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Analyse consequential choices with outside views, incentives, inversion, scenario arithmetic, opportunity costs, and explicit update conditions. Use for decision memos, premortems, strategy choices, or a requested red-team; not routine factual questions or a reason to overanalyse a small decision.

Use this Skill: https://skilld.dev/gh/tristanmanchester/agent-skills/wordly-wisdom

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referencesmodel-latticework.md

≈1k tokens on demand. Your agent reads this file only when SKILL.md points to it.

Model Latticework

Use this file to choose the smallest useful set of models for the specific case. Usually 4 to 8 is enough.

Selection rule

Do not ask, "Which models do I know?" Ask, "Which models will materially change the answer?"

Mathematics and decision theory

Base rates

Use when the case belongs to a recognisable class.

Ask:

  • What usually happens in situations like this?
  • How often does this kind of plan fail, stall, or outperform?

Expected value

Use when there are discrete scenarios and payoffs.

Ask:

  • What are the plausible scenarios?
  • What is the probability-weighted payoff?

Opportunity cost

Use almost always.

Ask:

  • Compared with what?
  • What is the best alternative use of the same time, money, or reputation?

Compounding

Use when repeated small advantages or mistakes matter over time.

Ask:

  • What accumulates here?
  • Which path creates future optionality or future fragility?

Engineering and systems

Redundancy / backup systems

Use when failure is costly.

Ask:

  • What happens if the key assumption breaks?
  • Where is the backup?

Bottleneck

Use when outcomes depend on one scarce constraint.

Ask:

  • What single constraint governs throughput or quality?
  • What happens if that constraint worsens?

Breakpoint / critical mass

Use when effects are non-linear.

Ask:

  • Is there a threshold after which the result changes sharply?
  • Are we below the threshold, near it, or through it?

Feedback loops

Use when behaviour changes behaviour.

Ask:

  • What self-reinforcing loop is being created?
  • Is the loop stabilising or destabilising?

Economics and business

Incentives

Use always when people are involved.

Ask:

  • What is each actor rewarded for?
  • What are they likely to optimise in practice rather than in theory?

Comparative advantage / specialization

Use when deciding who should do what.

Ask:

  • Who has the real edge?
  • Is this task sitting with the highest-value actor?

Moats / durable advantage

Use for competitive questions.

Ask:

  • Why is this defensible?
  • What would stop a rival copying, undercutting, or displacing it?

Scale advantages and disadvantages

Use when growth is central.

Ask:

  • Does scale improve economics, data, distribution, or trust?
  • Or does scale mainly create bureaucracy and slop?

Psychology

Social proof

Use when crowd behaviour is salient.

Ask:

  • Are people believing this because it is true or because others believe it?

Inconsistency avoidance / sunk cost

Use when commitment is already high.

Ask:

  • Is the user defending the past instead of choosing afresh?

Deprival-superreaction

Use when near-loss, actual loss, or fear of shrinking matters.

Ask:

  • Is the desire to avoid loss stronger than the rational case for the next step?

Overoptimism

Use in plans, forecasts, and entrepreneurship.

Ask:

  • Which costs, timeframes, or difficulties are being understated?

Stress influence

Use when timing is ugly or pressure is high.

Ask:

  • How is stress distorting judgement, courage, or honesty?

Biology and adaptation

Evolution / selection

Use when systems reward what survives, not what sounds good.

Ask:

  • Which behaviours get selected over time by this environment?
  • Does the system breed honesty or gaming?

Adaptation

Use when the environment is changing.

Ask:

  • What happens if the environment moves faster than the plan?

History

Vicarious learning

Use always when there are precedents.

Ask:

  • Who has already made this mistake?
  • Which prior case is structurally similar, not merely superficially similar?

Practical guidance

  • If the user is choosing between options, opportunity cost and incentives are usually mandatory.
  • If the user is forecasting, use base rates, overoptimism, and feedback loops.
  • If the case is political, social, or organisational, social proof, authority, and incentives dominate more often than technical elegance.
  • If the case is emotionally loaded, add stress influence, deprival-superreaction, and identity-linked distortions.

Source: SKILL.md on GitHub

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2 warnings1mo5 checks · Risk MEDIUM
  • Gen Agent Trust Hub1mo

    The skill provides a framework for decision analysis using mental models and local Python scripts. It is flagged as medium risk due to metadata poisoning, as it claims to be authored by 'openai' while originating from an unrelated developer 'tristanmanchester'. Additionally, the skill's ingestion of untrusted user data for decision memos and script processing creates a surface for indirect prompt injection and potential arbitrary file writes.

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  • Snyk1mo

    Risk: LOW · No issues

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  • ZeroLeaks6mo

    Score: 93/100 · 2 sections analyzed

Signed by skilld at 3323bc9. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 2 weeks ago.

Steadyupdated last month
Other metadata
compatibility
Core reasoning needs no special tools. Optional arithmetic helpers require Python 3.10+ and use only the standard library. Time-sensitive or specialised claims require current relevant sources.
metadata
{
  "version": "4.0.0",
  "reviewed": "2026-09-13",
  "source-book": "Poor Charlie's Almanack"
}

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