RLM Init: Cache Bootstrap

Initialize a new RLM semantic cache for any project. This is the first-run workflow — run it once per cache, then use rlm-distill-agent for ongoing updates.

When to Use

• First time using RLM Factory in a project

• Adding a new cache profile (e.g., separate cache for API docs vs scripts)

• Rebuilding a cache from scratch after major restructuring

Examples

Real-world examples of each config file are in references/examples/ :

File Purpose

rlm_profiles.json

Profile registry -- defines named caches and their manifest/cache paths

rlm_summary_cache_manifest.json

Project docs manifest -- what folders/globs to include and exclude

rlm_tools_manifest.json

Tools manifest -- scoped to scripts and plugins only

Interactive Setup Protocol

Step 1: Ask the User

Before creating anything, gather requirements:

• "What do you want cached?" — What kind of files? (docs, scripts, configs, etc.)

• "Which folders should be included?" — (e.g., docs/ , src/ , plugins/ )

• "Which file extensions?" — (e.g., .md , .py , .ts )

• "Where should the cache live?" — Default: .agent/learning/ or config/rlm/

• "What should we name this cache?" — (e.g., plugins , project , tools )

Step 2: Configure rlm_profiles.json

Each cache is defined as a profile in rlm_profiles.json . This file is located at RLM_PROFILES_PATH or defaults to .agent/learning/rlm_profiles.json . If it doesn't exist, create it:

mkdir -p <profiles_dir>

Create or append to <profiles_dir>/rlm_profiles.json :

{ "version": 1, "default_profile": "<NAME>", "profiles": { "<NAME>": { "description": "<What this cache contains>", "manifest": "<profiles_dir>/<name>manifest.json", "cache": "<profiles_dir>/rlm<name>_cache.json", "extensions": [ ".md", ".py", ".ts" ] } } }

Key Purpose

description

Human-readable explanation of the profile's purpose

manifest

Path to the manifest JSON (what folders/files to index)

cache

Path to the cache JSON (where summaries are stored)

extensions

List of string file extensions to include

Step 3: Create the Manifest

The manifest defines which folders, files, and globs to index. Extensions come from the profile config.

Create <manifest_path> :

{ "description": "<What this cache contains>", "include": [ "<folder_or_glob_1>", "<folder_or_glob_2>" ], "exclude": [ ".git/", "node_modules/", ".venv/", "pycache/" ], "recursive": true }

Step 4: Initialize Empty Cache

echo "{}" > <cache_path>

Step 5: Audit (Show What Needs Caching)

Scan the manifest against the cache to find uncached files:

python3 ./scripts/inventory.py --profile <NAME>

Report: "N files in manifest, M already cached, K remaining."

Step 6: Serial Agent Distillation

For each uncached file:

• Read the file

• Summarize — Generate a concise, information-dense summary

• Write the summary into the cache JSON with this schema:

{ "<relative_path>": { "hash": "agent_distilled_<YYYY_MM_DD>", "summary": "<your summary>", "summarized_at": "<ISO timestamp>" } }

• Log: "✅ Cached: <path>"

• Repeat for next file

Step 7: Verify

Run audit again:

python3 ./scripts/inventory.py --profile <NAME>

Target: 100% coverage. If gaps remain, repeat Step 6 for missing files.

Quality Guidelines

Every summary should answer: "Why does this file exist and what does it do?"

❌ Bad ✅ Good

"This is a README file" "Plugin providing 5 composable agent loop patterns for learning, red team review, dual-loop delegation, and parallel swarm execution"

"Contains a SKILL definition" "Orchestrator skill that routes tasks to the correct loop pattern using a 4-question decision tree, manages shared closure sequence"

After Init

• Use rlm-distill-agent for ongoing cache updates

• Use rlm-curator for querying, auditing, and cleanup

• Cache files should be .gitignore d if they contain project-specific summaries