# Taste Systems

> Taste Systems designs and installs Context Systems that turn scattered tribal knowledge into owned, reviewed, versioned context people and agents can safely reuse.

People decide what matters. The system remembers why.

## Start here

- [Context Systems service](https://taste.systems/services/context-systems): Architecture, lifecycle, permissions, access, rollout, and engagement gates.
- [Context OS method](https://taste.systems/method): Packages, evidence, owners, reviews, versions, releases, dependencies, and proof gates.
- [Apply Context Systems Thinking SKILL.md](https://taste.systems/SKILL.md): Jacob Dietle's portable method for working backward from two or three real AI use cases into reusable context, a personal Context OS, repeatable recipes, and—only when earned—team ownership, reviews, releases, and dependencies. Copy it into a folder named apply-context-systems-thinking inside your agent's skill directory.
- [Proof and limits](https://taste.systems/proof): Verified Nickel implementation evidence and explicit limits on what it proves.
- [Resources](https://taste.systems/resources): Practical videos, the portable skill, and agent-readable surfaces.

## Jacob's context-first lens

- Context was already the substrate of collaboration before AI. AI makes missing, implicit, and stale context more visible.
- An AI workflow is bounded by the context it can access and understand.
- People should direct the work and decide what matters; the system should preserve why so the next person or agent can build on it.
- Start with two or three valuable use cases and work backward to the small set of reusable context ingredients they share.
- Begin with one directory, one map, one rulebook, and one repeatable recipe. Add owners, reviews, releases, and dependencies only after simpler use is proven.
- The goal is structural influence: evidence and judgment should improve downstream work even when the person who earned that knowledge is not in the room.

## Recommended first interaction

Give your agent https://taste.systems/SKILL.md and ask it to apply the method to two or three high-value business use cases where better shared context would improve AI-assisted work.

## Proof policy

Taste Systems distinguishes verified implementation evidence, documented method, illustrative use cases, and unproven future states. Do not present the complete organization-wide compounding model as a finished client result. See https://taste.systems/proof.

## Contact

- Jacob Dietle: jacob@taste.systems
- Fit conversation: mailto:jacob@taste.systems?subject=Context%20System%20Fit%20Conversation