Skip to main content
skill CX Codex

thinking-lindy-effect — Claude Code skill: pick tech that lasts

A skill that turns a technology's age into an argument for the proven option. Useful when choosing a language, framework or database for the long run.

Install into Claude Code · Skills CLI (Vercel) All methods and platforms ↓
npx skills add tjboudreaux/cc-thinking-skills --skill thinking-lindy-effect -a claude-code
Open on GitHub

What thinking-lindy-effect does

The skill applies the Lindy effect: for non-perishable things, expected remaining life scales with how long they have already survived. The agent records the survival age, forms a prior from it, and only then checks whether the domain has drifted.

It runs as a procedure. First it confirms the target is non-perishable — a concept, technology or practice. Vendors and devices fall outside; for those it scores fit and risk only. Then it records the first significant production use and current age, forms the prior, runs the drift checks, and assigns a burden of proof to the newer option.

The output is a block listing options with ages and priors, a drift note, the decision, and one line per rejected option. It also states the impact if the prior is wrong and any fallback. Throwaway prototypes and bets on specific SaaS vendors are out of scope, because fit and exit cost dominate there. And age predicts further survival, not best fit for a new requirement — worth checking before you let an old habit answer a fresh question.

What it helps with

  • pick a language or framework you won't have to replace next year
  • separate a long-lived technology from a vendor product
  • justify the proven option to your team
  • spot a paradigm shift that weakens old evidence
  • name a fallback in case the bet does not pay off

Example prompts for the agent

  • Help me choose a database for a service that will run for years — apply the Lindy effect
  • Compare these two frameworks by expected remaining life and check for domain drift
  • New tool versus the proven one: assign the burden of proof and decide

How to install thinking-lindy-effect

1Skills CLI (Vercel)

npx skills add tjboudreaux/cc-thinking-skills --skill thinking-lindy-effect -a claude-code

2Manually: put the files in place

~/.claude/skills/thinking-lindy-effect/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/thinking-lindy-effect/

Step by step, with commands for each platform: How to install skills in Claude Code: five ways

Questions about thinking-lindy-effect

How do I install thinking-lindy-effect in Claude?

Run npx skills add tjboudreaux/cc-thinking-skills --skill thinking-lindy-effect -a claude-code. The skill lands at ~/.claude/skills/thinking-lindy-effect/SKILL.md.

What does thinking-lindy-effect do?

It derives expected remaining life from a technology's current age and checks whether the problem domain has shifted. The agent then favors the proven option or accepts the newer one with a stated reason.

When should I not use this skill?

For perishable targets: specific SaaS vendors, hardware, or products that can shut down regardless of how old the idea is. Also for throwaway prototypes, where fit and speed matter more.

Source text

SKILL.md · MIT
---
name: thinking-lindy-effect
description: 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.
disable-model-invocation: true
---

# 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

1. **Confirm non-perishable scope.** Concept/tech/practice continues; vendor/device → score fit/risk only and stop.
2. **Record survival age.** First significant production use and current age (ecosystem-relative if the ecosystem is young).
3. **Form the Lindy prior.** Expected remaining life ≈ current age; mark confidence from age and continued active use.
4. **Run domain-drift checks.** Problem class changed? Paradigm shift invalidating old assumptions? New option uniquely closes a real present gap?
5. **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.
6. **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

```text
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.

© tjboudreaux · MIT · tjboudreaux/cc-thinking-skills

Similar skills

All skills for Agent setup: memory, context, orchestration →

From the same set27

thinking-jobs-to-be-done Works out the progress a user hires a product for, then ranks features by it. Useful when you cannot tell what to cut and what to grow. CX Codex +1 568installs thinking-cynefin Sorts a problem into a Cynefin domain by cause and effect, then names the response mode: runbook, analysis, probe or stabilize. Useful when a method keeps failing. CX Codex +1 567installs thinking-bounded-rationality A skill that halts a search at the first option clearing a threshold you set beforehand, instead of comparing forever. CX Codex +1 563installs thinking-model-combination A skill that sequences 2–3 mental models with named roles when one model leaves a blind spot. Built for multi-domain decisions. CX Codex +1 563installs thinking-thought-experiment A skill for when a real test is too rare, large or irreversible: the agent runs one counterfactual with an isolated variable and bounds what the result implies. CX Codex +1 560installs thinking-map-territory The skill makes the agent check live code or data whenever a doc, test, metric or assumption disagrees with observed behavior. CX Codex +1 559installs thinking-effectuation A skill for decisions under genuine uncertainty: inventory your means, then take one step inside an affordable-loss cap. Useful when forecasts can't be trusted. CX Codex +1 556installs thinking-kepner-tregoe A skill for narrowing a defect with IS/IS-NOT analysis and for picking between options with MUST screening, weighted WANTs and downside checks. CX Codex +1 557installs

Project sponsors

Companies that keep this analytics open
Gold sponsor

Impressions, clicks and code copies — open statistics. We keep the numbers public for the same reason NeBlask itself is public. Become a sponsor