Why Agent Skills Are Doomed to Become Obsolete

What Exactly Is a Skill?

Think of a skill as an SOP written for an intern: a step‑by‑step guide that tells the AI how to handle a specific situation, including decision points and edge cases. The author stresses that a skill exists only because a human has pre‑thought the process for the model.

Key insight: If a model someday can think for itself, the need for such pre‑written SOPs disappears.

The Script Has Played Out Three Times

The author presents a timeline of past “AI hacks” that followed the same pattern: humans create a wrapper, models learn it, the wrapper becomes redundant, and a new wrapper emerges.

2022 – CoT (Chain‑of‑Thought) prompt templates; later superseded by OpenAI o1 and DeepSeek‑R1 internal reasoning.

2020‑2023 – Few‑shot example templates; lost value as zero‑shot capabilities improved.

2023 – Function‑calling tutorials; rendered obsolete when major models added native tool‑calling.

2024 – .cursorrules files; within a year Cursor rewrote the rules, sparking community debate about their relevance.

2025 – Agent Skills / SKILL.md (future unknown).

Skills Are Premium Data, Not Ordinary Corpus

Training data comes in three scarcity levels:

Abundant : raw text from the web, books, forums – virtually unlimited.

Scarce : result‑oriented data such as Q&A pairs, code, annotated QA – limited.

Extremely scarce : process data that includes full execution flows, decision points, and boundaries – very few.

Skills belong to the “extremely scarce” tier: they are structured, high‑quality demonstrations of human expertise, which are the most valuable inputs for SFT and RLHF training.

Open Source Is Unpaid Labor

The author draws a parallel with Stack Overflow. After ChatGPT’s launch in 2022, SO’s question volume dropped sharply. In 2024 the platform licensed its content to OpenAI and Google, turning years of volunteer contributions into model training data. The same closed loop applies to skills, but the loop is faster because skills are already in a model‑ready format.

Human contribution → Platform aggregation → Vendor acquisition → Model training → Community collapse → Humans continue contributing residual value.

Thus, contributors to the skill ecosystem become unpaid annotators for model vendors.

The Real Threat: Sub‑Optimality

Even if new skills keep being written, their impact will shrink. Most new problems are variants of existing ones, and models already possess the core capability. Moreover, a model that has absorbed millions of skills and real execution results can discover solutions that surpass any single human‑crafted skill.

Consequences: Your skill may become a sub‑optimal solution that the model already knows a better way to achieve.

Will Skills Disappear Completely?

Skills will not vanish entirely; they will retreat to three strongholds:

Private – internal company workflows.

Timely – up‑to‑date API calls and regulatory‑compliant decision chains.

Compliance – auditable processes required by law.

These niches retain value because models cannot instantly acquire them, but they represent only a small fraction of today’s skill market. The bulk of generic, publicly shared skills will become long‑tail supplements.

Final Takeaways

It is still worthwhile to write skills now; current models benefit from them.

Do not hoard skills as assets; they are consumables with an expiration date.

Writing skills is not a moat; models will eventually learn the same capabilities.

The lasting competitive edge lies in judgment (knowing when to use AI), expression (clearly articulating requirements), and system design (building end‑to‑end solutions rather than just feeding skills).

The author concludes that today’s skill‑filled GitHub hot list is already part of the next‑generation model’s training set, giving those models a head start and foreshadowing the eventual obsolescence of skill creators.