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/wordly-wisdom

@3323bc9
by Tristan Manchestertristanmanchester/agent-skills3 stars
2
Run it yourself

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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referencesuse-cases-and-examples.md

≈684 tokens on demand. Your agent reads this file only when SKILL.md points to it.

Use Cases and Examples

These are compact examples. They are not scripts to follow mechanically.

Example 1: Leave a stable job for a startup?

Likely mode

Oracle Review

Crux questions

  • How much runway exists?
  • What is the quality of the people and incentives?
  • Is the upside real or just envy-driven comparison?
  • Is the decision reversible?

Models likely to matter

  • opportunity cost
  • incentives
  • overoptimism
  • social proof
  • margin of safety
  • compounding of career capital

Good answer shape

  • state the decision and runway
  • build the outside view for similar startups
  • test whether the role compounds skill and network even if the company fails
  • inspect founder quality and equity terms
  • recommend join, wait, or decline
  • state what would change the view

Example 2: Ship the product now or delay?

Likely mode

Decision Memo or Quick Take

Crux questions

  • What is the cost of delay?
  • What is the cost of a bad launch?
  • Which failure modes are reputational versus fixable?
  • Are we succumbing to sunk cost, fear, or social proof?

Models likely to matter

  • bottleneck
  • feedback loops
  • incentives
  • stress influence
  • redundancy
  • second-order effects

Good answer shape

  • define the launch threshold
  • distinguish fatal defects from annoying defects
  • map incentives pushing either haste or caution
  • run a premortem on the first 30 days after launch
  • recommend ship, stage, or hold

Example 3: Evaluate a proposed acquisition or partnership

Likely mode

Decision Memo

Crux questions

  • Is there a real moat or only a story?
  • What incentives survive integration?
  • What hidden liabilities exist?
  • What second-order effects appear after acquisition?

Models likely to matter

  • incentives
  • opportunity cost
  • scale advantages and disadvantages
  • social proof
  • denial
  • lollapalooza combinations

Good answer shape

  • kill the obvious bad structure early
  • check character and incentive alignment
  • inspect hidden exposures
  • model upside, downside, and reversibility
  • recommend proceed, renegotiate, or walk away

Example 4: Relationship or personal life decision

Likely mode

Oracle Review with softer tone

Crux questions

  • What is the real objective?
  • Which feelings are informative and which are distorting?
  • Is the user trying to avoid short-term discomfort at long-term cost?
  • What would a misery-avoidance inversion say?

Models likely to matter

  • inversion
  • envy / jealousy
  • liking / loving
  • stress influence
  • fairness
  • opportunity cost

Good answer shape

  • define the actual decision
  • acknowledge emotion without surrendering judgement to it
  • invert to likely sources of misery
  • recommend one honest next step

Source: SKILL.md on GitHub

Skill behaviors

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Third-party checks

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.

  • Socket1mo

    No alerts

  • Snyk1mo

    Risk: LOW · No issues

  • Runlayer7mo

    1/16 files flagged

  • ZeroLeaks6mo

    Score: 93/100 · 2 sections analyzed

Provenance

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

Capability

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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