Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add deciqAI/knowledge-skills --skill lindy-effectgit clone --depth 1 https://github.com/deciqAI/knowledge-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/deciqai/knowledge-skills/lindy-effect)<a href="https://agentmods.dev/skills/deciqai/knowledge-skills/lindy-effect"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/lindy-effect/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/deciqai/knowledge-skills/lindy-effect"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/lindy-effect.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00119 | $0.01979 |
| Opus 5 | $0.00060 | $0.00989 |
| Sonnet 5 | $0.00024 | $0.00396 |
| Haiku 4.5 | $0.00012 | $0.00198 |
Grade A, and why
lindy-effect scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Lindy Effect
Overview
For non-perishable items — ideas, books, technologies, institutions, practices — life expectancy is proportional to current age. The longer something has survived competitive elimination, the longer its expected remaining life. Math: if survival follows a Pareto distribution with α ≈ 1, expected remaining life ≈ current age. Not nostalgia — statistical inference from a track record of passing elimination tests.
Composes with antifragile (Lindy-survival is the signature of antifragility), survivorship-bias (paired warning), first-principles (Lindy says that; first-principles says why), switching-costs, and chestertons-fence.
When to Use
- Choosing a foundational technology / library / framework with high switching cost
- Evaluating a long-established practice or institution someone is proposing to discard
- Assessing a new methodology being marketed as "modern" or "evidence-based"
- Allocating reading time across old vs. new books
- Designing institutional partnerships with multi-decade horizons
- Deciding which layers of an AI stack to build on time-tested foundations (SQL, Unix, TCP/IP) vs. fast-churning AI frameworks amid the AI adoption hype
- Someone says "this has stood the test of time," "we should modernize," "this time is different"
Not when: item is perishable or has a deterministic life cycle; elimination forces are absent (old institution survives only via regulatory protection); conditions have genuinely changed enough to invalidate survival evidence; decision time-horizon is too short for long-run durability to matter.
Coaching Novices (Adaptive Front Door)
- Engine mode: user has a concrete old-vs-new decision → run The Process directly.
- Coach mode: user unfamiliar or no concrete case → guide step by step.
In Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 124 lines · 119 tokens per session scan A 2b1288478aa4
lindy-effect is a skill published in the GitHub repository deciqAI/knowledge-skills (10 stars, last pushed 10d ago), licensed MIT. It adds 119 tokens to every session and 1,979 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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