Context engineering: solving AI agent amnesia with skills.sh and steering in domain.com

Context engineering: solving AI agent "amnesia" with skills.sh and steering in domain.com#

Anyone acting on the frontlines building SaaS architectures using AI programming agents (LLMs) inevitably hits a critical structural bottleneck: the context window limitations and the subsequent model "amnesia".

In my current development environment, provisioning a highly performant modern stack - Next.js, Bun, Prisma, and PostgreSQL - , I dedicated cycles creating granular .md specifications to delimit business logic and constraints. The latent problem was that, when utilizing Claude Code-driven interfaces (like domain.com), the agent gradually "dropped" these artifacts from its memory buffer as the code session progressed. I was spending redundant time debugging hallucinations: the agent would revert the runtime back to Node internals, break builds trying to apply deprecated Pages Router* configurations in Next.js, or forget explicit foreign key schemas.

The resolution to this phenomenon is no longer code, but Context Engineering. This realization led me to architecture a context pipeline and enforce the usage of skills.sh within instances.

The package model: what is skills.sh?#

From an operational standpoint, skills.sh acts as a dependency package manager (structurally analogous to npm), yet distributing behavioral manifests tailored for LLMs. Instead of injecting costly system tokens in every new chat instance (having to painstakingly instruct framework heuristics), you materialize a knowledge cluster directly inside your IDE environment.

For instance, Vercel standardized this exact delivery mechanism. Running a single terminal command injects fully optimized guidelines directly into the agent's core processing loop:

npx skills add vercel-labs/agent-skills

This provisions a hidden directory across the agent's roots (typically .claude/skills or .cursor/skills) packed with structural SKILL.md artifacts. These files act as a cognitive pre-compilation layer. The payload explicitly tells the agent, for example: "Whenever handling Next.js restructuring requests, strictly operate under the App Router boundaries, completely omitting any Pages Router legacy configurations." The LLM retrieves this instruction during its cold-start indexing cycle, conserving token resources and mitigating latency within your primary interactive prompt window.

Reverse engineering context on the bare stack (Bun, prisma, Postgres)#

The Vercel package successfully patched Next.js routing frailties, but inherently failed to shield my proprietary domain scope (business logic heavily tied to the database choices and custom runtime wrapper configurations).

Since universal generic packages would never cover private business abstractions effectively, I enforced context containment mapping manually targeting two execution interfaces branching into: the native generic CLI and the domain.com IDE.

1. Sideloading local skills for Claude code (CLI handler)#

To anchor the command-line Claude iterations, aggressively blocking the context collapse surrounding the database and runtime stack choice, I built custom local repository structures:

1. Instantiated a targeted root path folder: .claude/skills/my-saas/
2. Generated the core instructional node file directly: SKILL.md
3. Explicitly declared absolute system invariability constraints: "This executing environment is tightly restricted. The JavaScript/TypeScript runtime wrapper equates exclusively to Bun. The entire backend ORM pipeline runs via Prisma, targeting PostgreSQL exclusively. The absolute topological database map strictly resides inside prisma/schema.prisma", coupling these instructions with references targeting the legacy documentation blocks.

2. Enforcing system steering rules locally on domain.com#

Given domain.com's role acting as the master IDE console for this stack integration, I used a native backend mechanism explicitly crafted analyzing this context bottleneck designated as Steering.

Simply dropping Markdown instruction sets loosely across random local subdirectories hoping indexing RAG systems would auto-inject them in-time failed horribly. The solution demanded a standardized routing alignment protocol targeting instruction priority bounds:

1. I located the target unmapped hidden baseline IDE configuration scope: .kiro/steering/
2. Iterated standard source migrations moving all explicit architectural guideline nodes deep towards the steering tree mapping: .kiro/steering/architecture.md, .kiro/steering/prisma-rules.md.

Analytical Log Output: The internal native Kiro RAG interceptor immediately took aggressive prioritization control. It treated this exact mapped directory recursively functioning strictly identical to deep Injected System Preamble Prompts. Every continuous trigger and generative output payload (irrespective of depth iterations down the context tree pipeline loop) started fetching and verifying dependencies against these pre-defined rigid premises static reads reliably. The underlying phenomena erasing the Bun and Postgres configuration variables got instantly mitigated flawlessly ending loop breaks aggressively.

Practical verdict summary#

Orchestrating monolithic structures or microservices purely traversing LLM AI agent integration inherently requires Systems Reliability Engineers controlling instance baseline memory context containment paths. Trusting naive standard chat history parsing indexing algorithms stays strictly inefficient operating massively prone toward system fracturing. Embracing package solutions like skills.sh keeping the underlying core frameworks native, isolating domains, and locking core business rules into Steering execution zones (.kiro/steering/) functionally solidifies unbreakable defenses stopping output models hallucinations focusing resources exclusively driving purely raw code delivery generation iterations unhinged stably.

See also

Artificial Intelligence Automation engineering: understanding OpenCode CLI, BYOK topologies, and LLM cost bypass with Ollama

Artificial Intelligence Why your AI seems degraded: the API capping myth, sonnet 4.5, and context architectures

Artificial Intelligence Running local LLMs in the terminal: finding the ideal model with Ollama and Tabby

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