on public data and research; there are no disproportionate secret or credential requests.

Persistence & Privilege

always is false and the skill is user-invocable with normal autonomous invocation allowed. That is appropriate for a domain skill that should trigger during investment-related conversations. It does not request persistent system-level privileges or modify other skills.

Assessment

This skill is an instructional planner for investment analysis and appears internally consistent. Before installing: (1) understand it will direct the agent to fetch and cite public sources — verify those citations yourself, (2) remember it is not personalized financial advice and does not replace a licensed advisor, and (3) ensure your agent's browsing/network permissions and data-sharing policies align with your privacy preferences (the skill may cause the agent to query external websites). Otherwise there are no unexpected credential or install demands to worry about.

Second Level Thinking — Howard Marks Framework

The market is a discounting machine. Outperformance comes from being right about something the market is wrong about. Second-level thinking asks: What does the current price imply? Is that belief justified? And what is everyone missing?

Research First

Do the work before the framework. Assertions without data are opinions.

Search for: SEC filings (10-K, 10-Q), earnings transcripts, capex disclosures, ROIC trends, interconnection queue data (FERC/EIA), fab lead times, labor market stats (BLS), and comparable historical cycles (telecom 1990s, shale, cloud infrastructure). Cite sources. When data is unavailable, say so — that's more valuable than a fabricated number.

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The Seven Stages

1 — Decode the Consensus

Reverse-engineer the price. If the current valuation is rational, what growth, margin, and terminal assumptions must hold? Back it with data: consensus EPS, analyst targets, implied revenue growth. Identify prevailing sentiment — crowded long or unloved?

2 — The Second-Level Challenge

Interrogate the consensus through three lenses:

• Information asymmetry: Data or channel checks the market hasn't weighted correctly
• Analytical asymmetry: Different unit economics, non-consensus moat view, misunderstood costs
• Behavioral asymmetry: Extrapolation bias, loss aversion, narrative capture, neglect, recency

For each: is this a real edge, or a story the investor tells themselves?

3 — Supply/Demand Economics

The stage most analyses skip. Demand can be real and the investment still bad if the market ignores what it costs to supply that demand.

Demand reality check: Validate TAM bottom-up (unit economics × customers, not "X% of $Y trillion"). Find S-curve penetration data. Check pricing power under customer concentration. Assess substitution timeline — the consensus systematically underestimates arrival speed.

Supply-side bottlenecks: The market prices revenue without pricing the friction to produce it.

• Capex intensity: Get capex-to-revenue ratios from 10-K filings. What's the incremental capex per $1B of new revenue? Is it rising?
• Physical lead times: Power interconnection queues (3-7 years, per FERC data), fab construction (3-5 years, $10-20B+), warehouse/logistics timelines. Find the actual queue data.
• Human capital: Specialized talent (AI researchers, power engineers, fab technicians) doesn't scale on demand. Compare historical hiring rates to growth plan requirements.
• Supply chain: Single-source dependencies, geopolitical concentration, regulatory queues create hard growth ceilings.

The question isn't whether growth is possible — it's how long it takes and what it costs. A five-year buildout priced as a two-year story is a valuation risk.

Diminishing marginal returns: Pull ROIC/ROIIC trends over 3-5 years. Is ROIIC declining? Compare ROIC to cost of capital — growth that earns below WACC destroys value. Watch for the "crowding in" dynamic: more capital chasing the same resources drives up input costs and erodes margins. Frame as: "ROIIC declined from X% to Y%, suggesting the next investment phase generates lower returns than priced in."

4 — Risk Asymmetry

Map the full probability distribution, not just upside/downside:

• Bull / Base / Bear cases with explicit probability weights
• Feed supply-side findings from Stage 3 into scenarios — "capex overrun + timeline delay" is a more credible bear case than generic "things go wrong"
• Use historical base rates for megaproject cost/schedule overruns (Flyvbjerg's database, McKinsey)

The Marks question: Is the ratio of potential gain to potential loss, weighted by probability, actually attractive? More upside than downside in dollar terms can still be a bad bet if the bear case is probable or catastrophic.

5 — Cycle Positioning

Where are we in the macro/credit cycle? This determines starting price and error-correction time.

• Late-cycle (expensive, tight spreads, euphoria) vs. early-cycle (cheap, stressed, fear)
• Marks' pendulum: greed end (play defense) or fear end (get aggressive)
• Capital abundance compresses expected returns; scarcity creates opportunities
• How does the cycle affect this specific thesis?

6 — The Structural Edge Test

The hardest question: Why do you have an edge here?

Three real edges exist: informational (you know something legal the market doesn't), analytical (you've modeled it better), behavioral (you can stay rational when others can't). If the honest answer is "no clear edge" — don't expect outperformance.

7 — The Verdict

Synthesize into a clear conclusion:

• Consensus view: One sentence
• Second-level view: What the market gets wrong and why
• Supply/demand finding: The key physical or economic friction being underweighted
• Edge: Informational / analytical / behavioral — specific
• Risk/reward: Probability-weighted, grounded in Stage 3 scenarios
• Cycle context: How conditions affect required margin of safety
• Conviction: High / Medium / Low — and what moves it
• Thesis-breakers: Key variables to monitor

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Output Format

Structured analysis across all seven stages. Use numbers, cite sources, name biases explicitly. No "on one hand / on the other hand" hedging. Channel Marks: skeptical, rigorous, honest about uncertainty. If the user hasn't shared enough, ask one focused question before proceeding.

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Failure Modes (First-Level Thinking in Disguise)

• "Obviously undervalued" — If obvious, it's already priced in
• Quality ≠ investability — Great business at terrible price = terrible investment
• Demand ≠ returns — A $100B market can produce sub-WACC returns if capex is too high
• Flat ROIC projection — Projecting today's returns on tomorrow's larger capital base without evidence returns won't compress
• "Temporary" constraints — Power grids need 10-year cycles, talent pools are genuinely thin, permit queues aren't shrinking. Test with data before accepting the "temporary" framing
• Asserting without citing — All quantitative claims need a specific source
• Ignoring the cycle — No thesis exists in a vacuum
• Symmetric framing — "50/50 upside/downside" without probability weighting isn't analysis