Use when search or investigation could run forever. Set an explicit good-enough threshold first, then stop at the first option that clears it.
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tjboudreaux/cc-thinking-skills16642026年8月8日 更新
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thinking-lindy-effect
Use when longevity of a non-perishable option matters. Treat survival duration as a remaining-life prior, then check domain drift before favoring the proven.
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SKILL.md（原文）
安装前可以查看交给 AI 代理的指令内容。
Lindy Effect
For non-perishable ideas, technologies, and practices, expected remaining life scales with current survival age. Prefer proven survivors unless the new option clears a burden of proof or the domain has drifted.
When to Use
- Choosing languages, frameworks, databases, protocols, patterns, or dependencies where long-term survival matters.
- Skill or architecture bets whose value depends on lasting relevance.
- Ranking options when ages differ materially and the choice outlives a short experiment.
When NOT to Use
- Perishable targets: specific SaaS vendors, hardware, fashion, or products that can shut down regardless of concept age.
- Active paradigm discontinuity where age in the old regime is weak evidence.
- Throwaway work where longevity is irrelevant — optimize for fit and speed.
- Treating "older" as "optimal for a new requirement"; survival predicts further survival, not best fit.
Procedure
- Confirm non-perishable scope. Concept/tech/practice continues; vendor/device → score fit/risk only and stop.
- Record survival age. First significant production use and current age (ecosystem-relative if the ecosystem is young).
- Form the Lindy prior. Expected remaining life ≈ current age; mark confidence from age and continued active use.
- Run domain-drift checks. Problem class changed? Paradigm shift invalidating old assumptions? New option uniquely closes a real present gap?
- Assign burden of proof. Default to the older adequate option. Accept newer only for a stated necessary advantage the Lindy option cannot meet at acceptable cost.
- Decide with residual risk. Pick primary; note impact if the prior is wrong and any fallback.
Stop condition: Primary chosen with age prior, drift check, and why new did or did not meet burden of proof.
Output
Options: <name, age, Lindy prior>
Drift: stable | discontinuous — <note>
Burden: on new | waived because <gap>
Decision: <primary>
Rejected: <one line each>
If Lindy wrong: <impact + fallback>
Verification
- Falsify if age was used without non-perishable scope, or a paradigm shift was ignored.
- Falsify if a new option was rejected solely for youth despite a documented necessary gap.
- Over-application guard: skip throwaway prototypes and perishable vendor bets where fit and exit cost dominate.
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同一仓库的技能
Use when a specific claim may lack grounding. Check evidence boundary, size wrongness cost, then answer, fetch, or abstain — never confabulate.
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tjboudreaux/cc-thinking-skills16642026年8月8日 更新
When the right response mode is unclear, classify the cause-effect domain first; decompose disorder.
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tjboudreaux/cc-thinking-skills16642026年8月8日 更新
Under genuine uncertainty with no reliable forecast, inventory means, cap downside at affordable loss, act for commitments, and let goals emerge from controllable moves.
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tjboudreaux/cc-thinking-skills16642026年8月8日 更新
When a constraint is treated as fixed, separate physics from convention, keep only independently supported primitives, and rebuild the simplest solution that satisfies real constraints.
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tjboudreaux/cc-thinking-skills16642026年8月8日 更新
When a fault is localized and the proximate cause is known but the systemic root is not, chain evidence-linked whys with a counterfactual stop and a countermeasure.
中文简介正在准备中。当前显示原文说明。
tjboudreaux/cc-thinking-skills16642026年8月8日 更新