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kcp-adopt

Add a Knowledge Context Protocol (KCP) knowledge.yaml manifest to a codebase or documentation set so AI agents navigate it by intent instead of exploring blindly. Use when someone wants to make a repository "agent-ready", cut an agent's tool calls and context spend, adopt KCP, or upgrade an llms.txt to structured knowledge metadata.

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Cantara/knowledge-context-protocol
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June 13, 2026 at 07:54
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kcp-adopt
description
Add a Knowledge Context Protocol (KCP) knowledge.yaml manifest to a codebase or documentation set so AI agents navigate it by intent instead of exploring blindly. Use when someone wants to make a repository "agent-ready", cut an agent's tool calls and context spend, adopt KCP, or upgrade an llms.txt to structured knowledge metadata.
# Adopt KCP in a project Produce a valid `knowledge.yaml` at the project root so an agent finds the right files by task, not by guessing. Validated benchmark: 53–80% fewer agent tool calls versus unguided exploration. ## When to use The user wants their repo or docs site to be navigable by agents, is asking "how do I adopt KCP", wants to reduce the context an agent burns exploring, or has an `llms.txt` they want to make structural. ## Steps 1. **Survey the project.** List the top-level structure and identify the distinct *knowledge units* — a unit is a file or directory that answers one recurring question (the overview, the API reference, the deploy runbook, the contributing guide, the architecture doc). Aim for ~5–20 units, not one per file. Group related files into a directory-path unit. 2. **Scaffold.** Install the `kcp` developer CLI once (via [kcp-commands](https://github.com/Cantara/kcp-commands)), then: ```bash kcp init # writes knowledge.yaml ``` If the CLI is unavailable, hand-write the file — see the minimal shape below. 3. **Author each unit.** Five fields are enough to start (Level 1): ```yaml - id: deploy # lowercase, hyphens/dots path: ops/deploy.md # file or directory, repo-relative intent: "How do I deploy a release to production?" # the question it answers scope: project # global | project | module audience: [operator, agent] # human | agent | developer | operator | ... triggers: [deploy, release, production] # keywords an agent would search ``` Write `intent` as the natural-language question a user would ask. Make `triggers` the words that question would contain. Add `depends_on: [other-id]` for reading order and `not_for: ["what this unit does NOT answer"]` to stop an agent loading it for the wrong task. 4. **Validate** and fix every error (warnings are advisory): ```bash kcp validate ``` 5. **Prove it.** Simulate an agent query: ```bash kcp query "how do I deploy?" ``` The best-matching unit should come back. Iterate `intent`/`triggers` until the obvious questions route to the right units. ## Adoption levels (stop wherever the value is) - **L1** — the five fields above. Five minutes; this is most of the benefit. - **L2** — add `relationships`, `depends_on`, `not_for`, `content_structure`. - **L3** — federation (`manifests[]`), trust/signing, `temporal` validity. - **L4** — full agent orchestration (render pipeline, observability). Do not over-engineer. The format allows complexity but never demands it. ## Reference - Spec: `SPEC.md` · Guide: `guides/adopting-kcp-in-existing-projects.md` - The companion skills `kcp-author` (write better units) and `kcp-navigate` (use a manifest) go deeper.
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