DR Context Pipeline v1 (retrieval + compression + lint)

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name: dr-context-pipeline-v1

description: "Deterministic memory/context pipeline for agents: route a user message, retrieve relevant memory snippets, compress into a cited Context Pack (sources are snippet IDs), lint, and fall back safely. Prerequisite: a file-based memory layout with memory/always_on.md + topic files (works out-of-the-box with dr-memory-foundation). Use when building or standardizing agent memory, reducing prompt bloat, implementing retrieval+compression, creating a context pack, designing a memory pipeline, adding lint gates, or setting up golden regression tests for agent context."

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# DR Context Pipeline v1 (retrieval + compression + lint)

Use this skill to standardize how an agent loads memory into its prompt **for correctness**.

Prerequisites

Operating procedure (default)

1) Load the always-on policy + topic catalog (your `memory/always_on.md`).

2) Route the message deterministically (task type + caps) using `references/router.yml`.

3) Retrieve top relevant snippets from your memory store; emit a **Retrieval Bundle JSON** (see schema).

4) Compress Retrieval Bundle → **Context Pack JSON** using `references/compressor_prompt.txt`.

- **IMPORTANT:** Context Pack `sources` MUST be **snippet IDs only** (`S1`, `S2`, …).

5) Lint the Context Pack. If lint fails, **skip compression** and fall back to raw retrieved snippets.

6) Call the main reasoning model with: always-on policy header + Context Pack (+ raw snippets for high-stakes tasks) + user message.

What to read / use

Notes